Admission Order Sets for DVT Prophylaxis

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Medical admission order sets to improve deep vein thrombosis prophylaxis rates and other outcomes

The use of clinical decision support, despite great promise to improve health care, remains preliminary.1 The broad scope of quality and safety challenges facing clinicians2, 3 requires this situation to change. There is an urgent need to develop decision support tools and strategies that are effective, address many quality issues simultaneously, and are easy to implement in both academic and community settings.

One decision support tool that could help to meet this challenge is the order set. An order set is a group of orders with a common functional purpose that is used directly by a physician to create orders for a specific patient. Order sets can be used with either paper‐based or computerized provider order entry (CPOE) systems. Several studies have investigated the delivery of focused evidence‐based treatments to patients admitted using disease‐specific order sets compared with either historical or concurrent controls and have demonstrated increased use of therapies such as aspirin for acute myocardial infarction admissions,4 systemic corticosteroids, metered‐dose inhalers and pulse oximetry for pediatric asthma admissions,5 and venous thromboembolism prophylaxis for adult emergency department admissions.6 However, the ability of order sets to improve multiple quality measures in a diverse patient population has not been evaluated previously.

This study examined the effect of paper‐based order sets on the quality of admission orders for general medical patients in a community hospital. The primary hypothesis was that order set use would increase the proportion of general medical patients ordered deep venous thrombosis (DVT) prophylaxis. We chose this primary endpoint because DVT prophylaxis continues to be significantly underused in hospitalized patients.7, 8 Secondary hypotheses were that order sets would improve other admission order quality of care measures. We studied paper‐based order sets because the study hospital, along with the vast majority of North American hospitals, uses paper for order entry.9

PATIENTS AND METHODS

Study Setting

The study took place in a 750‐bed community hospital in Mississauga, Ontario, Canada. The study included only general medical patients and excluded cardiology, neurology, and intensive care unit patients. Approximately 30 different internists admitted patients during the study period from April 1, 2003 to March 31, 2005. The internists were not aware that this study was being conducted. Order sets were implemented as an option for writing admission orders in December 2003. Prior to the implementation of order sets, physicians wrote all admission orders using traditional free‐text handwritten orders on blank paper order sheets. Essentially all general medical patients are admitted through the emergency room. The hospital's Research Ethics Board approved this study.

Order Set Development

Local specialists developed order set content (evidence‐based where possible) using informal consensus methods, without explicitly grading evidence. This process created a general admission order set and six diagnosis‐specific order sets (community acquired pneumonia, chronic obstructive pulmonary disease [COPD], febrile neutropenia, soft tissue infection, upper gastrointestinal [GI] bleeding, and urinary tract infection [UTI]). All order sets contained the same orders pertaining to the primary and secondary outcomes, except for the GI bleed admission order set, which did not contain a DVT prophylaxis section.

Order sets were paper‐based and consisted of a menu of orders typically required for a medical admission. These included admitting service, admitting physician, allergies, resuscitation status, diet, activity level, frequency of vital sign measurement, laboratory investigations, diagnostic imaging, intravenous fluid therapy, and medications. The orders were either optional (requiring the physician to check a box to be performed) or default (enacted unless specifically crossed out by the physician). Both order types could consist of a single order (for example, heparin for DVT prophylaxis) or several orders simultaneously (for example, measurement of serum sodium, potassium, and creatinine). All order sets included space for additional free‐text handwritten orders to meet individual patient needs.

The DVT prophylaxis section contained optional orders for 5,000 units of heparin subcutaneously (sc) twice daily (BID) and compression stockings. The ordering physician could select 1, both, or neither of these options. Initiating other forms of DVT prophylaxis or therapeutic anticoagulation required additional free‐text handwritten orders.

Informal clinician feedback led to improved order set content and formatting in August 2004. Orders pertaining to study outcomes were unchanged in this upgrade.

Implementation

In December 2003, we placed the order sets near the stacks of blank paper order sheets used by internists admitting patients in the Emergency Department. We notified physicians by e‐mail when order sets became available but provided no formal education about order sets, DVT prophylaxis, or other study outcomes. The use of order sets was voluntary. We developed a website to facilitate timely reordering of depleted order sets from the hospital's print shop and trained all emergency room clerks regarding website access and storage of the order sets in convenient locations for physicians. Although order set availability was not formally assessed, there were no reports by physicians or observations by study investigators of order sets being unavailable at any time.

Data Collection

To assess the effect of order sets on the ordering of DVT prophylaxis, we retrospectively and randomly selected patient admissions and reviewed these patients' charts from 3 time periods during the study period: OctoberNovember 2003 (period 1, immediately prior to availability of order sets; 113 charts available of 120 discharged patients randomly selected from a total of 1,169 discharges); AprilDecember 2004 (period 2, 412 months after order set availability; 291 charts available of 300 discharged patients randomly selected from a total of 4,620 discharges); and FebruaryMarch 2005 (period 3, 1415 months after order set availability; 283 charts available of 290 randomly selected discharges out of a total of 1,057 discharges). We conducted an additional chart audit just prior to final submission of the manuscript (108 charts available of 120 discharged patients randomly selected from a total of 1,060 discharges in OctoberNovember 2007) to determine the sustainability of the improvements. The same patient could be selected in different time periods. One author (C.O. or K.D.) reviewed each chart using a jointly developed data collection form.

We assessed the admission orders of each chart for the use of an order set and the ordering of DVT prophylaxis, defined as 5,000 units of heparin sc BID or compression stockings (no patient received sc heparin 3 times daily, heparin sc BID in doses greater than 5,000 units, prophylactic doses of low molecular‐weight heparin, or low dose warfarin). We recorded the ordering of therapeutic anticoagulation, defined as intravenous heparin, full‐dose low molecular‐weight heparin, or warfarin with a target international normalized ratio 2.

Independent from the chart review, we examined the overall administration of heparin doses for DVT prophylaxis to all medical inpatients using the hospital pharmacy database. We estimated the overall administration of heparin for DVT prophylaxis in medical inpatients (136 medical beds, 4 wards) on a monthly basis from April 1, 2003 (8 months prior to order set availability) to March 30, 2005 (15 months after order set availability). We calculated monthly utilization as the proportion of patient‐days for which DVT prophylaxis was administered, as follows: (number of doses of subcutaneous heparin dispensed by the hospital pharmacy to the 4 wards)/(2 [since there are 2 doses per patient‐day] number of patient‐days).

We collected additional data from the charts selected during period 2 (AprilDecember 2004) to evaluate the effect of order sets on the following secondary outcomes: (1) the documentation of admission diagnosis, allergies, and code status; (2) general care orders (electrocardiogram [ECG] and notification of physician for chest pain, allied health consultation, standard hospital potassium replacement protocol [already available in the hospital], standard hospital diabetic diet and standard hospital insulin sliding scale [for patients with diabetes], night time sedation diet or nil per os, activity level and vital sign frequency); (3) blood urea nitrogen (BUN), a laboratory test often inappropriately ordered according to local guidelines10; (4) order formatting (numbering, dating, timing, and signing of all order pages); and (5) organization of orders in the standardized arrangement used in the order sets. This standardized arrangement of content was as follows: attending physician, admitting diagnosis, requests for consultation, diet, activity, vital signs, oxygen, nasogastric tube, urinary catheter, investigations, intravenous fluids, and medications. Free‐text admission orders and order set orders that maintained this arrangement were recorded as standardized. We did not assess order appropriateness.

We recorded the characteristics of all medical patients admitted to the hospital in two 1‐year periods during the study (April 1, 2003 to March 30, 2004 and April 1, 2004 to March 30, 2005), including age, gender, length of stay, diagnosis (defined by case management group [CMG]), and resource intensity weight (RIW). CMG defines groups of patients who are similar in diagnosis or procedure and RIW is a measure of resources used during a patient's hospital stay.11 The definitions of CMG and RIW did not change during the study.

Statistical Analysis

Baseline characteristics were compared using Student t‐test for normally distributed continuous variables (patient age) and the Mann‐Whitney U test for skewed continuous variables (length of stay and RIW). Chi square or Fisher's exact tests were used to compare categorical variables. Relative risks (RR) and 95% confidence intervals (CI) were calculated and compared using a z‐test. A 2‐sided P value <0.05 was taken to be statistically significant. All calculations were carried out using SAS Version 8.2 (SAS Institute, Cary, NC).

RESULTS

As shown in Table 1, there were no clinically important differences in demographic or clinical characteristics of medical patients between the 2 years of the study. There were small but statistically significant increases in patient illness complexity (as reflected in median RIW) (P = 0.003) and length of stay (P = 0.0002).

Patient Characteristics in the Two 1‐Year Periods
Patient CharacteristicApril 1, 2003 to March 30, 2004 (n = 4,415)April 1, 2004 to March 30, 2005 (n = 4,287)
  • NOTE: Only the 5 most common case mix groups are shown.

  • Abbreviations: IQR, interquartile range; n, number of medical admissions; RIW, resource intensity weight (see Data Collection section for details); SD, standard deviation.

Age, mean SD67.2 17.767.6 17.5
Length of stay, median days (IQR)6 (3‐12)6 (3‐13)
RIW,11 median (IQR)0.96 (0.68‐1.73)1.03 (0.72‐1.88)
Females, number (% of total)2,276 (52)2,223 (52)
Case mix group,11 number of patients (% of total)  
Chronic obstructive pulmonary disease357 (8.1)385 (9.0)
Simple pneumonia and pleurisy322 (7.3)217 (5.1)
Esophageal, gastrointestinal, and miscellaneous digestive disease223 (5.1)239 (5.6)
Gastrointestinal hemorrhage185 (4.2)198 (4.6)
Respiratory neoplasm127 (2.9)144 (3.4)
Total1,214 (27.5)1,183 (27.7)

Clinicians used order sets in 32.3% of admissions during period 2 (AprilDecember 2004, 412 months after order set availability), increasing to 51.6% in period 3 (FebruaryMarch 2005, 1415 months after order set availability). The results of the chart audit assessing the impact of order set use on DVT prophylaxis are shown in Figure 1. Prior to order set introduction, 10.9% of patients received orders for DVT prophylaxis. Subsequently, ordering of DVT prophylaxis in patients admitted with order sets increased (period 2: 35.6%; P < 0.001; RR, 3.27; 95% CI, 1.806.12 and period 3: 44.0%; P < 0.001; RR, 4.04; 95% CI, 2.327.31). In contrast, DVT prophylaxis ordering in the nonorder set group was initially unchanged (period 2: 10.6%; P = 0.93; RR, 0.97; 95% CI, 0.491.95), although later it increased to a smaller extent (period 3: 20.6%; P = 0.049; RR, 1.90; 95% CI, 1.013.65). As a result of this differential increase, patients admitted with order sets were more likely to be ordered DVT prophylaxis in both study periods (period 2: 35.6% versus 10.6%; P < 0.0001; RR, 3.38; 95% CI, 2.035.62 and period 3: 44.0% versus 20.6%; P < 0.0001; RR, 2.13; 95% CI, 1.443.16). The use of therapeutic anticoagulation was similar in patients admitted with and without order sets and did not change between time periods.

Figure 1
Orders for DVT prophylaxis in patients admitted with and without order sets for 4 time periods: October‐November 2003 (n = 113), April‐December 2004 (n = 291), February‐March 2005 (n = 283), and November‐December 2007 (n = 108).

The hospital‐wide monthly utilization of heparin for DVT prophylaxis in medical inpatients increased from an average of 12.8% (range, 9.7%16.1%) of patient‐days before order set implementation (AprilNovember 2003) to 18.5% (range, 16.4%20.0%) of patient‐days in the 8 months after order sets were first implemented (DecemberJuly 2004, P < 0.0001 compared to the preorder set time period). After August 2004, when upgraded order sets were introduced, DVT prophylaxis utilization increased further in the last 7 months of the study to 25.8% (range, 22.4%32.2%; P < 0.0001 compared to preorder set time period; Figure 2).

Figure 2
Monthly DVT utilization was calculated as the proportion of patient‐days for which DVT prophylaxis was administered: (number of doses of subcutaneous heparin dispensed by the hospital pharmacy to the 4 wards)/(2 [since there are 2 doses per patient‐day] × number of patient‐days). White bars, prior to order sets; gray hashed bars, after order set introduction; black solid bars, after introduction of revised order sets based on clinician feedback with improved content and formatting.

Table 2 shows the impact of order sets on secondary outcomes. Admissions completed with order sets had statistically significant increases in general care orders (ECG and notification of physician for chest pain, allied health consultations and standard hospital diabetic diet, insulin scale, and potassium replacement protocol orders), documentation of allergies and code status, numbering of pages, and use of a standardized arrangement for orders. Ordering of BUN decreased significantly.

Secondary Outcomes Based on Admission Order Set Use During Period 2 (April to December 2004)
OutcomeOptional or DefaultOrder Set [n = 94 (%)]No Order Set [n = 197 (%)]P Value
  • NOTE: Data refer to the number (proportion) of admissions with the specific order. Optional or default refers to the order's status in the order set (see Order Set Development section). As discussed in Patients and Methods, this additional information was only collected during this time period.

  • A printing error left this order off of some order sets.

  • This order was default for the chronic obstructive pulmonary disease order set and optional for all others.

Documentation    
Admitting diagnosisOptional91 (96.8)187 (94.9)0.47
AllergiesOptional51 (54.3)19 (9.6)<0.0001
Resuscitation statusOptional54 (57.4)20 (10.2)<0.0001
General care orders    
ECG and call MD for chest painDefault*80 (85.1)0 (0.0)<0.0001
Allied health consult 59 (62.8)25 (12.7)<0.0001
DietOptional90 (95.7)188 (95.4)0.90
ActivityOptional80 (85.1)150 (76.1)0.08
Vitals signs and frequencyOptional91 (96.8)178 (90.4)0.052
Standard hospital diabetic dietOptional16 (17.0)10 (5.1)0.0008
Standard hospital insulin sliding scaleOptional18 (19.1)15 (7.6)0.004
Standard hospital potassium protocolOptional60 (63.8)1 (0.51)<0.0001
Nighttime sedation    
Zopiclone as neededOptional43 (45.7)2 (1.0)< 0.0001
Lorazepam as neededOptional12 (12.8)15 (7.6)0.16
Laboratory test order    
Blood urea nitrogenOptional37 (39.4)117 (59.0)0.0014
Order formatting    
Numbering of pagesDefault94 (100)4 (2.0)<0.0001
Dating of ordersOptional79 (84.0)185 (93.9)0.0067
Timing of ordersOptional14 (14.9)29 (14.7)0.97
Signing of ordersOptional93 (98.9)196 (99.5)0.54
Standard arrangement of orders 81 (86.2)66 (33.5)<0.0001

Order sets were not associated with changes in diet, activity, or vital sign orders, documentation of admission diagnosis, or the signing and timing of orders. Apart from order timing, these orders were present in >75% of admissions completed without order sets. The only negative effect of order sets was a reduction in the dating of orders (84.0% of order set admissions versus 93.9% of nonorder set admissions, P = 0.007). Finally, order sets had both an intended and unintended effect on nighttime sedation orders. Relative frequency of ordering of zopiclone compared to lorazepam increased (43/55 in the order set group vs. 2/17 in the no order set group [P < 0.0001], the intended effect), and increased overall frequency of ordering of nighttime sedation (55/94 vs. 17/197 [P < 0.0001], an unintended effect).

The additional chart audit in OctoberNovember 2007, just prior to final submission of the manuscript, determined that clinician use of order sets had increased to 92.6% of admitted medical patients, and that ordering of DVT prophylaxis in patients admitted with order sets had been sustained at 43.2% (P = 0.90 compared to period 3) (Figure 1).

DISCUSSION

We found that paper‐based order sets were associated with markedly increased use of DVT prophylaxis and made physician ordering more consistent with hospital consensus guidelines in multiple other areas, including laboratory test utilization and general care, while also increasing completeness of documentation. Given the difficulties and limited resources frequently associated with guideline development, dissemination, and implementation,12 it is worth noting that our improvements were achieved in a community hospital with voluntary physician adoption and no dedicated project funding, care process redesign, or healthcare worker education. The broad impact of order sets combined with minimal organizational resources required for implementation in this study suggests that this clinical decision support tool may have wide applicability.

The study hospital used paper‐based orders rather than CPOE, similar to 90% of U.S. hospitals at the time of the study.9 Order sets can be deployed in either paper‐based or computerized ordering systems. By providing a mechanism for entering large blocks of orders in an efficient manner, paper‐based order sets may be a necessary first step to facilitate the paper to CPOE transition, making them well suited to the current care delivery environment. Successful use of paper‐based order sets may help accelerate adoption of CPOE, which appears to be many years away from full implementation in the majority of U.S. hospitals.13

The most clinically important outcome in our study was a more than 4‐fold increase in ordering of DVT prophylaxis (last study period compared with baseline) in medical patients admitted with order sets, compared to a smaller increase in patients admitted without order sets. Our result is particularly significant as this study was performed in a community hospital, a setting with a lower adherence to DVT prophylaxis guidelines compared to academic centers.8, 14 The increase in DVT prophylaxis in patients admitted without order sets could be the result of a secular trend or a passive educational effect of order sets on physicians who only used order sets intermittently. The study was not publicized and thus was unlikely in itself to contribute to the increased performance.

We did not assess clinical outcomes of DVT or pulmonary embolism, but the clinical efficacy of improving adherence to DVT prophylaxis has been previously established.15 We also did not assess the appropriateness of DVT prophylaxis (or any other order). However, a recent multicenter Canadian observational study, using the American College of Chest Physician's Consensus Guidelines on Antithrombotic Therapy16 as a reference standard, found that 90% of medical patients admitted to hospital meeting study criteria had indications for thromboprophylaxis, but only 16% of eligible patients actually received it.8 In addition, multivariable regression analysis demonstrated even lower utilization in community hospitals compared to academic hospitals. These data suggest that the study hospital is typical of Canadian hospitals, and that the low overall utilization of DVT prophylaxis (13% of hospital patient‐days) prior to the availability of order sets in the study hospital is a significant gap between optimal and actual practice.

In addition, order sets had an impact on many secondary outcomes, such as standardization and completeness of orders (for example documentation of allergies and resuscitation status). While these effects appear to be beneficial in terms of quality of care and patient safety, the relationship of our secondary outcomes to patient‐important outcomes has not been established.

Furthermore, our before‐after design does not exclude the possibility of unknown confounding effects as explanations of improved performance in the order set group. For example, the change could have been driven by a small number of admitting physicians, since it is likely that order sets were adopted more readily by some physicians than others, and this group could have been responsible for a greater proportion of the admissions at different times. Unfortunately, we did not record the identity of the admitting physician. However, data from OctoberNovember 2007 show that >90% of medical patients were admitted using order sets, suggesting that voluntary clinician adoption of order sets has become nearly universal. Nevertheless, there still appear to be a few physicians who rarely or never used orders sets. Motivating these physicians to prescribe appropriate DVT prophylaxis remains a challenge.

Although this study was conducted in 1 center, other hospitals have similarly low rates of thromboprophylaxis,8 and our order set implementation strategy consumed few resources, improving the generalizability of our results. While most changes were beneficial, order set use was associated with decreased dating of orders and with an unintended effect or overall increase ordering of nightime sedation. Although the reasons for this are unclear, it highlights the importance of systematically evaluating the impact of order sets to identify unintended consequences and areas in which the order set may need to be redesigned to address these issues.

The study of order sets is preliminary despite their role as a key enabler for CPOE17 and their suggested usefulness to reduce medical error.18 For example, order sets were not considered in recent analyses of factors predicting success of computerized decision support systems19, 20 and have not been reviewed by the Cochrane Effective Practice and Organisation of Care Group.21 As discussed in the introduction, several studies have demonstrated that disease‐specific order sets can increase the use of evidence‐based treatments.46 Our study extends this work by demonstrating that admission order sets can improve performance hospital‐wide for a broad range of outcomes simultaneously, including DVT prophylaxis. Although most studies have demonstrated increased utilization of evidence‐based therapies, at least 1 study found no increased use of aspirin, heparin, or beta‐blockers in acute coronary syndrome admissions with the introduction of order sets.22 This suggests that the way order sets are structured or introduced is important to ensure that they achieve the desired changes in practice. Finally, our study suggests that improved outcomes using order sets can still be achieved with minimal organizational resources.

Order sets may potentially complement other decision support tools such as alerts and reminders. Alerts are an effective decision support tool12, 23, 24 but risk disrupting clinician workflow. Moreover, excessive alerts can lead to alert fatigue, resulting in many alerts being ignored.25 This phenomenon reduces alert effectiveness and limits the number of issues that alerts can address simultaneously. In contrast, order sets are broad in scope due to integration with clinical workflow, but lack the ability of alerts to apply rules to a specific patient's data. A potentially effective 2‐staged decision support strategy would use order sets as the primary admission decision support tool and selective alerts for remaining issues. This approach may increase the overall scope, physician adoption, and effectiveness of clinical decision support, and should be evaluated.

Our postintervention rate of DVT prophylaxis, while substantially improved from baseline, is still below ideal practice. Order sets were simply made available to clinicians admitting medical patients, who had the option to select DVT prophylaxis. Given limited resources, we did not develop and implement education programs regarding the appropriate use of DVT prophylaxis or make available any DVT risk assessment evaluations (available in Ref.26). Our study methodology thus provides a realistic assessment of improvements attainable in other hospitals with similarly limited resources. Additional increases in DVT prophylaxis rates would likely require a more comprehensive and resource‐intensive multifaceted quality improvement initiative. Detailed guidelines and supporting references for implementing such an initiative are available from the Society of Hospital Medicine.26 As described in their Venous Thromboembolism (VTE) Resource Room,26 such an initiative should include a standardized DVT risk assessment to guide the need for DVT prophylaxis integrated into admission order sets; prompts to order DVT prophylaxis when completing admission orders; and a system to audit adverse events and variations from best practice and return this information to clinicians.26

CONCLUSIONS

This is the largest and most comprehensive evaluation of the effectiveness of order sets as a clinical decision support tool. We found that order sets improved the quality of multiple patient orders and improved hospital‐wide DVT prophylaxis rates. These improvements were achieved in a community hospital with voluntary physician adoption and no dedicated project funding, care process redesign, or healthcare worker education. Although used in a paper‐based format in this study, order sets can also be employed in a computerized ordering environment. By providing a mechanism for entering large blocks of orders in an efficient manner, paper‐based order sets may be a necessary first step to facilitate the paper‐to‐CPOE transition. These attributes make order sets an attractive quality improvement tool in community and academic settings. More research is needed on the optimal design and use of this promising decision support tool.

References
  1. Berg M.Health Information Management: Integrating Information Technology in Health Care Work.London:Routledge;2004.
  2. Kohn KT,Corrigan JM,Donaldson MS.To Err Is Human: Building a Safer Health System.Washington, DC:National Academy Press;2006.
  3. National Healthcare Quality Report.2004.Rockville, MD:Agency for Healthcare Research and Quality;2006.
  4. Ozdas A,Speroff T,Waitman LR,Ozbolt J,Butler J,Miller RA.Integrating “best of care” protocols into clinicians' workflow via care provider order entry: impact on quality‐of‐care indicators for acute myocardial infarction.J Am Med Inform Assoc.2006;13:188196.
  5. Chisholm DJ,McAlearney AS,Veneris S,Fisher D,Holtzlander M,McCoy KS.The role of computerized order sets in pediatric inpatient asthma treatment.Ped Allergy Immunol.2006;17:199206.
  6. Levine RL,Hergenroeder GW,Miller CC,Davies A.Venous thromboembolism prophylaxis in emergency department admissions.J Hosp Med.2007;2:7985.
  7. Goldhaber SZ.DVT prevention: what is happening in the “real world”?Semin Thromb Hemost.2003;29(Suppl 1):2331.
  8. Kahn SR,Panju A,Geerts W, et al.Multicenter evaluation of the use of venous thromboembolism prophylaxis in acutely ill medical patients in Canada.Thromb Res.2007;119:145155.
  9. Ash JS,Gorman PN,Seshadri V,Hersh WR.Computerized physician order entry in US hospitals: results of a 2002 survey.J Am Med Inform Assoc.2004;11:9599.
  10. Ontario Association of Medical Laboratories. Guidelines for the Use of Serum Tests to Detect Renal Dysfunction. Available at:http://www.oaml.com/PDF/CLP007.pdf. Accessed 12 May2008.
  11. Pink GH,Bolley HB.Physicians in health care management: 3. Case mix groups and resource intensity weights: an overview for physicians.CMAJ.1994;150:889894.
  12. Grimshaw JM,Thomas RE,MacLennan G, et al.Effectiveness and efficiency of guideline dissemination and implementation strategies.Health Technol Assess.2004;8(6):iiiiv,1–72.
  13. Ford EW,McAlearney AS,Phillips MT,Menachemi N,Rudolph B.Predicting computerized physician order entry system adoption in US hospitals: can the federal mandate be met?Int J Med Inform.2008;77(8):539545.
  14. Anderson FA,Wheeler HB,Goldberg RJ,Hosmer DW,Forcier A,Patwardhan NA.Physician practices in the prevention of venous thromboembolism.Ann Intern Med.1991;115:591595.
  15. Kucher N,Koo S,Quiroz R, et al.Electronic alerts to prevent venous thromboembolism among hospitalized patients.N Engl J Med.2005;352:969977.
  16. Geerts WH,Heit JA,Clagett GP, et al.Prevention of venous thromboembolism.Chest.2001;119(Suppl 1):132S175S.
  17. Ash JS,Stavri PZ,Kuperman GJ.A consensus statement on considerations for a successful CPOE implementation.J Am Med Inform Assoc.2003;10:229234.
  18. Volpp KGM,Grande D.Residents' suggestions for reducing errors in teaching hospitals.N Engl J Med.2003;348:851855.
  19. Kawamoto K,Houlihan CA,Balas EA,Lobach DF.Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.BMJ.2005;330:765.
  20. Garg AX,Adhikari NKJ,McDonald H, et al.Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.JAMA.2005;293:12231238.
  21. Cochrane Reviews by the Effective Practice and Organisation of Care Group. Available at: http://www.cochrane.org/reviews/en/topics/61_reviews.html. Accessed 12 May2008.
  22. Asaro PV,Sheldahl AL,Char DM.Embedded guideline information without patient specificity in a commercial emergency department computerized order‐entry system.Acad Emer Med.2006;13:452458.
  23. Dexter PR,Perkins S,Overhage JM,Maharry K,Kohler RB,McDonald CJ.A computerized reminder system to increase the use of preventive care for hospitalized patients.N Engl J Med.2001;345:965970.
  24. Durieux P,Nizard R,Ravaud P,Mounier N,Lepage E.A clinical decision support system for prevention of venous thromboembolism: effect on physician behavior.JAMA.2000;283:28162821.
  25. Handler JA,Feied CF,Coonan K, et al.Computerized physician order entry and online decision support.Acad Emerg Med.2004;11:11351141.
  26. Society of Hospital Medicine VTE Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_VTE/VTE_Home.cfm. Accessed 12 May2008.
Article PDF
Issue
Journal of Hospital Medicine - 4(2)
Page Number
81-89
Legacy Keywords
decision support, deep vein thrombosis prophylaxis, order sets
Sections
Article PDF
Article PDF

The use of clinical decision support, despite great promise to improve health care, remains preliminary.1 The broad scope of quality and safety challenges facing clinicians2, 3 requires this situation to change. There is an urgent need to develop decision support tools and strategies that are effective, address many quality issues simultaneously, and are easy to implement in both academic and community settings.

One decision support tool that could help to meet this challenge is the order set. An order set is a group of orders with a common functional purpose that is used directly by a physician to create orders for a specific patient. Order sets can be used with either paper‐based or computerized provider order entry (CPOE) systems. Several studies have investigated the delivery of focused evidence‐based treatments to patients admitted using disease‐specific order sets compared with either historical or concurrent controls and have demonstrated increased use of therapies such as aspirin for acute myocardial infarction admissions,4 systemic corticosteroids, metered‐dose inhalers and pulse oximetry for pediatric asthma admissions,5 and venous thromboembolism prophylaxis for adult emergency department admissions.6 However, the ability of order sets to improve multiple quality measures in a diverse patient population has not been evaluated previously.

This study examined the effect of paper‐based order sets on the quality of admission orders for general medical patients in a community hospital. The primary hypothesis was that order set use would increase the proportion of general medical patients ordered deep venous thrombosis (DVT) prophylaxis. We chose this primary endpoint because DVT prophylaxis continues to be significantly underused in hospitalized patients.7, 8 Secondary hypotheses were that order sets would improve other admission order quality of care measures. We studied paper‐based order sets because the study hospital, along with the vast majority of North American hospitals, uses paper for order entry.9

PATIENTS AND METHODS

Study Setting

The study took place in a 750‐bed community hospital in Mississauga, Ontario, Canada. The study included only general medical patients and excluded cardiology, neurology, and intensive care unit patients. Approximately 30 different internists admitted patients during the study period from April 1, 2003 to March 31, 2005. The internists were not aware that this study was being conducted. Order sets were implemented as an option for writing admission orders in December 2003. Prior to the implementation of order sets, physicians wrote all admission orders using traditional free‐text handwritten orders on blank paper order sheets. Essentially all general medical patients are admitted through the emergency room. The hospital's Research Ethics Board approved this study.

Order Set Development

Local specialists developed order set content (evidence‐based where possible) using informal consensus methods, without explicitly grading evidence. This process created a general admission order set and six diagnosis‐specific order sets (community acquired pneumonia, chronic obstructive pulmonary disease [COPD], febrile neutropenia, soft tissue infection, upper gastrointestinal [GI] bleeding, and urinary tract infection [UTI]). All order sets contained the same orders pertaining to the primary and secondary outcomes, except for the GI bleed admission order set, which did not contain a DVT prophylaxis section.

Order sets were paper‐based and consisted of a menu of orders typically required for a medical admission. These included admitting service, admitting physician, allergies, resuscitation status, diet, activity level, frequency of vital sign measurement, laboratory investigations, diagnostic imaging, intravenous fluid therapy, and medications. The orders were either optional (requiring the physician to check a box to be performed) or default (enacted unless specifically crossed out by the physician). Both order types could consist of a single order (for example, heparin for DVT prophylaxis) or several orders simultaneously (for example, measurement of serum sodium, potassium, and creatinine). All order sets included space for additional free‐text handwritten orders to meet individual patient needs.

The DVT prophylaxis section contained optional orders for 5,000 units of heparin subcutaneously (sc) twice daily (BID) and compression stockings. The ordering physician could select 1, both, or neither of these options. Initiating other forms of DVT prophylaxis or therapeutic anticoagulation required additional free‐text handwritten orders.

Informal clinician feedback led to improved order set content and formatting in August 2004. Orders pertaining to study outcomes were unchanged in this upgrade.

Implementation

In December 2003, we placed the order sets near the stacks of blank paper order sheets used by internists admitting patients in the Emergency Department. We notified physicians by e‐mail when order sets became available but provided no formal education about order sets, DVT prophylaxis, or other study outcomes. The use of order sets was voluntary. We developed a website to facilitate timely reordering of depleted order sets from the hospital's print shop and trained all emergency room clerks regarding website access and storage of the order sets in convenient locations for physicians. Although order set availability was not formally assessed, there were no reports by physicians or observations by study investigators of order sets being unavailable at any time.

Data Collection

To assess the effect of order sets on the ordering of DVT prophylaxis, we retrospectively and randomly selected patient admissions and reviewed these patients' charts from 3 time periods during the study period: OctoberNovember 2003 (period 1, immediately prior to availability of order sets; 113 charts available of 120 discharged patients randomly selected from a total of 1,169 discharges); AprilDecember 2004 (period 2, 412 months after order set availability; 291 charts available of 300 discharged patients randomly selected from a total of 4,620 discharges); and FebruaryMarch 2005 (period 3, 1415 months after order set availability; 283 charts available of 290 randomly selected discharges out of a total of 1,057 discharges). We conducted an additional chart audit just prior to final submission of the manuscript (108 charts available of 120 discharged patients randomly selected from a total of 1,060 discharges in OctoberNovember 2007) to determine the sustainability of the improvements. The same patient could be selected in different time periods. One author (C.O. or K.D.) reviewed each chart using a jointly developed data collection form.

We assessed the admission orders of each chart for the use of an order set and the ordering of DVT prophylaxis, defined as 5,000 units of heparin sc BID or compression stockings (no patient received sc heparin 3 times daily, heparin sc BID in doses greater than 5,000 units, prophylactic doses of low molecular‐weight heparin, or low dose warfarin). We recorded the ordering of therapeutic anticoagulation, defined as intravenous heparin, full‐dose low molecular‐weight heparin, or warfarin with a target international normalized ratio 2.

Independent from the chart review, we examined the overall administration of heparin doses for DVT prophylaxis to all medical inpatients using the hospital pharmacy database. We estimated the overall administration of heparin for DVT prophylaxis in medical inpatients (136 medical beds, 4 wards) on a monthly basis from April 1, 2003 (8 months prior to order set availability) to March 30, 2005 (15 months after order set availability). We calculated monthly utilization as the proportion of patient‐days for which DVT prophylaxis was administered, as follows: (number of doses of subcutaneous heparin dispensed by the hospital pharmacy to the 4 wards)/(2 [since there are 2 doses per patient‐day] number of patient‐days).

We collected additional data from the charts selected during period 2 (AprilDecember 2004) to evaluate the effect of order sets on the following secondary outcomes: (1) the documentation of admission diagnosis, allergies, and code status; (2) general care orders (electrocardiogram [ECG] and notification of physician for chest pain, allied health consultation, standard hospital potassium replacement protocol [already available in the hospital], standard hospital diabetic diet and standard hospital insulin sliding scale [for patients with diabetes], night time sedation diet or nil per os, activity level and vital sign frequency); (3) blood urea nitrogen (BUN), a laboratory test often inappropriately ordered according to local guidelines10; (4) order formatting (numbering, dating, timing, and signing of all order pages); and (5) organization of orders in the standardized arrangement used in the order sets. This standardized arrangement of content was as follows: attending physician, admitting diagnosis, requests for consultation, diet, activity, vital signs, oxygen, nasogastric tube, urinary catheter, investigations, intravenous fluids, and medications. Free‐text admission orders and order set orders that maintained this arrangement were recorded as standardized. We did not assess order appropriateness.

We recorded the characteristics of all medical patients admitted to the hospital in two 1‐year periods during the study (April 1, 2003 to March 30, 2004 and April 1, 2004 to March 30, 2005), including age, gender, length of stay, diagnosis (defined by case management group [CMG]), and resource intensity weight (RIW). CMG defines groups of patients who are similar in diagnosis or procedure and RIW is a measure of resources used during a patient's hospital stay.11 The definitions of CMG and RIW did not change during the study.

Statistical Analysis

Baseline characteristics were compared using Student t‐test for normally distributed continuous variables (patient age) and the Mann‐Whitney U test for skewed continuous variables (length of stay and RIW). Chi square or Fisher's exact tests were used to compare categorical variables. Relative risks (RR) and 95% confidence intervals (CI) were calculated and compared using a z‐test. A 2‐sided P value <0.05 was taken to be statistically significant. All calculations were carried out using SAS Version 8.2 (SAS Institute, Cary, NC).

RESULTS

As shown in Table 1, there were no clinically important differences in demographic or clinical characteristics of medical patients between the 2 years of the study. There were small but statistically significant increases in patient illness complexity (as reflected in median RIW) (P = 0.003) and length of stay (P = 0.0002).

Patient Characteristics in the Two 1‐Year Periods
Patient CharacteristicApril 1, 2003 to March 30, 2004 (n = 4,415)April 1, 2004 to March 30, 2005 (n = 4,287)
  • NOTE: Only the 5 most common case mix groups are shown.

  • Abbreviations: IQR, interquartile range; n, number of medical admissions; RIW, resource intensity weight (see Data Collection section for details); SD, standard deviation.

Age, mean SD67.2 17.767.6 17.5
Length of stay, median days (IQR)6 (3‐12)6 (3‐13)
RIW,11 median (IQR)0.96 (0.68‐1.73)1.03 (0.72‐1.88)
Females, number (% of total)2,276 (52)2,223 (52)
Case mix group,11 number of patients (% of total)  
Chronic obstructive pulmonary disease357 (8.1)385 (9.0)
Simple pneumonia and pleurisy322 (7.3)217 (5.1)
Esophageal, gastrointestinal, and miscellaneous digestive disease223 (5.1)239 (5.6)
Gastrointestinal hemorrhage185 (4.2)198 (4.6)
Respiratory neoplasm127 (2.9)144 (3.4)
Total1,214 (27.5)1,183 (27.7)

Clinicians used order sets in 32.3% of admissions during period 2 (AprilDecember 2004, 412 months after order set availability), increasing to 51.6% in period 3 (FebruaryMarch 2005, 1415 months after order set availability). The results of the chart audit assessing the impact of order set use on DVT prophylaxis are shown in Figure 1. Prior to order set introduction, 10.9% of patients received orders for DVT prophylaxis. Subsequently, ordering of DVT prophylaxis in patients admitted with order sets increased (period 2: 35.6%; P < 0.001; RR, 3.27; 95% CI, 1.806.12 and period 3: 44.0%; P < 0.001; RR, 4.04; 95% CI, 2.327.31). In contrast, DVT prophylaxis ordering in the nonorder set group was initially unchanged (period 2: 10.6%; P = 0.93; RR, 0.97; 95% CI, 0.491.95), although later it increased to a smaller extent (period 3: 20.6%; P = 0.049; RR, 1.90; 95% CI, 1.013.65). As a result of this differential increase, patients admitted with order sets were more likely to be ordered DVT prophylaxis in both study periods (period 2: 35.6% versus 10.6%; P < 0.0001; RR, 3.38; 95% CI, 2.035.62 and period 3: 44.0% versus 20.6%; P < 0.0001; RR, 2.13; 95% CI, 1.443.16). The use of therapeutic anticoagulation was similar in patients admitted with and without order sets and did not change between time periods.

Figure 1
Orders for DVT prophylaxis in patients admitted with and without order sets for 4 time periods: October‐November 2003 (n = 113), April‐December 2004 (n = 291), February‐March 2005 (n = 283), and November‐December 2007 (n = 108).

The hospital‐wide monthly utilization of heparin for DVT prophylaxis in medical inpatients increased from an average of 12.8% (range, 9.7%16.1%) of patient‐days before order set implementation (AprilNovember 2003) to 18.5% (range, 16.4%20.0%) of patient‐days in the 8 months after order sets were first implemented (DecemberJuly 2004, P < 0.0001 compared to the preorder set time period). After August 2004, when upgraded order sets were introduced, DVT prophylaxis utilization increased further in the last 7 months of the study to 25.8% (range, 22.4%32.2%; P < 0.0001 compared to preorder set time period; Figure 2).

Figure 2
Monthly DVT utilization was calculated as the proportion of patient‐days for which DVT prophylaxis was administered: (number of doses of subcutaneous heparin dispensed by the hospital pharmacy to the 4 wards)/(2 [since there are 2 doses per patient‐day] × number of patient‐days). White bars, prior to order sets; gray hashed bars, after order set introduction; black solid bars, after introduction of revised order sets based on clinician feedback with improved content and formatting.

Table 2 shows the impact of order sets on secondary outcomes. Admissions completed with order sets had statistically significant increases in general care orders (ECG and notification of physician for chest pain, allied health consultations and standard hospital diabetic diet, insulin scale, and potassium replacement protocol orders), documentation of allergies and code status, numbering of pages, and use of a standardized arrangement for orders. Ordering of BUN decreased significantly.

Secondary Outcomes Based on Admission Order Set Use During Period 2 (April to December 2004)
OutcomeOptional or DefaultOrder Set [n = 94 (%)]No Order Set [n = 197 (%)]P Value
  • NOTE: Data refer to the number (proportion) of admissions with the specific order. Optional or default refers to the order's status in the order set (see Order Set Development section). As discussed in Patients and Methods, this additional information was only collected during this time period.

  • A printing error left this order off of some order sets.

  • This order was default for the chronic obstructive pulmonary disease order set and optional for all others.

Documentation    
Admitting diagnosisOptional91 (96.8)187 (94.9)0.47
AllergiesOptional51 (54.3)19 (9.6)<0.0001
Resuscitation statusOptional54 (57.4)20 (10.2)<0.0001
General care orders    
ECG and call MD for chest painDefault*80 (85.1)0 (0.0)<0.0001
Allied health consult 59 (62.8)25 (12.7)<0.0001
DietOptional90 (95.7)188 (95.4)0.90
ActivityOptional80 (85.1)150 (76.1)0.08
Vitals signs and frequencyOptional91 (96.8)178 (90.4)0.052
Standard hospital diabetic dietOptional16 (17.0)10 (5.1)0.0008
Standard hospital insulin sliding scaleOptional18 (19.1)15 (7.6)0.004
Standard hospital potassium protocolOptional60 (63.8)1 (0.51)<0.0001
Nighttime sedation    
Zopiclone as neededOptional43 (45.7)2 (1.0)< 0.0001
Lorazepam as neededOptional12 (12.8)15 (7.6)0.16
Laboratory test order    
Blood urea nitrogenOptional37 (39.4)117 (59.0)0.0014
Order formatting    
Numbering of pagesDefault94 (100)4 (2.0)<0.0001
Dating of ordersOptional79 (84.0)185 (93.9)0.0067
Timing of ordersOptional14 (14.9)29 (14.7)0.97
Signing of ordersOptional93 (98.9)196 (99.5)0.54
Standard arrangement of orders 81 (86.2)66 (33.5)<0.0001

Order sets were not associated with changes in diet, activity, or vital sign orders, documentation of admission diagnosis, or the signing and timing of orders. Apart from order timing, these orders were present in >75% of admissions completed without order sets. The only negative effect of order sets was a reduction in the dating of orders (84.0% of order set admissions versus 93.9% of nonorder set admissions, P = 0.007). Finally, order sets had both an intended and unintended effect on nighttime sedation orders. Relative frequency of ordering of zopiclone compared to lorazepam increased (43/55 in the order set group vs. 2/17 in the no order set group [P < 0.0001], the intended effect), and increased overall frequency of ordering of nighttime sedation (55/94 vs. 17/197 [P < 0.0001], an unintended effect).

The additional chart audit in OctoberNovember 2007, just prior to final submission of the manuscript, determined that clinician use of order sets had increased to 92.6% of admitted medical patients, and that ordering of DVT prophylaxis in patients admitted with order sets had been sustained at 43.2% (P = 0.90 compared to period 3) (Figure 1).

DISCUSSION

We found that paper‐based order sets were associated with markedly increased use of DVT prophylaxis and made physician ordering more consistent with hospital consensus guidelines in multiple other areas, including laboratory test utilization and general care, while also increasing completeness of documentation. Given the difficulties and limited resources frequently associated with guideline development, dissemination, and implementation,12 it is worth noting that our improvements were achieved in a community hospital with voluntary physician adoption and no dedicated project funding, care process redesign, or healthcare worker education. The broad impact of order sets combined with minimal organizational resources required for implementation in this study suggests that this clinical decision support tool may have wide applicability.

The study hospital used paper‐based orders rather than CPOE, similar to 90% of U.S. hospitals at the time of the study.9 Order sets can be deployed in either paper‐based or computerized ordering systems. By providing a mechanism for entering large blocks of orders in an efficient manner, paper‐based order sets may be a necessary first step to facilitate the paper to CPOE transition, making them well suited to the current care delivery environment. Successful use of paper‐based order sets may help accelerate adoption of CPOE, which appears to be many years away from full implementation in the majority of U.S. hospitals.13

The most clinically important outcome in our study was a more than 4‐fold increase in ordering of DVT prophylaxis (last study period compared with baseline) in medical patients admitted with order sets, compared to a smaller increase in patients admitted without order sets. Our result is particularly significant as this study was performed in a community hospital, a setting with a lower adherence to DVT prophylaxis guidelines compared to academic centers.8, 14 The increase in DVT prophylaxis in patients admitted without order sets could be the result of a secular trend or a passive educational effect of order sets on physicians who only used order sets intermittently. The study was not publicized and thus was unlikely in itself to contribute to the increased performance.

We did not assess clinical outcomes of DVT or pulmonary embolism, but the clinical efficacy of improving adherence to DVT prophylaxis has been previously established.15 We also did not assess the appropriateness of DVT prophylaxis (or any other order). However, a recent multicenter Canadian observational study, using the American College of Chest Physician's Consensus Guidelines on Antithrombotic Therapy16 as a reference standard, found that 90% of medical patients admitted to hospital meeting study criteria had indications for thromboprophylaxis, but only 16% of eligible patients actually received it.8 In addition, multivariable regression analysis demonstrated even lower utilization in community hospitals compared to academic hospitals. These data suggest that the study hospital is typical of Canadian hospitals, and that the low overall utilization of DVT prophylaxis (13% of hospital patient‐days) prior to the availability of order sets in the study hospital is a significant gap between optimal and actual practice.

In addition, order sets had an impact on many secondary outcomes, such as standardization and completeness of orders (for example documentation of allergies and resuscitation status). While these effects appear to be beneficial in terms of quality of care and patient safety, the relationship of our secondary outcomes to patient‐important outcomes has not been established.

Furthermore, our before‐after design does not exclude the possibility of unknown confounding effects as explanations of improved performance in the order set group. For example, the change could have been driven by a small number of admitting physicians, since it is likely that order sets were adopted more readily by some physicians than others, and this group could have been responsible for a greater proportion of the admissions at different times. Unfortunately, we did not record the identity of the admitting physician. However, data from OctoberNovember 2007 show that >90% of medical patients were admitted using order sets, suggesting that voluntary clinician adoption of order sets has become nearly universal. Nevertheless, there still appear to be a few physicians who rarely or never used orders sets. Motivating these physicians to prescribe appropriate DVT prophylaxis remains a challenge.

Although this study was conducted in 1 center, other hospitals have similarly low rates of thromboprophylaxis,8 and our order set implementation strategy consumed few resources, improving the generalizability of our results. While most changes were beneficial, order set use was associated with decreased dating of orders and with an unintended effect or overall increase ordering of nightime sedation. Although the reasons for this are unclear, it highlights the importance of systematically evaluating the impact of order sets to identify unintended consequences and areas in which the order set may need to be redesigned to address these issues.

The study of order sets is preliminary despite their role as a key enabler for CPOE17 and their suggested usefulness to reduce medical error.18 For example, order sets were not considered in recent analyses of factors predicting success of computerized decision support systems19, 20 and have not been reviewed by the Cochrane Effective Practice and Organisation of Care Group.21 As discussed in the introduction, several studies have demonstrated that disease‐specific order sets can increase the use of evidence‐based treatments.46 Our study extends this work by demonstrating that admission order sets can improve performance hospital‐wide for a broad range of outcomes simultaneously, including DVT prophylaxis. Although most studies have demonstrated increased utilization of evidence‐based therapies, at least 1 study found no increased use of aspirin, heparin, or beta‐blockers in acute coronary syndrome admissions with the introduction of order sets.22 This suggests that the way order sets are structured or introduced is important to ensure that they achieve the desired changes in practice. Finally, our study suggests that improved outcomes using order sets can still be achieved with minimal organizational resources.

Order sets may potentially complement other decision support tools such as alerts and reminders. Alerts are an effective decision support tool12, 23, 24 but risk disrupting clinician workflow. Moreover, excessive alerts can lead to alert fatigue, resulting in many alerts being ignored.25 This phenomenon reduces alert effectiveness and limits the number of issues that alerts can address simultaneously. In contrast, order sets are broad in scope due to integration with clinical workflow, but lack the ability of alerts to apply rules to a specific patient's data. A potentially effective 2‐staged decision support strategy would use order sets as the primary admission decision support tool and selective alerts for remaining issues. This approach may increase the overall scope, physician adoption, and effectiveness of clinical decision support, and should be evaluated.

Our postintervention rate of DVT prophylaxis, while substantially improved from baseline, is still below ideal practice. Order sets were simply made available to clinicians admitting medical patients, who had the option to select DVT prophylaxis. Given limited resources, we did not develop and implement education programs regarding the appropriate use of DVT prophylaxis or make available any DVT risk assessment evaluations (available in Ref.26). Our study methodology thus provides a realistic assessment of improvements attainable in other hospitals with similarly limited resources. Additional increases in DVT prophylaxis rates would likely require a more comprehensive and resource‐intensive multifaceted quality improvement initiative. Detailed guidelines and supporting references for implementing such an initiative are available from the Society of Hospital Medicine.26 As described in their Venous Thromboembolism (VTE) Resource Room,26 such an initiative should include a standardized DVT risk assessment to guide the need for DVT prophylaxis integrated into admission order sets; prompts to order DVT prophylaxis when completing admission orders; and a system to audit adverse events and variations from best practice and return this information to clinicians.26

CONCLUSIONS

This is the largest and most comprehensive evaluation of the effectiveness of order sets as a clinical decision support tool. We found that order sets improved the quality of multiple patient orders and improved hospital‐wide DVT prophylaxis rates. These improvements were achieved in a community hospital with voluntary physician adoption and no dedicated project funding, care process redesign, or healthcare worker education. Although used in a paper‐based format in this study, order sets can also be employed in a computerized ordering environment. By providing a mechanism for entering large blocks of orders in an efficient manner, paper‐based order sets may be a necessary first step to facilitate the paper‐to‐CPOE transition. These attributes make order sets an attractive quality improvement tool in community and academic settings. More research is needed on the optimal design and use of this promising decision support tool.

The use of clinical decision support, despite great promise to improve health care, remains preliminary.1 The broad scope of quality and safety challenges facing clinicians2, 3 requires this situation to change. There is an urgent need to develop decision support tools and strategies that are effective, address many quality issues simultaneously, and are easy to implement in both academic and community settings.

One decision support tool that could help to meet this challenge is the order set. An order set is a group of orders with a common functional purpose that is used directly by a physician to create orders for a specific patient. Order sets can be used with either paper‐based or computerized provider order entry (CPOE) systems. Several studies have investigated the delivery of focused evidence‐based treatments to patients admitted using disease‐specific order sets compared with either historical or concurrent controls and have demonstrated increased use of therapies such as aspirin for acute myocardial infarction admissions,4 systemic corticosteroids, metered‐dose inhalers and pulse oximetry for pediatric asthma admissions,5 and venous thromboembolism prophylaxis for adult emergency department admissions.6 However, the ability of order sets to improve multiple quality measures in a diverse patient population has not been evaluated previously.

This study examined the effect of paper‐based order sets on the quality of admission orders for general medical patients in a community hospital. The primary hypothesis was that order set use would increase the proportion of general medical patients ordered deep venous thrombosis (DVT) prophylaxis. We chose this primary endpoint because DVT prophylaxis continues to be significantly underused in hospitalized patients.7, 8 Secondary hypotheses were that order sets would improve other admission order quality of care measures. We studied paper‐based order sets because the study hospital, along with the vast majority of North American hospitals, uses paper for order entry.9

PATIENTS AND METHODS

Study Setting

The study took place in a 750‐bed community hospital in Mississauga, Ontario, Canada. The study included only general medical patients and excluded cardiology, neurology, and intensive care unit patients. Approximately 30 different internists admitted patients during the study period from April 1, 2003 to March 31, 2005. The internists were not aware that this study was being conducted. Order sets were implemented as an option for writing admission orders in December 2003. Prior to the implementation of order sets, physicians wrote all admission orders using traditional free‐text handwritten orders on blank paper order sheets. Essentially all general medical patients are admitted through the emergency room. The hospital's Research Ethics Board approved this study.

Order Set Development

Local specialists developed order set content (evidence‐based where possible) using informal consensus methods, without explicitly grading evidence. This process created a general admission order set and six diagnosis‐specific order sets (community acquired pneumonia, chronic obstructive pulmonary disease [COPD], febrile neutropenia, soft tissue infection, upper gastrointestinal [GI] bleeding, and urinary tract infection [UTI]). All order sets contained the same orders pertaining to the primary and secondary outcomes, except for the GI bleed admission order set, which did not contain a DVT prophylaxis section.

Order sets were paper‐based and consisted of a menu of orders typically required for a medical admission. These included admitting service, admitting physician, allergies, resuscitation status, diet, activity level, frequency of vital sign measurement, laboratory investigations, diagnostic imaging, intravenous fluid therapy, and medications. The orders were either optional (requiring the physician to check a box to be performed) or default (enacted unless specifically crossed out by the physician). Both order types could consist of a single order (for example, heparin for DVT prophylaxis) or several orders simultaneously (for example, measurement of serum sodium, potassium, and creatinine). All order sets included space for additional free‐text handwritten orders to meet individual patient needs.

The DVT prophylaxis section contained optional orders for 5,000 units of heparin subcutaneously (sc) twice daily (BID) and compression stockings. The ordering physician could select 1, both, or neither of these options. Initiating other forms of DVT prophylaxis or therapeutic anticoagulation required additional free‐text handwritten orders.

Informal clinician feedback led to improved order set content and formatting in August 2004. Orders pertaining to study outcomes were unchanged in this upgrade.

Implementation

In December 2003, we placed the order sets near the stacks of blank paper order sheets used by internists admitting patients in the Emergency Department. We notified physicians by e‐mail when order sets became available but provided no formal education about order sets, DVT prophylaxis, or other study outcomes. The use of order sets was voluntary. We developed a website to facilitate timely reordering of depleted order sets from the hospital's print shop and trained all emergency room clerks regarding website access and storage of the order sets in convenient locations for physicians. Although order set availability was not formally assessed, there were no reports by physicians or observations by study investigators of order sets being unavailable at any time.

Data Collection

To assess the effect of order sets on the ordering of DVT prophylaxis, we retrospectively and randomly selected patient admissions and reviewed these patients' charts from 3 time periods during the study period: OctoberNovember 2003 (period 1, immediately prior to availability of order sets; 113 charts available of 120 discharged patients randomly selected from a total of 1,169 discharges); AprilDecember 2004 (period 2, 412 months after order set availability; 291 charts available of 300 discharged patients randomly selected from a total of 4,620 discharges); and FebruaryMarch 2005 (period 3, 1415 months after order set availability; 283 charts available of 290 randomly selected discharges out of a total of 1,057 discharges). We conducted an additional chart audit just prior to final submission of the manuscript (108 charts available of 120 discharged patients randomly selected from a total of 1,060 discharges in OctoberNovember 2007) to determine the sustainability of the improvements. The same patient could be selected in different time periods. One author (C.O. or K.D.) reviewed each chart using a jointly developed data collection form.

We assessed the admission orders of each chart for the use of an order set and the ordering of DVT prophylaxis, defined as 5,000 units of heparin sc BID or compression stockings (no patient received sc heparin 3 times daily, heparin sc BID in doses greater than 5,000 units, prophylactic doses of low molecular‐weight heparin, or low dose warfarin). We recorded the ordering of therapeutic anticoagulation, defined as intravenous heparin, full‐dose low molecular‐weight heparin, or warfarin with a target international normalized ratio 2.

Independent from the chart review, we examined the overall administration of heparin doses for DVT prophylaxis to all medical inpatients using the hospital pharmacy database. We estimated the overall administration of heparin for DVT prophylaxis in medical inpatients (136 medical beds, 4 wards) on a monthly basis from April 1, 2003 (8 months prior to order set availability) to March 30, 2005 (15 months after order set availability). We calculated monthly utilization as the proportion of patient‐days for which DVT prophylaxis was administered, as follows: (number of doses of subcutaneous heparin dispensed by the hospital pharmacy to the 4 wards)/(2 [since there are 2 doses per patient‐day] number of patient‐days).

We collected additional data from the charts selected during period 2 (AprilDecember 2004) to evaluate the effect of order sets on the following secondary outcomes: (1) the documentation of admission diagnosis, allergies, and code status; (2) general care orders (electrocardiogram [ECG] and notification of physician for chest pain, allied health consultation, standard hospital potassium replacement protocol [already available in the hospital], standard hospital diabetic diet and standard hospital insulin sliding scale [for patients with diabetes], night time sedation diet or nil per os, activity level and vital sign frequency); (3) blood urea nitrogen (BUN), a laboratory test often inappropriately ordered according to local guidelines10; (4) order formatting (numbering, dating, timing, and signing of all order pages); and (5) organization of orders in the standardized arrangement used in the order sets. This standardized arrangement of content was as follows: attending physician, admitting diagnosis, requests for consultation, diet, activity, vital signs, oxygen, nasogastric tube, urinary catheter, investigations, intravenous fluids, and medications. Free‐text admission orders and order set orders that maintained this arrangement were recorded as standardized. We did not assess order appropriateness.

We recorded the characteristics of all medical patients admitted to the hospital in two 1‐year periods during the study (April 1, 2003 to March 30, 2004 and April 1, 2004 to March 30, 2005), including age, gender, length of stay, diagnosis (defined by case management group [CMG]), and resource intensity weight (RIW). CMG defines groups of patients who are similar in diagnosis or procedure and RIW is a measure of resources used during a patient's hospital stay.11 The definitions of CMG and RIW did not change during the study.

Statistical Analysis

Baseline characteristics were compared using Student t‐test for normally distributed continuous variables (patient age) and the Mann‐Whitney U test for skewed continuous variables (length of stay and RIW). Chi square or Fisher's exact tests were used to compare categorical variables. Relative risks (RR) and 95% confidence intervals (CI) were calculated and compared using a z‐test. A 2‐sided P value <0.05 was taken to be statistically significant. All calculations were carried out using SAS Version 8.2 (SAS Institute, Cary, NC).

RESULTS

As shown in Table 1, there were no clinically important differences in demographic or clinical characteristics of medical patients between the 2 years of the study. There were small but statistically significant increases in patient illness complexity (as reflected in median RIW) (P = 0.003) and length of stay (P = 0.0002).

Patient Characteristics in the Two 1‐Year Periods
Patient CharacteristicApril 1, 2003 to March 30, 2004 (n = 4,415)April 1, 2004 to March 30, 2005 (n = 4,287)
  • NOTE: Only the 5 most common case mix groups are shown.

  • Abbreviations: IQR, interquartile range; n, number of medical admissions; RIW, resource intensity weight (see Data Collection section for details); SD, standard deviation.

Age, mean SD67.2 17.767.6 17.5
Length of stay, median days (IQR)6 (3‐12)6 (3‐13)
RIW,11 median (IQR)0.96 (0.68‐1.73)1.03 (0.72‐1.88)
Females, number (% of total)2,276 (52)2,223 (52)
Case mix group,11 number of patients (% of total)  
Chronic obstructive pulmonary disease357 (8.1)385 (9.0)
Simple pneumonia and pleurisy322 (7.3)217 (5.1)
Esophageal, gastrointestinal, and miscellaneous digestive disease223 (5.1)239 (5.6)
Gastrointestinal hemorrhage185 (4.2)198 (4.6)
Respiratory neoplasm127 (2.9)144 (3.4)
Total1,214 (27.5)1,183 (27.7)

Clinicians used order sets in 32.3% of admissions during period 2 (AprilDecember 2004, 412 months after order set availability), increasing to 51.6% in period 3 (FebruaryMarch 2005, 1415 months after order set availability). The results of the chart audit assessing the impact of order set use on DVT prophylaxis are shown in Figure 1. Prior to order set introduction, 10.9% of patients received orders for DVT prophylaxis. Subsequently, ordering of DVT prophylaxis in patients admitted with order sets increased (period 2: 35.6%; P < 0.001; RR, 3.27; 95% CI, 1.806.12 and period 3: 44.0%; P < 0.001; RR, 4.04; 95% CI, 2.327.31). In contrast, DVT prophylaxis ordering in the nonorder set group was initially unchanged (period 2: 10.6%; P = 0.93; RR, 0.97; 95% CI, 0.491.95), although later it increased to a smaller extent (period 3: 20.6%; P = 0.049; RR, 1.90; 95% CI, 1.013.65). As a result of this differential increase, patients admitted with order sets were more likely to be ordered DVT prophylaxis in both study periods (period 2: 35.6% versus 10.6%; P < 0.0001; RR, 3.38; 95% CI, 2.035.62 and period 3: 44.0% versus 20.6%; P < 0.0001; RR, 2.13; 95% CI, 1.443.16). The use of therapeutic anticoagulation was similar in patients admitted with and without order sets and did not change between time periods.

Figure 1
Orders for DVT prophylaxis in patients admitted with and without order sets for 4 time periods: October‐November 2003 (n = 113), April‐December 2004 (n = 291), February‐March 2005 (n = 283), and November‐December 2007 (n = 108).

The hospital‐wide monthly utilization of heparin for DVT prophylaxis in medical inpatients increased from an average of 12.8% (range, 9.7%16.1%) of patient‐days before order set implementation (AprilNovember 2003) to 18.5% (range, 16.4%20.0%) of patient‐days in the 8 months after order sets were first implemented (DecemberJuly 2004, P < 0.0001 compared to the preorder set time period). After August 2004, when upgraded order sets were introduced, DVT prophylaxis utilization increased further in the last 7 months of the study to 25.8% (range, 22.4%32.2%; P < 0.0001 compared to preorder set time period; Figure 2).

Figure 2
Monthly DVT utilization was calculated as the proportion of patient‐days for which DVT prophylaxis was administered: (number of doses of subcutaneous heparin dispensed by the hospital pharmacy to the 4 wards)/(2 [since there are 2 doses per patient‐day] × number of patient‐days). White bars, prior to order sets; gray hashed bars, after order set introduction; black solid bars, after introduction of revised order sets based on clinician feedback with improved content and formatting.

Table 2 shows the impact of order sets on secondary outcomes. Admissions completed with order sets had statistically significant increases in general care orders (ECG and notification of physician for chest pain, allied health consultations and standard hospital diabetic diet, insulin scale, and potassium replacement protocol orders), documentation of allergies and code status, numbering of pages, and use of a standardized arrangement for orders. Ordering of BUN decreased significantly.

Secondary Outcomes Based on Admission Order Set Use During Period 2 (April to December 2004)
OutcomeOptional or DefaultOrder Set [n = 94 (%)]No Order Set [n = 197 (%)]P Value
  • NOTE: Data refer to the number (proportion) of admissions with the specific order. Optional or default refers to the order's status in the order set (see Order Set Development section). As discussed in Patients and Methods, this additional information was only collected during this time period.

  • A printing error left this order off of some order sets.

  • This order was default for the chronic obstructive pulmonary disease order set and optional for all others.

Documentation    
Admitting diagnosisOptional91 (96.8)187 (94.9)0.47
AllergiesOptional51 (54.3)19 (9.6)<0.0001
Resuscitation statusOptional54 (57.4)20 (10.2)<0.0001
General care orders    
ECG and call MD for chest painDefault*80 (85.1)0 (0.0)<0.0001
Allied health consult 59 (62.8)25 (12.7)<0.0001
DietOptional90 (95.7)188 (95.4)0.90
ActivityOptional80 (85.1)150 (76.1)0.08
Vitals signs and frequencyOptional91 (96.8)178 (90.4)0.052
Standard hospital diabetic dietOptional16 (17.0)10 (5.1)0.0008
Standard hospital insulin sliding scaleOptional18 (19.1)15 (7.6)0.004
Standard hospital potassium protocolOptional60 (63.8)1 (0.51)<0.0001
Nighttime sedation    
Zopiclone as neededOptional43 (45.7)2 (1.0)< 0.0001
Lorazepam as neededOptional12 (12.8)15 (7.6)0.16
Laboratory test order    
Blood urea nitrogenOptional37 (39.4)117 (59.0)0.0014
Order formatting    
Numbering of pagesDefault94 (100)4 (2.0)<0.0001
Dating of ordersOptional79 (84.0)185 (93.9)0.0067
Timing of ordersOptional14 (14.9)29 (14.7)0.97
Signing of ordersOptional93 (98.9)196 (99.5)0.54
Standard arrangement of orders 81 (86.2)66 (33.5)<0.0001

Order sets were not associated with changes in diet, activity, or vital sign orders, documentation of admission diagnosis, or the signing and timing of orders. Apart from order timing, these orders were present in >75% of admissions completed without order sets. The only negative effect of order sets was a reduction in the dating of orders (84.0% of order set admissions versus 93.9% of nonorder set admissions, P = 0.007). Finally, order sets had both an intended and unintended effect on nighttime sedation orders. Relative frequency of ordering of zopiclone compared to lorazepam increased (43/55 in the order set group vs. 2/17 in the no order set group [P < 0.0001], the intended effect), and increased overall frequency of ordering of nighttime sedation (55/94 vs. 17/197 [P < 0.0001], an unintended effect).

The additional chart audit in OctoberNovember 2007, just prior to final submission of the manuscript, determined that clinician use of order sets had increased to 92.6% of admitted medical patients, and that ordering of DVT prophylaxis in patients admitted with order sets had been sustained at 43.2% (P = 0.90 compared to period 3) (Figure 1).

DISCUSSION

We found that paper‐based order sets were associated with markedly increased use of DVT prophylaxis and made physician ordering more consistent with hospital consensus guidelines in multiple other areas, including laboratory test utilization and general care, while also increasing completeness of documentation. Given the difficulties and limited resources frequently associated with guideline development, dissemination, and implementation,12 it is worth noting that our improvements were achieved in a community hospital with voluntary physician adoption and no dedicated project funding, care process redesign, or healthcare worker education. The broad impact of order sets combined with minimal organizational resources required for implementation in this study suggests that this clinical decision support tool may have wide applicability.

The study hospital used paper‐based orders rather than CPOE, similar to 90% of U.S. hospitals at the time of the study.9 Order sets can be deployed in either paper‐based or computerized ordering systems. By providing a mechanism for entering large blocks of orders in an efficient manner, paper‐based order sets may be a necessary first step to facilitate the paper to CPOE transition, making them well suited to the current care delivery environment. Successful use of paper‐based order sets may help accelerate adoption of CPOE, which appears to be many years away from full implementation in the majority of U.S. hospitals.13

The most clinically important outcome in our study was a more than 4‐fold increase in ordering of DVT prophylaxis (last study period compared with baseline) in medical patients admitted with order sets, compared to a smaller increase in patients admitted without order sets. Our result is particularly significant as this study was performed in a community hospital, a setting with a lower adherence to DVT prophylaxis guidelines compared to academic centers.8, 14 The increase in DVT prophylaxis in patients admitted without order sets could be the result of a secular trend or a passive educational effect of order sets on physicians who only used order sets intermittently. The study was not publicized and thus was unlikely in itself to contribute to the increased performance.

We did not assess clinical outcomes of DVT or pulmonary embolism, but the clinical efficacy of improving adherence to DVT prophylaxis has been previously established.15 We also did not assess the appropriateness of DVT prophylaxis (or any other order). However, a recent multicenter Canadian observational study, using the American College of Chest Physician's Consensus Guidelines on Antithrombotic Therapy16 as a reference standard, found that 90% of medical patients admitted to hospital meeting study criteria had indications for thromboprophylaxis, but only 16% of eligible patients actually received it.8 In addition, multivariable regression analysis demonstrated even lower utilization in community hospitals compared to academic hospitals. These data suggest that the study hospital is typical of Canadian hospitals, and that the low overall utilization of DVT prophylaxis (13% of hospital patient‐days) prior to the availability of order sets in the study hospital is a significant gap between optimal and actual practice.

In addition, order sets had an impact on many secondary outcomes, such as standardization and completeness of orders (for example documentation of allergies and resuscitation status). While these effects appear to be beneficial in terms of quality of care and patient safety, the relationship of our secondary outcomes to patient‐important outcomes has not been established.

Furthermore, our before‐after design does not exclude the possibility of unknown confounding effects as explanations of improved performance in the order set group. For example, the change could have been driven by a small number of admitting physicians, since it is likely that order sets were adopted more readily by some physicians than others, and this group could have been responsible for a greater proportion of the admissions at different times. Unfortunately, we did not record the identity of the admitting physician. However, data from OctoberNovember 2007 show that >90% of medical patients were admitted using order sets, suggesting that voluntary clinician adoption of order sets has become nearly universal. Nevertheless, there still appear to be a few physicians who rarely or never used orders sets. Motivating these physicians to prescribe appropriate DVT prophylaxis remains a challenge.

Although this study was conducted in 1 center, other hospitals have similarly low rates of thromboprophylaxis,8 and our order set implementation strategy consumed few resources, improving the generalizability of our results. While most changes were beneficial, order set use was associated with decreased dating of orders and with an unintended effect or overall increase ordering of nightime sedation. Although the reasons for this are unclear, it highlights the importance of systematically evaluating the impact of order sets to identify unintended consequences and areas in which the order set may need to be redesigned to address these issues.

The study of order sets is preliminary despite their role as a key enabler for CPOE17 and their suggested usefulness to reduce medical error.18 For example, order sets were not considered in recent analyses of factors predicting success of computerized decision support systems19, 20 and have not been reviewed by the Cochrane Effective Practice and Organisation of Care Group.21 As discussed in the introduction, several studies have demonstrated that disease‐specific order sets can increase the use of evidence‐based treatments.46 Our study extends this work by demonstrating that admission order sets can improve performance hospital‐wide for a broad range of outcomes simultaneously, including DVT prophylaxis. Although most studies have demonstrated increased utilization of evidence‐based therapies, at least 1 study found no increased use of aspirin, heparin, or beta‐blockers in acute coronary syndrome admissions with the introduction of order sets.22 This suggests that the way order sets are structured or introduced is important to ensure that they achieve the desired changes in practice. Finally, our study suggests that improved outcomes using order sets can still be achieved with minimal organizational resources.

Order sets may potentially complement other decision support tools such as alerts and reminders. Alerts are an effective decision support tool12, 23, 24 but risk disrupting clinician workflow. Moreover, excessive alerts can lead to alert fatigue, resulting in many alerts being ignored.25 This phenomenon reduces alert effectiveness and limits the number of issues that alerts can address simultaneously. In contrast, order sets are broad in scope due to integration with clinical workflow, but lack the ability of alerts to apply rules to a specific patient's data. A potentially effective 2‐staged decision support strategy would use order sets as the primary admission decision support tool and selective alerts for remaining issues. This approach may increase the overall scope, physician adoption, and effectiveness of clinical decision support, and should be evaluated.

Our postintervention rate of DVT prophylaxis, while substantially improved from baseline, is still below ideal practice. Order sets were simply made available to clinicians admitting medical patients, who had the option to select DVT prophylaxis. Given limited resources, we did not develop and implement education programs regarding the appropriate use of DVT prophylaxis or make available any DVT risk assessment evaluations (available in Ref.26). Our study methodology thus provides a realistic assessment of improvements attainable in other hospitals with similarly limited resources. Additional increases in DVT prophylaxis rates would likely require a more comprehensive and resource‐intensive multifaceted quality improvement initiative. Detailed guidelines and supporting references for implementing such an initiative are available from the Society of Hospital Medicine.26 As described in their Venous Thromboembolism (VTE) Resource Room,26 such an initiative should include a standardized DVT risk assessment to guide the need for DVT prophylaxis integrated into admission order sets; prompts to order DVT prophylaxis when completing admission orders; and a system to audit adverse events and variations from best practice and return this information to clinicians.26

CONCLUSIONS

This is the largest and most comprehensive evaluation of the effectiveness of order sets as a clinical decision support tool. We found that order sets improved the quality of multiple patient orders and improved hospital‐wide DVT prophylaxis rates. These improvements were achieved in a community hospital with voluntary physician adoption and no dedicated project funding, care process redesign, or healthcare worker education. Although used in a paper‐based format in this study, order sets can also be employed in a computerized ordering environment. By providing a mechanism for entering large blocks of orders in an efficient manner, paper‐based order sets may be a necessary first step to facilitate the paper‐to‐CPOE transition. These attributes make order sets an attractive quality improvement tool in community and academic settings. More research is needed on the optimal design and use of this promising decision support tool.

References
  1. Berg M.Health Information Management: Integrating Information Technology in Health Care Work.London:Routledge;2004.
  2. Kohn KT,Corrigan JM,Donaldson MS.To Err Is Human: Building a Safer Health System.Washington, DC:National Academy Press;2006.
  3. National Healthcare Quality Report.2004.Rockville, MD:Agency for Healthcare Research and Quality;2006.
  4. Ozdas A,Speroff T,Waitman LR,Ozbolt J,Butler J,Miller RA.Integrating “best of care” protocols into clinicians' workflow via care provider order entry: impact on quality‐of‐care indicators for acute myocardial infarction.J Am Med Inform Assoc.2006;13:188196.
  5. Chisholm DJ,McAlearney AS,Veneris S,Fisher D,Holtzlander M,McCoy KS.The role of computerized order sets in pediatric inpatient asthma treatment.Ped Allergy Immunol.2006;17:199206.
  6. Levine RL,Hergenroeder GW,Miller CC,Davies A.Venous thromboembolism prophylaxis in emergency department admissions.J Hosp Med.2007;2:7985.
  7. Goldhaber SZ.DVT prevention: what is happening in the “real world”?Semin Thromb Hemost.2003;29(Suppl 1):2331.
  8. Kahn SR,Panju A,Geerts W, et al.Multicenter evaluation of the use of venous thromboembolism prophylaxis in acutely ill medical patients in Canada.Thromb Res.2007;119:145155.
  9. Ash JS,Gorman PN,Seshadri V,Hersh WR.Computerized physician order entry in US hospitals: results of a 2002 survey.J Am Med Inform Assoc.2004;11:9599.
  10. Ontario Association of Medical Laboratories. Guidelines for the Use of Serum Tests to Detect Renal Dysfunction. Available at:http://www.oaml.com/PDF/CLP007.pdf. Accessed 12 May2008.
  11. Pink GH,Bolley HB.Physicians in health care management: 3. Case mix groups and resource intensity weights: an overview for physicians.CMAJ.1994;150:889894.
  12. Grimshaw JM,Thomas RE,MacLennan G, et al.Effectiveness and efficiency of guideline dissemination and implementation strategies.Health Technol Assess.2004;8(6):iiiiv,1–72.
  13. Ford EW,McAlearney AS,Phillips MT,Menachemi N,Rudolph B.Predicting computerized physician order entry system adoption in US hospitals: can the federal mandate be met?Int J Med Inform.2008;77(8):539545.
  14. Anderson FA,Wheeler HB,Goldberg RJ,Hosmer DW,Forcier A,Patwardhan NA.Physician practices in the prevention of venous thromboembolism.Ann Intern Med.1991;115:591595.
  15. Kucher N,Koo S,Quiroz R, et al.Electronic alerts to prevent venous thromboembolism among hospitalized patients.N Engl J Med.2005;352:969977.
  16. Geerts WH,Heit JA,Clagett GP, et al.Prevention of venous thromboembolism.Chest.2001;119(Suppl 1):132S175S.
  17. Ash JS,Stavri PZ,Kuperman GJ.A consensus statement on considerations for a successful CPOE implementation.J Am Med Inform Assoc.2003;10:229234.
  18. Volpp KGM,Grande D.Residents' suggestions for reducing errors in teaching hospitals.N Engl J Med.2003;348:851855.
  19. Kawamoto K,Houlihan CA,Balas EA,Lobach DF.Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.BMJ.2005;330:765.
  20. Garg AX,Adhikari NKJ,McDonald H, et al.Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.JAMA.2005;293:12231238.
  21. Cochrane Reviews by the Effective Practice and Organisation of Care Group. Available at: http://www.cochrane.org/reviews/en/topics/61_reviews.html. Accessed 12 May2008.
  22. Asaro PV,Sheldahl AL,Char DM.Embedded guideline information without patient specificity in a commercial emergency department computerized order‐entry system.Acad Emer Med.2006;13:452458.
  23. Dexter PR,Perkins S,Overhage JM,Maharry K,Kohler RB,McDonald CJ.A computerized reminder system to increase the use of preventive care for hospitalized patients.N Engl J Med.2001;345:965970.
  24. Durieux P,Nizard R,Ravaud P,Mounier N,Lepage E.A clinical decision support system for prevention of venous thromboembolism: effect on physician behavior.JAMA.2000;283:28162821.
  25. Handler JA,Feied CF,Coonan K, et al.Computerized physician order entry and online decision support.Acad Emerg Med.2004;11:11351141.
  26. Society of Hospital Medicine VTE Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_VTE/VTE_Home.cfm. Accessed 12 May2008.
References
  1. Berg M.Health Information Management: Integrating Information Technology in Health Care Work.London:Routledge;2004.
  2. Kohn KT,Corrigan JM,Donaldson MS.To Err Is Human: Building a Safer Health System.Washington, DC:National Academy Press;2006.
  3. National Healthcare Quality Report.2004.Rockville, MD:Agency for Healthcare Research and Quality;2006.
  4. Ozdas A,Speroff T,Waitman LR,Ozbolt J,Butler J,Miller RA.Integrating “best of care” protocols into clinicians' workflow via care provider order entry: impact on quality‐of‐care indicators for acute myocardial infarction.J Am Med Inform Assoc.2006;13:188196.
  5. Chisholm DJ,McAlearney AS,Veneris S,Fisher D,Holtzlander M,McCoy KS.The role of computerized order sets in pediatric inpatient asthma treatment.Ped Allergy Immunol.2006;17:199206.
  6. Levine RL,Hergenroeder GW,Miller CC,Davies A.Venous thromboembolism prophylaxis in emergency department admissions.J Hosp Med.2007;2:7985.
  7. Goldhaber SZ.DVT prevention: what is happening in the “real world”?Semin Thromb Hemost.2003;29(Suppl 1):2331.
  8. Kahn SR,Panju A,Geerts W, et al.Multicenter evaluation of the use of venous thromboembolism prophylaxis in acutely ill medical patients in Canada.Thromb Res.2007;119:145155.
  9. Ash JS,Gorman PN,Seshadri V,Hersh WR.Computerized physician order entry in US hospitals: results of a 2002 survey.J Am Med Inform Assoc.2004;11:9599.
  10. Ontario Association of Medical Laboratories. Guidelines for the Use of Serum Tests to Detect Renal Dysfunction. Available at:http://www.oaml.com/PDF/CLP007.pdf. Accessed 12 May2008.
  11. Pink GH,Bolley HB.Physicians in health care management: 3. Case mix groups and resource intensity weights: an overview for physicians.CMAJ.1994;150:889894.
  12. Grimshaw JM,Thomas RE,MacLennan G, et al.Effectiveness and efficiency of guideline dissemination and implementation strategies.Health Technol Assess.2004;8(6):iiiiv,1–72.
  13. Ford EW,McAlearney AS,Phillips MT,Menachemi N,Rudolph B.Predicting computerized physician order entry system adoption in US hospitals: can the federal mandate be met?Int J Med Inform.2008;77(8):539545.
  14. Anderson FA,Wheeler HB,Goldberg RJ,Hosmer DW,Forcier A,Patwardhan NA.Physician practices in the prevention of venous thromboembolism.Ann Intern Med.1991;115:591595.
  15. Kucher N,Koo S,Quiroz R, et al.Electronic alerts to prevent venous thromboembolism among hospitalized patients.N Engl J Med.2005;352:969977.
  16. Geerts WH,Heit JA,Clagett GP, et al.Prevention of venous thromboembolism.Chest.2001;119(Suppl 1):132S175S.
  17. Ash JS,Stavri PZ,Kuperman GJ.A consensus statement on considerations for a successful CPOE implementation.J Am Med Inform Assoc.2003;10:229234.
  18. Volpp KGM,Grande D.Residents' suggestions for reducing errors in teaching hospitals.N Engl J Med.2003;348:851855.
  19. Kawamoto K,Houlihan CA,Balas EA,Lobach DF.Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.BMJ.2005;330:765.
  20. Garg AX,Adhikari NKJ,McDonald H, et al.Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.JAMA.2005;293:12231238.
  21. Cochrane Reviews by the Effective Practice and Organisation of Care Group. Available at: http://www.cochrane.org/reviews/en/topics/61_reviews.html. Accessed 12 May2008.
  22. Asaro PV,Sheldahl AL,Char DM.Embedded guideline information without patient specificity in a commercial emergency department computerized order‐entry system.Acad Emer Med.2006;13:452458.
  23. Dexter PR,Perkins S,Overhage JM,Maharry K,Kohler RB,McDonald CJ.A computerized reminder system to increase the use of preventive care for hospitalized patients.N Engl J Med.2001;345:965970.
  24. Durieux P,Nizard R,Ravaud P,Mounier N,Lepage E.A clinical decision support system for prevention of venous thromboembolism: effect on physician behavior.JAMA.2000;283:28162821.
  25. Handler JA,Feied CF,Coonan K, et al.Computerized physician order entry and online decision support.Acad Emerg Med.2004;11:11351141.
  26. Society of Hospital Medicine VTE Resource Room. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_VTE/VTE_Home.cfm. Accessed 12 May2008.
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Journal of Hospital Medicine - 4(2)
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Medical admission order sets to improve deep vein thrombosis prophylaxis rates and other outcomes
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Allogeneic Stem Cell Transplant

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Dr. Sergio Giralt discusses his findings that suggest age should no longer be a contraindication to allogeneic stem cell transplantation. Dr. Armand Keating, press conference moderator, comments on the implications of this study during a briefing at the annual meeting of the American Society of Hematology.

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Dr. Sergio Giralt discusses his findings that suggest age should no longer be a contraindication to allogeneic stem cell transplantation. Dr. Armand Keating, press conference moderator, comments on the implications of this study during a briefing at the annual meeting of the American Society of Hematology.

Dr. Sergio Giralt discusses his findings that suggest age should no longer be a contraindication to allogeneic stem cell transplantation. Dr. Armand Keating, press conference moderator, comments on the implications of this study during a briefing at the annual meeting of the American Society of Hematology.

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Hospitalists Applaud SCHIP Expansion

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Hospitalists Applaud SCHIP Expansion

Pediatric hospitalists are praising a new bill that expands the funding and scope of the State Children's Health Insurance Program (SCHIP), a program jointly funded by federal and state governments for children in families with incomes too high to qualify for Medicaid.

"Ideally, this will lead to better primary care, more immunizations, and disease prevention," says Jack Percelay, MD, MPH, a hospitalist at E.L.M.O. Pediatrics in New York City, Treasurer of SHM's board of directors, and a member of SHM's Public Policy Committee. Percelay foresees a twofold benefit of the new legislation: a likely decrease in uninsured pediatric patients using the ED and more fee recovery from patients who otherwise couldn't pay.

David Rappaport, MD, FAAP, a hospitalist at Alfred I. Dupont Hospital for Children in Wilmington, Del., agrees. "Children's health is more than the cuddly factor—it's a smart investment in healthcare," he says, explaining that paying for preventive measures in children, such as inoculations, can save on their healthcare costs in the future.

Signed by President Obama on Feb. 4, the bill reauthorizes SCHIP for four years and expands eligibility to children in families with incomes of up to three times the federal poverty level. It also covers legal immigrant pregnant women and children who have been in the country less than five years. The expansion will cover an additional 4 million children, raising the total to 11 million uninsured children enrolled in the program. Most of the $32.8 billion increase in federal funding for the program is to be covered by a 62-cent-per-pack increase in the federal cigarette tax.

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Pediatric hospitalists are praising a new bill that expands the funding and scope of the State Children's Health Insurance Program (SCHIP), a program jointly funded by federal and state governments for children in families with incomes too high to qualify for Medicaid.

"Ideally, this will lead to better primary care, more immunizations, and disease prevention," says Jack Percelay, MD, MPH, a hospitalist at E.L.M.O. Pediatrics in New York City, Treasurer of SHM's board of directors, and a member of SHM's Public Policy Committee. Percelay foresees a twofold benefit of the new legislation: a likely decrease in uninsured pediatric patients using the ED and more fee recovery from patients who otherwise couldn't pay.

David Rappaport, MD, FAAP, a hospitalist at Alfred I. Dupont Hospital for Children in Wilmington, Del., agrees. "Children's health is more than the cuddly factor—it's a smart investment in healthcare," he says, explaining that paying for preventive measures in children, such as inoculations, can save on their healthcare costs in the future.

Signed by President Obama on Feb. 4, the bill reauthorizes SCHIP for four years and expands eligibility to children in families with incomes of up to three times the federal poverty level. It also covers legal immigrant pregnant women and children who have been in the country less than five years. The expansion will cover an additional 4 million children, raising the total to 11 million uninsured children enrolled in the program. Most of the $32.8 billion increase in federal funding for the program is to be covered by a 62-cent-per-pack increase in the federal cigarette tax.

Pediatric hospitalists are praising a new bill that expands the funding and scope of the State Children's Health Insurance Program (SCHIP), a program jointly funded by federal and state governments for children in families with incomes too high to qualify for Medicaid.

"Ideally, this will lead to better primary care, more immunizations, and disease prevention," says Jack Percelay, MD, MPH, a hospitalist at E.L.M.O. Pediatrics in New York City, Treasurer of SHM's board of directors, and a member of SHM's Public Policy Committee. Percelay foresees a twofold benefit of the new legislation: a likely decrease in uninsured pediatric patients using the ED and more fee recovery from patients who otherwise couldn't pay.

David Rappaport, MD, FAAP, a hospitalist at Alfred I. Dupont Hospital for Children in Wilmington, Del., agrees. "Children's health is more than the cuddly factor—it's a smart investment in healthcare," he says, explaining that paying for preventive measures in children, such as inoculations, can save on their healthcare costs in the future.

Signed by President Obama on Feb. 4, the bill reauthorizes SCHIP for four years and expands eligibility to children in families with incomes of up to three times the federal poverty level. It also covers legal immigrant pregnant women and children who have been in the country less than five years. The expansion will cover an additional 4 million children, raising the total to 11 million uninsured children enrolled in the program. Most of the $32.8 billion increase in federal funding for the program is to be covered by a 62-cent-per-pack increase in the federal cigarette tax.

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Back to School

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Back to School

Patience Agborbesong, MD, didn't go to SHM's "Essential Procedures for the Hospitalist" seminar on a whim. In fact, the medical director of Wake Forest Inpatient Physicians of Winston-Salem, N.C., skipped her own hospital's career day to attend the society's One-Day Hospitalist University (ODHU) session where she received four hours of hands-on training in the use of ultrasound equipment for vascular access, paracentesis, and thoracentesis.

"When I was training, we didn't use ultrasounds to routinely do central lines," says Dr. Agborbesong, an ODHU rookie. "Now that is something that is recommended as a patient safety measure. When we do the procedure without ultrasounds, you're blind-sticking and going by the anatomic landmarks."

The course was one of four one-day seminars that drew nearly 200 hospitalists to Atlanta. CME credit is awarded for all of the ODHU courses. The program also included "Best Practices in Managing a Hospital Medicine Program," "Critical Care Medicine for the Hospitalist," and "Fundamentals of Inpatient Coding and Documentation."

Like many hospitalists, Dr. Agborbesong used ODHU as a chance to expand the skill set of her 15-hospitalist group. In addition to relaying what she learned during the ultrasound course to her colleagues, she's also planning to hone her skills with help from radiologists at Wake Forest University Baptist Medical Center.

"I didn't come here thinking I would be an expert," she says. "It was a very good place to start."

To stay updated on SHM-sponsored training programs, visit the SHM Web site.

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Patience Agborbesong, MD, didn't go to SHM's "Essential Procedures for the Hospitalist" seminar on a whim. In fact, the medical director of Wake Forest Inpatient Physicians of Winston-Salem, N.C., skipped her own hospital's career day to attend the society's One-Day Hospitalist University (ODHU) session where she received four hours of hands-on training in the use of ultrasound equipment for vascular access, paracentesis, and thoracentesis.

"When I was training, we didn't use ultrasounds to routinely do central lines," says Dr. Agborbesong, an ODHU rookie. "Now that is something that is recommended as a patient safety measure. When we do the procedure without ultrasounds, you're blind-sticking and going by the anatomic landmarks."

The course was one of four one-day seminars that drew nearly 200 hospitalists to Atlanta. CME credit is awarded for all of the ODHU courses. The program also included "Best Practices in Managing a Hospital Medicine Program," "Critical Care Medicine for the Hospitalist," and "Fundamentals of Inpatient Coding and Documentation."

Like many hospitalists, Dr. Agborbesong used ODHU as a chance to expand the skill set of her 15-hospitalist group. In addition to relaying what she learned during the ultrasound course to her colleagues, she's also planning to hone her skills with help from radiologists at Wake Forest University Baptist Medical Center.

"I didn't come here thinking I would be an expert," she says. "It was a very good place to start."

To stay updated on SHM-sponsored training programs, visit the SHM Web site.

Patience Agborbesong, MD, didn't go to SHM's "Essential Procedures for the Hospitalist" seminar on a whim. In fact, the medical director of Wake Forest Inpatient Physicians of Winston-Salem, N.C., skipped her own hospital's career day to attend the society's One-Day Hospitalist University (ODHU) session where she received four hours of hands-on training in the use of ultrasound equipment for vascular access, paracentesis, and thoracentesis.

"When I was training, we didn't use ultrasounds to routinely do central lines," says Dr. Agborbesong, an ODHU rookie. "Now that is something that is recommended as a patient safety measure. When we do the procedure without ultrasounds, you're blind-sticking and going by the anatomic landmarks."

The course was one of four one-day seminars that drew nearly 200 hospitalists to Atlanta. CME credit is awarded for all of the ODHU courses. The program also included "Best Practices in Managing a Hospital Medicine Program," "Critical Care Medicine for the Hospitalist," and "Fundamentals of Inpatient Coding and Documentation."

Like many hospitalists, Dr. Agborbesong used ODHU as a chance to expand the skill set of her 15-hospitalist group. In addition to relaying what she learned during the ultrasound course to her colleagues, she's also planning to hone her skills with help from radiologists at Wake Forest University Baptist Medical Center.

"I didn't come here thinking I would be an expert," she says. "It was a very good place to start."

To stay updated on SHM-sponsored training programs, visit the SHM Web site.

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Transparent Hospitalists

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Six weeks ago hospitalist Frank Michota Jr., MD, posted on the Cleveland Clinic's Web site that he receives at least $5,000 per year in fees from pharmaceutical firms Sanofi-Aventis U.S. Inc. and Scios Inc. Since then, not one of the 100 or so patients he's encountered has asked about it.

Still, hospitalists and specialists at the teaching hospital now are required to publicly disclose their financial ties to the pharmaceutical and medical device industries. The effort is aimed at increasing physician transparency and avoiding conflicts of interest—real or perceived.

As director of academic affairs for the hospital's Department of Hospital Medicine, Dr. Michota appreciates the credibility that the disclosure provides for research. But he thinks it does little to forward patient care. Because most hospitalists are required to use drugs or devices based on formularies, whether they have financial ties to the companies making the drugs or devices is irrelevant, he says.

"It’s not the patient that's looking at this stuff," Dr. Michota says, adding, "the disclosures are more for the ethereal discussions."

He emphasizes most physicians don’t seek out relationships with drug- and device-makers.

"I use Drug A because it's on my formulary," he says. "I have a lot of experience with [Drug A] because it's on my formulary. I'm then asked by the company to research it, because I have experience with Drug A. That's how it works, not the other way around."

Check out the Cleveland Clinic’s physician directory at http://my.clevelandclinic.org/staff_directory/default.aspx.

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Six weeks ago hospitalist Frank Michota Jr., MD, posted on the Cleveland Clinic's Web site that he receives at least $5,000 per year in fees from pharmaceutical firms Sanofi-Aventis U.S. Inc. and Scios Inc. Since then, not one of the 100 or so patients he's encountered has asked about it.

Still, hospitalists and specialists at the teaching hospital now are required to publicly disclose their financial ties to the pharmaceutical and medical device industries. The effort is aimed at increasing physician transparency and avoiding conflicts of interest—real or perceived.

As director of academic affairs for the hospital's Department of Hospital Medicine, Dr. Michota appreciates the credibility that the disclosure provides for research. But he thinks it does little to forward patient care. Because most hospitalists are required to use drugs or devices based on formularies, whether they have financial ties to the companies making the drugs or devices is irrelevant, he says.

"It’s not the patient that's looking at this stuff," Dr. Michota says, adding, "the disclosures are more for the ethereal discussions."

He emphasizes most physicians don’t seek out relationships with drug- and device-makers.

"I use Drug A because it's on my formulary," he says. "I have a lot of experience with [Drug A] because it's on my formulary. I'm then asked by the company to research it, because I have experience with Drug A. That's how it works, not the other way around."

Check out the Cleveland Clinic’s physician directory at http://my.clevelandclinic.org/staff_directory/default.aspx.

Six weeks ago hospitalist Frank Michota Jr., MD, posted on the Cleveland Clinic's Web site that he receives at least $5,000 per year in fees from pharmaceutical firms Sanofi-Aventis U.S. Inc. and Scios Inc. Since then, not one of the 100 or so patients he's encountered has asked about it.

Still, hospitalists and specialists at the teaching hospital now are required to publicly disclose their financial ties to the pharmaceutical and medical device industries. The effort is aimed at increasing physician transparency and avoiding conflicts of interest—real or perceived.

As director of academic affairs for the hospital's Department of Hospital Medicine, Dr. Michota appreciates the credibility that the disclosure provides for research. But he thinks it does little to forward patient care. Because most hospitalists are required to use drugs or devices based on formularies, whether they have financial ties to the companies making the drugs or devices is irrelevant, he says.

"It’s not the patient that's looking at this stuff," Dr. Michota says, adding, "the disclosures are more for the ethereal discussions."

He emphasizes most physicians don’t seek out relationships with drug- and device-makers.

"I use Drug A because it's on my formulary," he says. "I have a lot of experience with [Drug A] because it's on my formulary. I'm then asked by the company to research it, because I have experience with Drug A. That's how it works, not the other way around."

Check out the Cleveland Clinic’s physician directory at http://my.clevelandclinic.org/staff_directory/default.aspx.

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Research Roundup

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Clinical question: Is low molecular weight heparin (LMWH) safe for deep vein thrombosis (DVT) prophylaxis in patients with severe renal insufficiency?

 

Background: LMWH is avoided in patients with severe renal insufficiency due to concerns about increased bleeding risk caused by delayed renal excretion.

Study design: Multi center, prospective, single-arm trial.

Setting: Several Canadian intensive-care units (ICUs).

Synopsis: 138 critically ill patients with a creatinine clearance lower than 30 mL/min received open-labeled dalteparin, 5,000 IU once daily for DVT prophylaxis for a median duration of seven days. Peak and trough anti-Xa levels were measured to assess for efficacy and evidence of bioaccumulation, respectively. Patients were monitored for DVT development, heparin-induced thrombocytopenia (HIT), and bleeding. None of the patients (0%, CI, 0%-3%) had evidence of bioaccumulation. Seven patients (5.1%) developed DVT and only two patients (1.4%) developed HIT. Of the 10 patients who bled (7.2%), two died.

Of note, 62% of patients in this study had acute renal failure and their improvement in renal function during their ICU stay might have contributed to lack of bioaccumulation. Another limitation is the lack of comparison with other DVT prophylaxis strategies, especially unfractionated heparin.

Bottom line: Using dalteparin for DVT prophylaxis is unlikely to increase bleeding risk in patients with severe renal insufficiency. However, this finding cannot be generalized to dialysis-dependent patients or those with chronic kidney disease.

Citation: Arch Intern Med. 2008;168(16):1805-1812

—Reviewed by Rebecca Allyn, MD, Smitha Chadaga, MD, Mary Dedecker, MD, Vignesh Narayanan, MD, Eugene S. Chu, MD, Division of Hospital Medicine, Denver Health and Hospital Authority

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Clinical question: Is low molecular weight heparin (LMWH) safe for deep vein thrombosis (DVT) prophylaxis in patients with severe renal insufficiency?

 

Background: LMWH is avoided in patients with severe renal insufficiency due to concerns about increased bleeding risk caused by delayed renal excretion.

Study design: Multi center, prospective, single-arm trial.

Setting: Several Canadian intensive-care units (ICUs).

Synopsis: 138 critically ill patients with a creatinine clearance lower than 30 mL/min received open-labeled dalteparin, 5,000 IU once daily for DVT prophylaxis for a median duration of seven days. Peak and trough anti-Xa levels were measured to assess for efficacy and evidence of bioaccumulation, respectively. Patients were monitored for DVT development, heparin-induced thrombocytopenia (HIT), and bleeding. None of the patients (0%, CI, 0%-3%) had evidence of bioaccumulation. Seven patients (5.1%) developed DVT and only two patients (1.4%) developed HIT. Of the 10 patients who bled (7.2%), two died.

Of note, 62% of patients in this study had acute renal failure and their improvement in renal function during their ICU stay might have contributed to lack of bioaccumulation. Another limitation is the lack of comparison with other DVT prophylaxis strategies, especially unfractionated heparin.

Bottom line: Using dalteparin for DVT prophylaxis is unlikely to increase bleeding risk in patients with severe renal insufficiency. However, this finding cannot be generalized to dialysis-dependent patients or those with chronic kidney disease.

Citation: Arch Intern Med. 2008;168(16):1805-1812

—Reviewed by Rebecca Allyn, MD, Smitha Chadaga, MD, Mary Dedecker, MD, Vignesh Narayanan, MD, Eugene S. Chu, MD, Division of Hospital Medicine, Denver Health and Hospital Authority

Clinical question: Is low molecular weight heparin (LMWH) safe for deep vein thrombosis (DVT) prophylaxis in patients with severe renal insufficiency?

 

Background: LMWH is avoided in patients with severe renal insufficiency due to concerns about increased bleeding risk caused by delayed renal excretion.

Study design: Multi center, prospective, single-arm trial.

Setting: Several Canadian intensive-care units (ICUs).

Synopsis: 138 critically ill patients with a creatinine clearance lower than 30 mL/min received open-labeled dalteparin, 5,000 IU once daily for DVT prophylaxis for a median duration of seven days. Peak and trough anti-Xa levels were measured to assess for efficacy and evidence of bioaccumulation, respectively. Patients were monitored for DVT development, heparin-induced thrombocytopenia (HIT), and bleeding. None of the patients (0%, CI, 0%-3%) had evidence of bioaccumulation. Seven patients (5.1%) developed DVT and only two patients (1.4%) developed HIT. Of the 10 patients who bled (7.2%), two died.

Of note, 62% of patients in this study had acute renal failure and their improvement in renal function during their ICU stay might have contributed to lack of bioaccumulation. Another limitation is the lack of comparison with other DVT prophylaxis strategies, especially unfractionated heparin.

Bottom line: Using dalteparin for DVT prophylaxis is unlikely to increase bleeding risk in patients with severe renal insufficiency. However, this finding cannot be generalized to dialysis-dependent patients or those with chronic kidney disease.

Citation: Arch Intern Med. 2008;168(16):1805-1812

—Reviewed by Rebecca Allyn, MD, Smitha Chadaga, MD, Mary Dedecker, MD, Vignesh Narayanan, MD, Eugene S. Chu, MD, Division of Hospital Medicine, Denver Health and Hospital Authority

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Mismatched Cord Blood

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Dr. Mary Eapen reports findings that mismatched umbilical cord blood is as effective as mismatched bone marrow or peripheral blood in allogeneic stem cell transplantation in adults with acute leukemia.

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Dr. Mary Eapen reports findings that mismatched umbilical cord blood is as effective as mismatched bone marrow or peripheral blood in allogeneic stem cell transplantation in adults with acute leukemia.

Dr. Mary Eapen reports findings that mismatched umbilical cord blood is as effective as mismatched bone marrow or peripheral blood in allogeneic stem cell transplantation in adults with acute leukemia.

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Prepare to react to a medical malpractice lawsuit

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Prepare to react to a medical malpractice lawsuit

One of my colleagues is being sued for medical malpractice. After he was notified of the lawsuit, his lawyer immediately advised him of some things he should and should not do. Can you help me understand what those might be?

J. Boggs, Fort Smith, Ark.

ASK Dr. hospitalist

Do you have a problem or concern that you’d like Dr. Hospitalist to address? E-mail your questions to drhospit@wiley.com.

Dr. Hospitalist responds: Lawsuits alleging medical malpractice are common—probably more common than most of us realize. Most physicians either know someone who has been sued or is currently being sued for medical malpractice. Unfortunately, malpractice is not something most medical schools or postgraduate training programs address sufficiently as part of a curriculum.

To be clear, I am not an attorney. I advise you to speak with an attorney familiar with medical malpractice statutes in your state regarding any legal action. But in my discussions with medical malpractice lawyers and with physicians who have been sued, I have come to understand the process can be an emotional and lengthy experience for everyone involved.

Attorneys often advise medical malpractice defendants of several things:

  • Immediately contact your risk manager. If you do not know who your risk manager is or do not have one, contact your medical malpractice carrier. Your risk manager will open a file and notify your medical malpractice carrier. They will ask an attorney to contact you. This attorney will advise you of the most appropriate steps to take.
  • It is normal to feel a wide range of emotions when you learn you are being sued. You might be very angry with the plaintiff (the party filing the suit), especially if you believe you provided appropriate care. At times like this, many physicians want to vent and speak with friends, family, colleagues, or even the plaintiff. That leads to a second piece of advice: Don’t speak with ANYONE except your attorney about the case. This might be difficult and seem counterintuitive, but you can harm your case if you talk to others about it. As the lawsuit evolves, both parties will undergo a discovery process to learn everyone’s perspectives. Defendants likely will be asked to provide answers at a deposition. Plaintiff’s counsel will ask defendants whether they spoke with anyone about the case. If they learn defendants spoke with others about the case, they may depose those individuals to learn what was discussed in those private conversations with the defendants. As you can imagine, it can get messy when private conversations are revealed in depositions.
  • The last piece of advice attorneys often given new clients: Never alter the record. This seems crazy, right? What intelligent doctor would actually go back and alter the record? Well, silly as it might sound, many providers do go back and change something in the record. Most of the time, the change was something the physician thought was innocuous, such as adding a date to a note. By the time a physician has been sued, the medical record in question probably has been copied, likely more than once. It’s fairly simple to recognize when the original record has been altered. But regardless of how innocent any change may seem, the perception is the alteration was meant to deceive. Jurors normally do not view such instances favorably. Defendants can only hurt themselves when they alter the record.

So, if you are ever a defendant in a case, contact your risk manager immediately, don’t talk to anyone about the case, and stay away from the medical records office.

 

 

Take extra precautions to prevent C. difficile infections

I work at a hospital where the infection control officer advocates universal use of alcohol-based hand gels to prevent transmission of infectious pathogens. I previously had been told alcohol-based gels might be insufficient to kill C. difficile. Is this true?

C. Nelson, Atlanta

Dr. Hospitalist responds: You bring up an important question. The role of hand hygiene as a measure to control hospital-acquired infections has become increasingly visible. This is long overdue. The thought of healthcare providers transmitting diseases because they didn’t clean their hands is abhorrent.

Many institutions around the country have adopted policies similar to your hospital’s, encouraging the use of alcohol-based hand gels over the use of soap and water. Hospitals have done this for several reasons:

  • Healthcare providers are more likely to use alcohol-based gels than cleanse with soap and water;
  • Rubbing your hands with gel takes less time than washing with soap and water;
  • Hospitals can place gel dispensers in convenient locations outside each doorway, whereas there are only so many faucets and sinks on any given floor; and
  • Even those who do wash their hands with soap and water often do not spend enough time adequately cleaning them.

Alcohol-based gels are effective against a wide range of bacteria that cause hospital-acquired infections, particularly against Staph, including MRSA. But C. difficile may be different. The control of C. difficile in hospitals is difficult because the organism can produce highly resistant spores, which can survive for long periods of time in a hospital environment, such as in mattresses, equipment, furniture, etc. Alcohol-based gels might be less effective against C. difficile spores than other organisms that cause healthcare-associated infections. Providers caring for patients with C. difficile should wear protective clothing, such as gloves and gowns, as well as clean their hands with soap and water.

For additional information on this subject, I suggest you check out Morbidity and Mortality Weekly Report’s “Guideline for Hand Hygiene in Health-Care Settings” (Oct. 25, 2002). You can access it online at the Infectious Disease Society of America’s Web site at www.idsociety.org/ content.aspx?id=4434#hh. TH

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Prepare to react to a medical malpractice lawsuit

One of my colleagues is being sued for medical malpractice. After he was notified of the lawsuit, his lawyer immediately advised him of some things he should and should not do. Can you help me understand what those might be?

J. Boggs, Fort Smith, Ark.

ASK Dr. hospitalist

Do you have a problem or concern that you’d like Dr. Hospitalist to address? E-mail your questions to drhospit@wiley.com.

Dr. Hospitalist responds: Lawsuits alleging medical malpractice are common—probably more common than most of us realize. Most physicians either know someone who has been sued or is currently being sued for medical malpractice. Unfortunately, malpractice is not something most medical schools or postgraduate training programs address sufficiently as part of a curriculum.

To be clear, I am not an attorney. I advise you to speak with an attorney familiar with medical malpractice statutes in your state regarding any legal action. But in my discussions with medical malpractice lawyers and with physicians who have been sued, I have come to understand the process can be an emotional and lengthy experience for everyone involved.

Attorneys often advise medical malpractice defendants of several things:

  • Immediately contact your risk manager. If you do not know who your risk manager is or do not have one, contact your medical malpractice carrier. Your risk manager will open a file and notify your medical malpractice carrier. They will ask an attorney to contact you. This attorney will advise you of the most appropriate steps to take.
  • It is normal to feel a wide range of emotions when you learn you are being sued. You might be very angry with the plaintiff (the party filing the suit), especially if you believe you provided appropriate care. At times like this, many physicians want to vent and speak with friends, family, colleagues, or even the plaintiff. That leads to a second piece of advice: Don’t speak with ANYONE except your attorney about the case. This might be difficult and seem counterintuitive, but you can harm your case if you talk to others about it. As the lawsuit evolves, both parties will undergo a discovery process to learn everyone’s perspectives. Defendants likely will be asked to provide answers at a deposition. Plaintiff’s counsel will ask defendants whether they spoke with anyone about the case. If they learn defendants spoke with others about the case, they may depose those individuals to learn what was discussed in those private conversations with the defendants. As you can imagine, it can get messy when private conversations are revealed in depositions.
  • The last piece of advice attorneys often given new clients: Never alter the record. This seems crazy, right? What intelligent doctor would actually go back and alter the record? Well, silly as it might sound, many providers do go back and change something in the record. Most of the time, the change was something the physician thought was innocuous, such as adding a date to a note. By the time a physician has been sued, the medical record in question probably has been copied, likely more than once. It’s fairly simple to recognize when the original record has been altered. But regardless of how innocent any change may seem, the perception is the alteration was meant to deceive. Jurors normally do not view such instances favorably. Defendants can only hurt themselves when they alter the record.

So, if you are ever a defendant in a case, contact your risk manager immediately, don’t talk to anyone about the case, and stay away from the medical records office.

 

 

Take extra precautions to prevent C. difficile infections

I work at a hospital where the infection control officer advocates universal use of alcohol-based hand gels to prevent transmission of infectious pathogens. I previously had been told alcohol-based gels might be insufficient to kill C. difficile. Is this true?

C. Nelson, Atlanta

Dr. Hospitalist responds: You bring up an important question. The role of hand hygiene as a measure to control hospital-acquired infections has become increasingly visible. This is long overdue. The thought of healthcare providers transmitting diseases because they didn’t clean their hands is abhorrent.

Many institutions around the country have adopted policies similar to your hospital’s, encouraging the use of alcohol-based hand gels over the use of soap and water. Hospitals have done this for several reasons:

  • Healthcare providers are more likely to use alcohol-based gels than cleanse with soap and water;
  • Rubbing your hands with gel takes less time than washing with soap and water;
  • Hospitals can place gel dispensers in convenient locations outside each doorway, whereas there are only so many faucets and sinks on any given floor; and
  • Even those who do wash their hands with soap and water often do not spend enough time adequately cleaning them.

Alcohol-based gels are effective against a wide range of bacteria that cause hospital-acquired infections, particularly against Staph, including MRSA. But C. difficile may be different. The control of C. difficile in hospitals is difficult because the organism can produce highly resistant spores, which can survive for long periods of time in a hospital environment, such as in mattresses, equipment, furniture, etc. Alcohol-based gels might be less effective against C. difficile spores than other organisms that cause healthcare-associated infections. Providers caring for patients with C. difficile should wear protective clothing, such as gloves and gowns, as well as clean their hands with soap and water.

For additional information on this subject, I suggest you check out Morbidity and Mortality Weekly Report’s “Guideline for Hand Hygiene in Health-Care Settings” (Oct. 25, 2002). You can access it online at the Infectious Disease Society of America’s Web site at www.idsociety.org/ content.aspx?id=4434#hh. TH

Prepare to react to a medical malpractice lawsuit

One of my colleagues is being sued for medical malpractice. After he was notified of the lawsuit, his lawyer immediately advised him of some things he should and should not do. Can you help me understand what those might be?

J. Boggs, Fort Smith, Ark.

ASK Dr. hospitalist

Do you have a problem or concern that you’d like Dr. Hospitalist to address? E-mail your questions to drhospit@wiley.com.

Dr. Hospitalist responds: Lawsuits alleging medical malpractice are common—probably more common than most of us realize. Most physicians either know someone who has been sued or is currently being sued for medical malpractice. Unfortunately, malpractice is not something most medical schools or postgraduate training programs address sufficiently as part of a curriculum.

To be clear, I am not an attorney. I advise you to speak with an attorney familiar with medical malpractice statutes in your state regarding any legal action. But in my discussions with medical malpractice lawyers and with physicians who have been sued, I have come to understand the process can be an emotional and lengthy experience for everyone involved.

Attorneys often advise medical malpractice defendants of several things:

  • Immediately contact your risk manager. If you do not know who your risk manager is or do not have one, contact your medical malpractice carrier. Your risk manager will open a file and notify your medical malpractice carrier. They will ask an attorney to contact you. This attorney will advise you of the most appropriate steps to take.
  • It is normal to feel a wide range of emotions when you learn you are being sued. You might be very angry with the plaintiff (the party filing the suit), especially if you believe you provided appropriate care. At times like this, many physicians want to vent and speak with friends, family, colleagues, or even the plaintiff. That leads to a second piece of advice: Don’t speak with ANYONE except your attorney about the case. This might be difficult and seem counterintuitive, but you can harm your case if you talk to others about it. As the lawsuit evolves, both parties will undergo a discovery process to learn everyone’s perspectives. Defendants likely will be asked to provide answers at a deposition. Plaintiff’s counsel will ask defendants whether they spoke with anyone about the case. If they learn defendants spoke with others about the case, they may depose those individuals to learn what was discussed in those private conversations with the defendants. As you can imagine, it can get messy when private conversations are revealed in depositions.
  • The last piece of advice attorneys often given new clients: Never alter the record. This seems crazy, right? What intelligent doctor would actually go back and alter the record? Well, silly as it might sound, many providers do go back and change something in the record. Most of the time, the change was something the physician thought was innocuous, such as adding a date to a note. By the time a physician has been sued, the medical record in question probably has been copied, likely more than once. It’s fairly simple to recognize when the original record has been altered. But regardless of how innocent any change may seem, the perception is the alteration was meant to deceive. Jurors normally do not view such instances favorably. Defendants can only hurt themselves when they alter the record.

So, if you are ever a defendant in a case, contact your risk manager immediately, don’t talk to anyone about the case, and stay away from the medical records office.

 

 

Take extra precautions to prevent C. difficile infections

I work at a hospital where the infection control officer advocates universal use of alcohol-based hand gels to prevent transmission of infectious pathogens. I previously had been told alcohol-based gels might be insufficient to kill C. difficile. Is this true?

C. Nelson, Atlanta

Dr. Hospitalist responds: You bring up an important question. The role of hand hygiene as a measure to control hospital-acquired infections has become increasingly visible. This is long overdue. The thought of healthcare providers transmitting diseases because they didn’t clean their hands is abhorrent.

Many institutions around the country have adopted policies similar to your hospital’s, encouraging the use of alcohol-based hand gels over the use of soap and water. Hospitals have done this for several reasons:

  • Healthcare providers are more likely to use alcohol-based gels than cleanse with soap and water;
  • Rubbing your hands with gel takes less time than washing with soap and water;
  • Hospitals can place gel dispensers in convenient locations outside each doorway, whereas there are only so many faucets and sinks on any given floor; and
  • Even those who do wash their hands with soap and water often do not spend enough time adequately cleaning them.

Alcohol-based gels are effective against a wide range of bacteria that cause hospital-acquired infections, particularly against Staph, including MRSA. But C. difficile may be different. The control of C. difficile in hospitals is difficult because the organism can produce highly resistant spores, which can survive for long periods of time in a hospital environment, such as in mattresses, equipment, furniture, etc. Alcohol-based gels might be less effective against C. difficile spores than other organisms that cause healthcare-associated infections. Providers caring for patients with C. difficile should wear protective clothing, such as gloves and gowns, as well as clean their hands with soap and water.

For additional information on this subject, I suggest you check out Morbidity and Mortality Weekly Report’s “Guideline for Hand Hygiene in Health-Care Settings” (Oct. 25, 2002). You can access it online at the Infectious Disease Society of America’s Web site at www.idsociety.org/ content.aspx?id=4434#hh. TH

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Bonus-Pay Bonanza

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Although there is a lot of debate about the effectiveness of pay-for-performance (P4P) plans, I think the plans are only going to increase in the foreseeable future.

We need more research to tell us the relative impact of public reporting of performance data and P4P programs. Most importantly, the details of how these plans are set up, how and what they measure, and the dollar amount involved will have everything to do with whether they are successful in improving the value of care we provide.

SHM’S Practice Management Committee conducted a mini-survey of hospitalist group leaders in 2006. Here are some of the key findings.

Performance thresholds should be set so hospitalists need to change their practices to achieve them, but not so far out of reach that hospitalists give up on them.

P4P Prevalence

Forty-one percent (60 out of 146) of hospital medicine group (HMG) leaders reported their groups have a quality-incentive program. Of those HMG leaders more likely to report participation in a quality-incentive program:

  • 60% were at hospitals participating in a P4P program;
  • 50% were at multispecialty/PCP medical groups; and
  • 50% were in the Southern region.

Of those HMG leaders less likely to report participation in P4P programs, 28% were at academic programs and 31% were at local hospitalist-only groups.

Group vs. Individual Incentives

Of the HMG leaders participating in a quality-incentive program:

  • 43% reported it was an individual incentive;
  • 35% reported it was a group incentive;
  • 10% reported the plan had elements of both individual and group incentives; and
  • 12% were not sure if their plans had individual or group incentives.

Basis of Quality Targets

Of the HMG leaders reporting that they participate in a quality-incentive program (respondents could indicate one or more answers):

  • 60% of the programs have targets based on national benchmarks;
  • 23% have targets based on local or regional benchmarks;
  • 37% have targets based on their hospital’s previous experience; and
  • 47% have targets based on improvement over a baseline.

Maximum Impact of Incentives

Of the HMG leaders reporting that they participate in a quality-incentive program:

  • 16% report the maximum impact is less than 3%;
  • 24% report the maximum impact is from 3% to 7%;
  • 35% report the maximum impact is from 8% to 10%;
  • 17% report the maximum impact is from 11% to 20%;
  • 3% report the maximum impact is more than 20%; and
  • 5% report they do not know the maximum impact.

Group vs. Individual Incentives

Of the HMG leaders reporting that they participate in a quality-incentive program:

  • 61% said they have received an incentive payment;
  • 37% have not received an incentive payment; and
  • 2% were unsure if they have received an incentive payment.

Quality Metrics

The most common metrics used in P4P programs, based on 29 responses to the SHM survey: 

  • 93% of HM programs have metrics based on The Joint Commission’s (JCAHO) heart failure measures;
  • 86% have metrics based on JCAHO pneumonia measures;
  • 79% have metrics based on JCAHO myocardial infarction measures;
  • 28% have metrics based on a measure of medication reconciliation;
  • 24% have metrics based on avoidance of unapproved abbreviations;
  • 24% have metrics based on 100,000 Lives Campaign measures;
  • 21% have metrics based on patient satisfaction measures;
  • 17% have metrics based on transitions-of-care measures;
  • 10% have metrics based on throughput measures;
  • 7% have metrics based on end-of-life measures;
  • 7% have metrics based on “good citizenship” measures;
  • 7% have metrics based on mortality rate measures; and
  • 7% have metrics based on readmission rate measures.
 

 

The most common metrics used in quality-incentive programs, based on 45 responses to SHM’s survey: 

  • 73% of programs use JCAHO heart failure measures;
  • 73% use “good citizenship” measures;
  • 73% use patient satisfaction measures;
  • 67% use JCAHO pneumonia measures;
  • 51% use transitions-of-care measures;
  • 44% use JCAHO M.I. measures;
  • 31% use throughput measures;
  • 27% use avoidance of unapproved abbreviations;
  • 24% use a measure based on medication reconciliation;
  • 11% use 100,000 Lives Campaign measures;
  • 9% use readmission rate measures;
  • 7% use mortality rate measures; and
  • 2% use end-of-life measures.

Recommendations

The prevalence of hospitalist quality-based compensation plans is continuing to grow rapidly, but the details of the plans’ structure will govern whether they benefit our patients, improve the overall value of the care we provide, and serve as a meaningful component of our compensation. I suggest each practice consider implementing plans with the following attributes:

A total dollar amount available for performance that is enough to influence hospitalist behavior. I think quality incentives should compose as much as 15% to 20% of a hospitalist’s annual income. Plans connecting quality performance to equal to or less than 7% of annual compensation (the case for 40% of groups in the above survey) rarely are effective.

Money vs. metrics. It usually is better to establish a plan based on a sliding scale of improved performance rather than a single threshold. For example, if all of the bonus money is available for a 10% improvement in performance, consider providing 10% of the total available money for each 1% improvement in performance.

Degree of difficulty. Performance thresholds should be set so that hospitalists need to change their practices to achieve them, but not so far out of reach that hospitalists give up on them. This can get tricky. Many practices set thresholds that are very easy to reach (e.g., they may be near the current level of performance).

Metrics for which trusted data is readily available. In most cases, this means using data already being collected. Avoid hard-to-track metrics, as they are likely to lead to disagreements about their accuracy.

Group vs. individual measures. Most performance metrics can’t be clearly attributed to one hospitalist as compared to another. For example, who gets the credit or blame for Ms. Smith getting or not getting a pneumovax? The majority of performance metrics are best measured and paid on a group basis. Some metrics, such as documenting medicine reconciliation on admission and discharge, can be effectively attributed to a single hospitalist and could be paid on an individual basis.

Small number of metrics, A meaningfully large amount of money should be connected to each one. Don’t make the mistake of having a $10,000 per doctor annual quality bonus pool divided among 20 metrics (each metric would pay a maximum of $500 per year).

Rotating metrics. Consider an annual meeting with members of your hospital’s administration to jointly establish the metrics used in the hospitalist quality incentive for that year. It is reasonable to change the metrics periodically.

It seems to me P4P programs are in their infancy, and will continue to evolve rapidly. Plans that fail to improve outcomes enough to justify the complexity of implementing, tracking, and paying for them will disappear slowly. (I wonder if payment for pneumovax administration during the hospital stay will be in this category.) And new, more effective, and more valuable programs will be developed.

 

 

Hospitalist practices will need to be nimble to keep pace with all of this change. Although SHM can alert you to how new P4P initiatives might affect your practice, and even recommend methods to improve your performance, you and your hospitalist colleagues still will have a lot of work to operationalize these programs in your practice. TH

Dr. Nelson has been a practicing hospitalist since 1988 and is co-founder and past president of SHM. He is a principal in Nelson/Flores Associates, a national hospitalist practice management consulting firm. He is part of the faculty for SHM’s “Best Practices in Managing a Hospital Medicine Program” course. This column represents his views and is not intended to reflect an official position of SHM.

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The Hospitalist - 2009(02)
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Although there is a lot of debate about the effectiveness of pay-for-performance (P4P) plans, I think the plans are only going to increase in the foreseeable future.

We need more research to tell us the relative impact of public reporting of performance data and P4P programs. Most importantly, the details of how these plans are set up, how and what they measure, and the dollar amount involved will have everything to do with whether they are successful in improving the value of care we provide.

SHM’S Practice Management Committee conducted a mini-survey of hospitalist group leaders in 2006. Here are some of the key findings.

Performance thresholds should be set so hospitalists need to change their practices to achieve them, but not so far out of reach that hospitalists give up on them.

P4P Prevalence

Forty-one percent (60 out of 146) of hospital medicine group (HMG) leaders reported their groups have a quality-incentive program. Of those HMG leaders more likely to report participation in a quality-incentive program:

  • 60% were at hospitals participating in a P4P program;
  • 50% were at multispecialty/PCP medical groups; and
  • 50% were in the Southern region.

Of those HMG leaders less likely to report participation in P4P programs, 28% were at academic programs and 31% were at local hospitalist-only groups.

Group vs. Individual Incentives

Of the HMG leaders participating in a quality-incentive program:

  • 43% reported it was an individual incentive;
  • 35% reported it was a group incentive;
  • 10% reported the plan had elements of both individual and group incentives; and
  • 12% were not sure if their plans had individual or group incentives.

Basis of Quality Targets

Of the HMG leaders reporting that they participate in a quality-incentive program (respondents could indicate one or more answers):

  • 60% of the programs have targets based on national benchmarks;
  • 23% have targets based on local or regional benchmarks;
  • 37% have targets based on their hospital’s previous experience; and
  • 47% have targets based on improvement over a baseline.

Maximum Impact of Incentives

Of the HMG leaders reporting that they participate in a quality-incentive program:

  • 16% report the maximum impact is less than 3%;
  • 24% report the maximum impact is from 3% to 7%;
  • 35% report the maximum impact is from 8% to 10%;
  • 17% report the maximum impact is from 11% to 20%;
  • 3% report the maximum impact is more than 20%; and
  • 5% report they do not know the maximum impact.

Group vs. Individual Incentives

Of the HMG leaders reporting that they participate in a quality-incentive program:

  • 61% said they have received an incentive payment;
  • 37% have not received an incentive payment; and
  • 2% were unsure if they have received an incentive payment.

Quality Metrics

The most common metrics used in P4P programs, based on 29 responses to the SHM survey: 

  • 93% of HM programs have metrics based on The Joint Commission’s (JCAHO) heart failure measures;
  • 86% have metrics based on JCAHO pneumonia measures;
  • 79% have metrics based on JCAHO myocardial infarction measures;
  • 28% have metrics based on a measure of medication reconciliation;
  • 24% have metrics based on avoidance of unapproved abbreviations;
  • 24% have metrics based on 100,000 Lives Campaign measures;
  • 21% have metrics based on patient satisfaction measures;
  • 17% have metrics based on transitions-of-care measures;
  • 10% have metrics based on throughput measures;
  • 7% have metrics based on end-of-life measures;
  • 7% have metrics based on “good citizenship” measures;
  • 7% have metrics based on mortality rate measures; and
  • 7% have metrics based on readmission rate measures.
 

 

The most common metrics used in quality-incentive programs, based on 45 responses to SHM’s survey: 

  • 73% of programs use JCAHO heart failure measures;
  • 73% use “good citizenship” measures;
  • 73% use patient satisfaction measures;
  • 67% use JCAHO pneumonia measures;
  • 51% use transitions-of-care measures;
  • 44% use JCAHO M.I. measures;
  • 31% use throughput measures;
  • 27% use avoidance of unapproved abbreviations;
  • 24% use a measure based on medication reconciliation;
  • 11% use 100,000 Lives Campaign measures;
  • 9% use readmission rate measures;
  • 7% use mortality rate measures; and
  • 2% use end-of-life measures.

Recommendations

The prevalence of hospitalist quality-based compensation plans is continuing to grow rapidly, but the details of the plans’ structure will govern whether they benefit our patients, improve the overall value of the care we provide, and serve as a meaningful component of our compensation. I suggest each practice consider implementing plans with the following attributes:

A total dollar amount available for performance that is enough to influence hospitalist behavior. I think quality incentives should compose as much as 15% to 20% of a hospitalist’s annual income. Plans connecting quality performance to equal to or less than 7% of annual compensation (the case for 40% of groups in the above survey) rarely are effective.

Money vs. metrics. It usually is better to establish a plan based on a sliding scale of improved performance rather than a single threshold. For example, if all of the bonus money is available for a 10% improvement in performance, consider providing 10% of the total available money for each 1% improvement in performance.

Degree of difficulty. Performance thresholds should be set so that hospitalists need to change their practices to achieve them, but not so far out of reach that hospitalists give up on them. This can get tricky. Many practices set thresholds that are very easy to reach (e.g., they may be near the current level of performance).

Metrics for which trusted data is readily available. In most cases, this means using data already being collected. Avoid hard-to-track metrics, as they are likely to lead to disagreements about their accuracy.

Group vs. individual measures. Most performance metrics can’t be clearly attributed to one hospitalist as compared to another. For example, who gets the credit or blame for Ms. Smith getting or not getting a pneumovax? The majority of performance metrics are best measured and paid on a group basis. Some metrics, such as documenting medicine reconciliation on admission and discharge, can be effectively attributed to a single hospitalist and could be paid on an individual basis.

Small number of metrics, A meaningfully large amount of money should be connected to each one. Don’t make the mistake of having a $10,000 per doctor annual quality bonus pool divided among 20 metrics (each metric would pay a maximum of $500 per year).

Rotating metrics. Consider an annual meeting with members of your hospital’s administration to jointly establish the metrics used in the hospitalist quality incentive for that year. It is reasonable to change the metrics periodically.

It seems to me P4P programs are in their infancy, and will continue to evolve rapidly. Plans that fail to improve outcomes enough to justify the complexity of implementing, tracking, and paying for them will disappear slowly. (I wonder if payment for pneumovax administration during the hospital stay will be in this category.) And new, more effective, and more valuable programs will be developed.

 

 

Hospitalist practices will need to be nimble to keep pace with all of this change. Although SHM can alert you to how new P4P initiatives might affect your practice, and even recommend methods to improve your performance, you and your hospitalist colleagues still will have a lot of work to operationalize these programs in your practice. TH

Dr. Nelson has been a practicing hospitalist since 1988 and is co-founder and past president of SHM. He is a principal in Nelson/Flores Associates, a national hospitalist practice management consulting firm. He is part of the faculty for SHM’s “Best Practices in Managing a Hospital Medicine Program” course. This column represents his views and is not intended to reflect an official position of SHM.

Although there is a lot of debate about the effectiveness of pay-for-performance (P4P) plans, I think the plans are only going to increase in the foreseeable future.

We need more research to tell us the relative impact of public reporting of performance data and P4P programs. Most importantly, the details of how these plans are set up, how and what they measure, and the dollar amount involved will have everything to do with whether they are successful in improving the value of care we provide.

SHM’S Practice Management Committee conducted a mini-survey of hospitalist group leaders in 2006. Here are some of the key findings.

Performance thresholds should be set so hospitalists need to change their practices to achieve them, but not so far out of reach that hospitalists give up on them.

P4P Prevalence

Forty-one percent (60 out of 146) of hospital medicine group (HMG) leaders reported their groups have a quality-incentive program. Of those HMG leaders more likely to report participation in a quality-incentive program:

  • 60% were at hospitals participating in a P4P program;
  • 50% were at multispecialty/PCP medical groups; and
  • 50% were in the Southern region.

Of those HMG leaders less likely to report participation in P4P programs, 28% were at academic programs and 31% were at local hospitalist-only groups.

Group vs. Individual Incentives

Of the HMG leaders participating in a quality-incentive program:

  • 43% reported it was an individual incentive;
  • 35% reported it was a group incentive;
  • 10% reported the plan had elements of both individual and group incentives; and
  • 12% were not sure if their plans had individual or group incentives.

Basis of Quality Targets

Of the HMG leaders reporting that they participate in a quality-incentive program (respondents could indicate one or more answers):

  • 60% of the programs have targets based on national benchmarks;
  • 23% have targets based on local or regional benchmarks;
  • 37% have targets based on their hospital’s previous experience; and
  • 47% have targets based on improvement over a baseline.

Maximum Impact of Incentives

Of the HMG leaders reporting that they participate in a quality-incentive program:

  • 16% report the maximum impact is less than 3%;
  • 24% report the maximum impact is from 3% to 7%;
  • 35% report the maximum impact is from 8% to 10%;
  • 17% report the maximum impact is from 11% to 20%;
  • 3% report the maximum impact is more than 20%; and
  • 5% report they do not know the maximum impact.

Group vs. Individual Incentives

Of the HMG leaders reporting that they participate in a quality-incentive program:

  • 61% said they have received an incentive payment;
  • 37% have not received an incentive payment; and
  • 2% were unsure if they have received an incentive payment.

Quality Metrics

The most common metrics used in P4P programs, based on 29 responses to the SHM survey: 

  • 93% of HM programs have metrics based on The Joint Commission’s (JCAHO) heart failure measures;
  • 86% have metrics based on JCAHO pneumonia measures;
  • 79% have metrics based on JCAHO myocardial infarction measures;
  • 28% have metrics based on a measure of medication reconciliation;
  • 24% have metrics based on avoidance of unapproved abbreviations;
  • 24% have metrics based on 100,000 Lives Campaign measures;
  • 21% have metrics based on patient satisfaction measures;
  • 17% have metrics based on transitions-of-care measures;
  • 10% have metrics based on throughput measures;
  • 7% have metrics based on end-of-life measures;
  • 7% have metrics based on “good citizenship” measures;
  • 7% have metrics based on mortality rate measures; and
  • 7% have metrics based on readmission rate measures.
 

 

The most common metrics used in quality-incentive programs, based on 45 responses to SHM’s survey: 

  • 73% of programs use JCAHO heart failure measures;
  • 73% use “good citizenship” measures;
  • 73% use patient satisfaction measures;
  • 67% use JCAHO pneumonia measures;
  • 51% use transitions-of-care measures;
  • 44% use JCAHO M.I. measures;
  • 31% use throughput measures;
  • 27% use avoidance of unapproved abbreviations;
  • 24% use a measure based on medication reconciliation;
  • 11% use 100,000 Lives Campaign measures;
  • 9% use readmission rate measures;
  • 7% use mortality rate measures; and
  • 2% use end-of-life measures.

Recommendations

The prevalence of hospitalist quality-based compensation plans is continuing to grow rapidly, but the details of the plans’ structure will govern whether they benefit our patients, improve the overall value of the care we provide, and serve as a meaningful component of our compensation. I suggest each practice consider implementing plans with the following attributes:

A total dollar amount available for performance that is enough to influence hospitalist behavior. I think quality incentives should compose as much as 15% to 20% of a hospitalist’s annual income. Plans connecting quality performance to equal to or less than 7% of annual compensation (the case for 40% of groups in the above survey) rarely are effective.

Money vs. metrics. It usually is better to establish a plan based on a sliding scale of improved performance rather than a single threshold. For example, if all of the bonus money is available for a 10% improvement in performance, consider providing 10% of the total available money for each 1% improvement in performance.

Degree of difficulty. Performance thresholds should be set so that hospitalists need to change their practices to achieve them, but not so far out of reach that hospitalists give up on them. This can get tricky. Many practices set thresholds that are very easy to reach (e.g., they may be near the current level of performance).

Metrics for which trusted data is readily available. In most cases, this means using data already being collected. Avoid hard-to-track metrics, as they are likely to lead to disagreements about their accuracy.

Group vs. individual measures. Most performance metrics can’t be clearly attributed to one hospitalist as compared to another. For example, who gets the credit or blame for Ms. Smith getting or not getting a pneumovax? The majority of performance metrics are best measured and paid on a group basis. Some metrics, such as documenting medicine reconciliation on admission and discharge, can be effectively attributed to a single hospitalist and could be paid on an individual basis.

Small number of metrics, A meaningfully large amount of money should be connected to each one. Don’t make the mistake of having a $10,000 per doctor annual quality bonus pool divided among 20 metrics (each metric would pay a maximum of $500 per year).

Rotating metrics. Consider an annual meeting with members of your hospital’s administration to jointly establish the metrics used in the hospitalist quality incentive for that year. It is reasonable to change the metrics periodically.

It seems to me P4P programs are in their infancy, and will continue to evolve rapidly. Plans that fail to improve outcomes enough to justify the complexity of implementing, tracking, and paying for them will disappear slowly. (I wonder if payment for pneumovax administration during the hospital stay will be in this category.) And new, more effective, and more valuable programs will be developed.

 

 

Hospitalist practices will need to be nimble to keep pace with all of this change. Although SHM can alert you to how new P4P initiatives might affect your practice, and even recommend methods to improve your performance, you and your hospitalist colleagues still will have a lot of work to operationalize these programs in your practice. TH

Dr. Nelson has been a practicing hospitalist since 1988 and is co-founder and past president of SHM. He is a principal in Nelson/Flores Associates, a national hospitalist practice management consulting firm. He is part of the faculty for SHM’s “Best Practices in Managing a Hospital Medicine Program” course. This column represents his views and is not intended to reflect an official position of SHM.

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Don’t Be Afraid to Fear Fear

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As the New Year dawns I, like many Americans, am afraid.

Over the past year we’ve experienced a global financial meltdown, a deepening recession, rampant home foreclosures, Humpty Dumpty-like nest eggs, and the once-proud American auto industry gasping for breath. All of this is balanced against the hope that springs from the election of the first black President, who promises “change we can believe in.”

Hospitals are in growing financial straits and those in charge are deeply concerned, if not downright scared. In some ways, this is hard to imagine. Most hospitals reported outstanding, if not record, profits for the 2007 and 2008 fiscal years. However, change is afoot. Hospital admissions are down at nearly a third of hospitals, with a similar number of hospitals reporting declines in lucrative elective procedures. Additionally, recession-induced layoffs have resulted in a sharp rise in the proportion of uncompensated care that, when coupled with mushrooming debt and tighter credit, is propelling many hospitals into the red.

We should not cower from this challenge—rather embrace it; this is our chance to shine. Hospitalists are better positioned, better than any other medical group, to re-engineer the processes of care required to improve the quality of hospital care.

As a result, Moody’s, the credit-rating giant, has reported a rash of hospital credit downgrades. In October and November alone, Moody’s downgraded 18 hospitals and upgraded only one. At the same time, Fitch, another credit agency, downgraded the entire hospital sector from “stable” to “negative.” Although the subject of credit downgrades is somewhat abstract for many practicing physicians, the upshot is that it will be more expensive to finance hospital debt.

Coming on the heels of the largest hospital-building boom in American history, billions of dollars of debt present a very thorny fiscal rose. When this coalesces with less hospital utilization, more nonpaying patients, and potential decreases in federal reimbursement, it represents a financial crunch of catastrophic proportions.

Thus, it is no surprise our hospital administrators are on edge.

Hospitalist Impact

Enter me, the director of a money-losing service line, into an executive-filled room to propose expansion of hospital support for my hospitalist program. With average hospital support running a tad under $100,000 per hospitalist nationally, and overall support of about a million dollars per hospitalist group a year, any request for expansion will be scrutinized with a jaundiced eye.

In my experience, growing our hospitalist group was welcomed when the hospital’s coffers were bulging, the ED was overcrowded, and hospital beds were expanding. Granted, we had robust data showing our presence cut the average length of stay, increased throughput, and improved patient satisfaction—in other words, we paid for ourselves through enhanced efficiencies and cost savings. Still, the current economic realities dictate an unprecedented level of cost-consciousness and fiscal diligence. The result is my negotiations with my hospital administration have intensified, with an increased examination of expansion proposals, infrastructure development, and salary support.

Opportunity Abounds

So what are we—or I, in this case—to do? As I look at the potential of a prolonged recession, I am convinced this situation offers us a profound opportunity. Let’s face it: The hospital medicine boom was born out of opportunity. Early hospitalists took advantage of the opportunity to staff unassigned patients in the ED, backfill the migration of primary care doctors out of the hospital, enhance DRG reimbursements, reduce length of stay, and improve patient, staff, and subspecialist satisfaction because of our ability and willingness to staff inpatients around the clock.

In the coming years, we will again be offered opportunities, although they likely will come disguised as challenges. Some will choose to ignore these challenges in the hope they just go away, preferring instead to fear the unknown. Others will turn this fear into action and prosper. Opportunities will center on our ability to enhance patient outcomes and experiences. As federal dollars dry up and more and more Americans become uninsured or underinsured, hospitals will be pushed to augment the level of service and care they provide.

 

 

On one hand, payors have determined (appropriately so) that they want quality over quantity, and those who can provide superior outcomes will be better reimbursed. With thinning margins, hospitals will look for effector arms to engage the type of process improvement necessary to improve outcomes and, subsequently, revenue. We should not cower from this challenge, rather, embrace it; this is our chance to shine. Hospitalists are better positioned, better than any other medical group, to re-engineer the processes of care required to improve the quality of hospital care.

At the same time, our customers—the patients—likely will be footing more of the bill. As such, this new breed of healthcare consumer will expect a higher level of service than previously delivered. Again, hospitals that can provide five-star service will be better positioned to capture this coveted but ever-shrinking cohort of paying patients. This again positions hospitalists well. In my hospital, our group cares for just over 25% of all hospitalized patients, about 5,500 admissions per year.

Many hospitalist groups have a reach well beyond that, perhaps approaching 75%. Consider the type of bargaining power a hospitalist group could have by systematically showing that your work improves patient satisfaction, retention, and referral.

Measurement Is Crucial

Which brings me to my final point: As the economy tightens further, we will feel a heretofore-unrealized pressure to document our benefit. If we cannot document the fact our work improves processes, reduces length of stay, enhances the quality of patient care, and increases patient satisfaction, then we run the risk of being a glaringly large, negative budgetary line item waiting to be slashed.

With resolutions in the air, I resolve to work closely with my group and our hospital to document our value, prove our worth, do it better. Indubitably, this will meet with resistance, as some will advocate turning a blind eye, afraid of the challenges we might encounter. I, however, am going to choose to embrace these opportunities by fearing the known, rather than the unknown.

I have no doubt an honest assessment of the work we do and the value we provide might be anxiety provoking. It will force us to evaluate our care in ways we fear, measure our outcomes in ways we fear, push ourselves to improve in ways we fear.

In a word, change. TH

Dr. Glasheen is associate professor of medicine at the University of Colorado Denver, where he serves as director of the Hospital Medicine Program and the Hospitalist Training Program, and as associate program director of the Internal Medicine Residency Program.

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The Hospitalist - 2009(02)
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As the New Year dawns I, like many Americans, am afraid.

Over the past year we’ve experienced a global financial meltdown, a deepening recession, rampant home foreclosures, Humpty Dumpty-like nest eggs, and the once-proud American auto industry gasping for breath. All of this is balanced against the hope that springs from the election of the first black President, who promises “change we can believe in.”

Hospitals are in growing financial straits and those in charge are deeply concerned, if not downright scared. In some ways, this is hard to imagine. Most hospitals reported outstanding, if not record, profits for the 2007 and 2008 fiscal years. However, change is afoot. Hospital admissions are down at nearly a third of hospitals, with a similar number of hospitals reporting declines in lucrative elective procedures. Additionally, recession-induced layoffs have resulted in a sharp rise in the proportion of uncompensated care that, when coupled with mushrooming debt and tighter credit, is propelling many hospitals into the red.

We should not cower from this challenge—rather embrace it; this is our chance to shine. Hospitalists are better positioned, better than any other medical group, to re-engineer the processes of care required to improve the quality of hospital care.

As a result, Moody’s, the credit-rating giant, has reported a rash of hospital credit downgrades. In October and November alone, Moody’s downgraded 18 hospitals and upgraded only one. At the same time, Fitch, another credit agency, downgraded the entire hospital sector from “stable” to “negative.” Although the subject of credit downgrades is somewhat abstract for many practicing physicians, the upshot is that it will be more expensive to finance hospital debt.

Coming on the heels of the largest hospital-building boom in American history, billions of dollars of debt present a very thorny fiscal rose. When this coalesces with less hospital utilization, more nonpaying patients, and potential decreases in federal reimbursement, it represents a financial crunch of catastrophic proportions.

Thus, it is no surprise our hospital administrators are on edge.

Hospitalist Impact

Enter me, the director of a money-losing service line, into an executive-filled room to propose expansion of hospital support for my hospitalist program. With average hospital support running a tad under $100,000 per hospitalist nationally, and overall support of about a million dollars per hospitalist group a year, any request for expansion will be scrutinized with a jaundiced eye.

In my experience, growing our hospitalist group was welcomed when the hospital’s coffers were bulging, the ED was overcrowded, and hospital beds were expanding. Granted, we had robust data showing our presence cut the average length of stay, increased throughput, and improved patient satisfaction—in other words, we paid for ourselves through enhanced efficiencies and cost savings. Still, the current economic realities dictate an unprecedented level of cost-consciousness and fiscal diligence. The result is my negotiations with my hospital administration have intensified, with an increased examination of expansion proposals, infrastructure development, and salary support.

Opportunity Abounds

So what are we—or I, in this case—to do? As I look at the potential of a prolonged recession, I am convinced this situation offers us a profound opportunity. Let’s face it: The hospital medicine boom was born out of opportunity. Early hospitalists took advantage of the opportunity to staff unassigned patients in the ED, backfill the migration of primary care doctors out of the hospital, enhance DRG reimbursements, reduce length of stay, and improve patient, staff, and subspecialist satisfaction because of our ability and willingness to staff inpatients around the clock.

In the coming years, we will again be offered opportunities, although they likely will come disguised as challenges. Some will choose to ignore these challenges in the hope they just go away, preferring instead to fear the unknown. Others will turn this fear into action and prosper. Opportunities will center on our ability to enhance patient outcomes and experiences. As federal dollars dry up and more and more Americans become uninsured or underinsured, hospitals will be pushed to augment the level of service and care they provide.

 

 

On one hand, payors have determined (appropriately so) that they want quality over quantity, and those who can provide superior outcomes will be better reimbursed. With thinning margins, hospitals will look for effector arms to engage the type of process improvement necessary to improve outcomes and, subsequently, revenue. We should not cower from this challenge, rather, embrace it; this is our chance to shine. Hospitalists are better positioned, better than any other medical group, to re-engineer the processes of care required to improve the quality of hospital care.

At the same time, our customers—the patients—likely will be footing more of the bill. As such, this new breed of healthcare consumer will expect a higher level of service than previously delivered. Again, hospitals that can provide five-star service will be better positioned to capture this coveted but ever-shrinking cohort of paying patients. This again positions hospitalists well. In my hospital, our group cares for just over 25% of all hospitalized patients, about 5,500 admissions per year.

Many hospitalist groups have a reach well beyond that, perhaps approaching 75%. Consider the type of bargaining power a hospitalist group could have by systematically showing that your work improves patient satisfaction, retention, and referral.

Measurement Is Crucial

Which brings me to my final point: As the economy tightens further, we will feel a heretofore-unrealized pressure to document our benefit. If we cannot document the fact our work improves processes, reduces length of stay, enhances the quality of patient care, and increases patient satisfaction, then we run the risk of being a glaringly large, negative budgetary line item waiting to be slashed.

With resolutions in the air, I resolve to work closely with my group and our hospital to document our value, prove our worth, do it better. Indubitably, this will meet with resistance, as some will advocate turning a blind eye, afraid of the challenges we might encounter. I, however, am going to choose to embrace these opportunities by fearing the known, rather than the unknown.

I have no doubt an honest assessment of the work we do and the value we provide might be anxiety provoking. It will force us to evaluate our care in ways we fear, measure our outcomes in ways we fear, push ourselves to improve in ways we fear.

In a word, change. TH

Dr. Glasheen is associate professor of medicine at the University of Colorado Denver, where he serves as director of the Hospital Medicine Program and the Hospitalist Training Program, and as associate program director of the Internal Medicine Residency Program.

As the New Year dawns I, like many Americans, am afraid.

Over the past year we’ve experienced a global financial meltdown, a deepening recession, rampant home foreclosures, Humpty Dumpty-like nest eggs, and the once-proud American auto industry gasping for breath. All of this is balanced against the hope that springs from the election of the first black President, who promises “change we can believe in.”

Hospitals are in growing financial straits and those in charge are deeply concerned, if not downright scared. In some ways, this is hard to imagine. Most hospitals reported outstanding, if not record, profits for the 2007 and 2008 fiscal years. However, change is afoot. Hospital admissions are down at nearly a third of hospitals, with a similar number of hospitals reporting declines in lucrative elective procedures. Additionally, recession-induced layoffs have resulted in a sharp rise in the proportion of uncompensated care that, when coupled with mushrooming debt and tighter credit, is propelling many hospitals into the red.

We should not cower from this challenge—rather embrace it; this is our chance to shine. Hospitalists are better positioned, better than any other medical group, to re-engineer the processes of care required to improve the quality of hospital care.

As a result, Moody’s, the credit-rating giant, has reported a rash of hospital credit downgrades. In October and November alone, Moody’s downgraded 18 hospitals and upgraded only one. At the same time, Fitch, another credit agency, downgraded the entire hospital sector from “stable” to “negative.” Although the subject of credit downgrades is somewhat abstract for many practicing physicians, the upshot is that it will be more expensive to finance hospital debt.

Coming on the heels of the largest hospital-building boom in American history, billions of dollars of debt present a very thorny fiscal rose. When this coalesces with less hospital utilization, more nonpaying patients, and potential decreases in federal reimbursement, it represents a financial crunch of catastrophic proportions.

Thus, it is no surprise our hospital administrators are on edge.

Hospitalist Impact

Enter me, the director of a money-losing service line, into an executive-filled room to propose expansion of hospital support for my hospitalist program. With average hospital support running a tad under $100,000 per hospitalist nationally, and overall support of about a million dollars per hospitalist group a year, any request for expansion will be scrutinized with a jaundiced eye.

In my experience, growing our hospitalist group was welcomed when the hospital’s coffers were bulging, the ED was overcrowded, and hospital beds were expanding. Granted, we had robust data showing our presence cut the average length of stay, increased throughput, and improved patient satisfaction—in other words, we paid for ourselves through enhanced efficiencies and cost savings. Still, the current economic realities dictate an unprecedented level of cost-consciousness and fiscal diligence. The result is my negotiations with my hospital administration have intensified, with an increased examination of expansion proposals, infrastructure development, and salary support.

Opportunity Abounds

So what are we—or I, in this case—to do? As I look at the potential of a prolonged recession, I am convinced this situation offers us a profound opportunity. Let’s face it: The hospital medicine boom was born out of opportunity. Early hospitalists took advantage of the opportunity to staff unassigned patients in the ED, backfill the migration of primary care doctors out of the hospital, enhance DRG reimbursements, reduce length of stay, and improve patient, staff, and subspecialist satisfaction because of our ability and willingness to staff inpatients around the clock.

In the coming years, we will again be offered opportunities, although they likely will come disguised as challenges. Some will choose to ignore these challenges in the hope they just go away, preferring instead to fear the unknown. Others will turn this fear into action and prosper. Opportunities will center on our ability to enhance patient outcomes and experiences. As federal dollars dry up and more and more Americans become uninsured or underinsured, hospitals will be pushed to augment the level of service and care they provide.

 

 

On one hand, payors have determined (appropriately so) that they want quality over quantity, and those who can provide superior outcomes will be better reimbursed. With thinning margins, hospitals will look for effector arms to engage the type of process improvement necessary to improve outcomes and, subsequently, revenue. We should not cower from this challenge, rather, embrace it; this is our chance to shine. Hospitalists are better positioned, better than any other medical group, to re-engineer the processes of care required to improve the quality of hospital care.

At the same time, our customers—the patients—likely will be footing more of the bill. As such, this new breed of healthcare consumer will expect a higher level of service than previously delivered. Again, hospitals that can provide five-star service will be better positioned to capture this coveted but ever-shrinking cohort of paying patients. This again positions hospitalists well. In my hospital, our group cares for just over 25% of all hospitalized patients, about 5,500 admissions per year.

Many hospitalist groups have a reach well beyond that, perhaps approaching 75%. Consider the type of bargaining power a hospitalist group could have by systematically showing that your work improves patient satisfaction, retention, and referral.

Measurement Is Crucial

Which brings me to my final point: As the economy tightens further, we will feel a heretofore-unrealized pressure to document our benefit. If we cannot document the fact our work improves processes, reduces length of stay, enhances the quality of patient care, and increases patient satisfaction, then we run the risk of being a glaringly large, negative budgetary line item waiting to be slashed.

With resolutions in the air, I resolve to work closely with my group and our hospital to document our value, prove our worth, do it better. Indubitably, this will meet with resistance, as some will advocate turning a blind eye, afraid of the challenges we might encounter. I, however, am going to choose to embrace these opportunities by fearing the known, rather than the unknown.

I have no doubt an honest assessment of the work we do and the value we provide might be anxiety provoking. It will force us to evaluate our care in ways we fear, measure our outcomes in ways we fear, push ourselves to improve in ways we fear.

In a word, change. TH

Dr. Glasheen is associate professor of medicine at the University of Colorado Denver, where he serves as director of the Hospital Medicine Program and the Hospitalist Training Program, and as associate program director of the Internal Medicine Residency Program.

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The Hospitalist - 2009(02)
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