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Enhancing Usability of Health Information Technology: Comparative Evaluation of Workflow Support Tools

Article Type
Changed
Thu, 09/21/2023 - 12:15

BACKGROUND

The Breast and Gynecologic System of Excellence (BGSOE) program has developed a workflow support tool using health information technology to assist clinicians, coordinators and stakeholders in identifying, tracking and supporting Veterans with breast and gynecological cancers. This tool was designed and implemented through a novel process that involved clarifying program aims, defining workflows in process delivery diagrams, and identifying data, analytic products, and user needs. To determine the optimal tool for the program, a comparative usability evaluation was conducted, comparing the new workflow support tool with a previous tool that shared identical aims but utilized a different approach.

METHODS

Usability evaluation employed the System Usability Scale (SUS) and measured acceptance using modified items from a validated instrument used in a national survey of electronic health records. Task efficiency was evaluated based on time taken and the number of clicks required to complete tasks.

RESULTS

Eight healthcare professionals with experience in the BGSOE program or similar programs in the VA participated in the usability evaluation. This group comprised physicians (38%), clinical pharmacist (25%), health care coordinators (25%), and registered nurse (12%). The workflow support tool achieved an impressive SUS score of 89.06, with acceptance scores of 93% (positive statements) and 6% (negative statements), outperforming the standard tool, which scored score of 57.5 on the SUS and had acceptance scores of 53% (positive statements) and 50% (negative statements). In the comparative ranking, 100% of the users preferred the workflow support tool, citing its userfriendliness, intuitiveness, and ease of use. On average, users completed all tasks using the workflow support tool in 8 minutes with 31 clicks, while the standard tool required 18 minutes and 124 clicks.

CONCLUSIONS

The adoption of a workflow support tool in the design of health information technology interventions leads to improved usability, efficiency, and adoption. Based on the positive results from the usability evaluation, the BGSOE program has chosen to adopt the workflow support tool as its preferred health information technology solution.

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BACKGROUND

The Breast and Gynecologic System of Excellence (BGSOE) program has developed a workflow support tool using health information technology to assist clinicians, coordinators and stakeholders in identifying, tracking and supporting Veterans with breast and gynecological cancers. This tool was designed and implemented through a novel process that involved clarifying program aims, defining workflows in process delivery diagrams, and identifying data, analytic products, and user needs. To determine the optimal tool for the program, a comparative usability evaluation was conducted, comparing the new workflow support tool with a previous tool that shared identical aims but utilized a different approach.

METHODS

Usability evaluation employed the System Usability Scale (SUS) and measured acceptance using modified items from a validated instrument used in a national survey of electronic health records. Task efficiency was evaluated based on time taken and the number of clicks required to complete tasks.

RESULTS

Eight healthcare professionals with experience in the BGSOE program or similar programs in the VA participated in the usability evaluation. This group comprised physicians (38%), clinical pharmacist (25%), health care coordinators (25%), and registered nurse (12%). The workflow support tool achieved an impressive SUS score of 89.06, with acceptance scores of 93% (positive statements) and 6% (negative statements), outperforming the standard tool, which scored score of 57.5 on the SUS and had acceptance scores of 53% (positive statements) and 50% (negative statements). In the comparative ranking, 100% of the users preferred the workflow support tool, citing its userfriendliness, intuitiveness, and ease of use. On average, users completed all tasks using the workflow support tool in 8 minutes with 31 clicks, while the standard tool required 18 minutes and 124 clicks.

CONCLUSIONS

The adoption of a workflow support tool in the design of health information technology interventions leads to improved usability, efficiency, and adoption. Based on the positive results from the usability evaluation, the BGSOE program has chosen to adopt the workflow support tool as its preferred health information technology solution.

BACKGROUND

The Breast and Gynecologic System of Excellence (BGSOE) program has developed a workflow support tool using health information technology to assist clinicians, coordinators and stakeholders in identifying, tracking and supporting Veterans with breast and gynecological cancers. This tool was designed and implemented through a novel process that involved clarifying program aims, defining workflows in process delivery diagrams, and identifying data, analytic products, and user needs. To determine the optimal tool for the program, a comparative usability evaluation was conducted, comparing the new workflow support tool with a previous tool that shared identical aims but utilized a different approach.

METHODS

Usability evaluation employed the System Usability Scale (SUS) and measured acceptance using modified items from a validated instrument used in a national survey of electronic health records. Task efficiency was evaluated based on time taken and the number of clicks required to complete tasks.

RESULTS

Eight healthcare professionals with experience in the BGSOE program or similar programs in the VA participated in the usability evaluation. This group comprised physicians (38%), clinical pharmacist (25%), health care coordinators (25%), and registered nurse (12%). The workflow support tool achieved an impressive SUS score of 89.06, with acceptance scores of 93% (positive statements) and 6% (negative statements), outperforming the standard tool, which scored score of 57.5 on the SUS and had acceptance scores of 53% (positive statements) and 50% (negative statements). In the comparative ranking, 100% of the users preferred the workflow support tool, citing its userfriendliness, intuitiveness, and ease of use. On average, users completed all tasks using the workflow support tool in 8 minutes with 31 clicks, while the standard tool required 18 minutes and 124 clicks.

CONCLUSIONS

The adoption of a workflow support tool in the design of health information technology interventions leads to improved usability, efficiency, and adoption. Based on the positive results from the usability evaluation, the BGSOE program has chosen to adopt the workflow support tool as its preferred health information technology solution.

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An Interprofessional Effort to Reduce Infusion Drug Delivery Time

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Thu, 09/21/2023 - 12:46

PURPOSE

This quality improvement project aimed at addressing the issue of long waiting times in the hematology/ oncology clinic at Stratton VA Medical Center, aiming to improve the delivery time of infusion drugs and enhance patient care.

BACKGROUND

Patient feedback indicated that long waiting times were a significant barrier to care, with 32% of patients identifying this as an issue. Prolonged wait times in the healthcare setting can have various negative consequences, including increased patient dissatisfaction, reduced patient engagement, compromised patient safety, and increased healthcare costs.

METHODS

An interdisciplinary team comprising physicians, nurses, and pharmacists conducted a study to identify the primary contributors to extended wait times. Inadequate preparation for patients with complex infusion needs and delays in administering premedications were identified as the key factors. Wait times were measured using two variables: Go To Label Print (GTLP) and Go To First Bag Scanned (GTFS). Baseline data were collected showing a median GTLP of 8 minutes and a median GTFS of 67 minutes.

DATA ANALYSIS

The team analyzed real-time data related to wait times and the impact of interventions.

RESULTS

Two interventions were implemented: 1) redistributing patients with complex needs across the schedule and 2) adding premedications to the automated medication dispensing system. Postintervention analysis revealed a significant improvement in wait times. The median GTLP decreased to 2 minutes, and the median GTFS reduced to 53 minutes, representing a 75% improvement in GTLP and a 21% improvement in GTFS. These changes are estimated to save 303 patient hours annually.

IMPLICATIONS

This quality improvement project highlighted the significance of addressing long wait times, as they can significantly impact patient care. The team’s efforts, including the analysis of real-time data, interprofessional collaboration, and the implementation of sustainable changes through Plan-Do- Study-Act cycles, successfully improved infusion drug delivery time. These findings and interventions can serve as a model for other healthcare facilities seeking to streamline workflow in infusion centers and enhance patient care.

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PURPOSE

This quality improvement project aimed at addressing the issue of long waiting times in the hematology/ oncology clinic at Stratton VA Medical Center, aiming to improve the delivery time of infusion drugs and enhance patient care.

BACKGROUND

Patient feedback indicated that long waiting times were a significant barrier to care, with 32% of patients identifying this as an issue. Prolonged wait times in the healthcare setting can have various negative consequences, including increased patient dissatisfaction, reduced patient engagement, compromised patient safety, and increased healthcare costs.

METHODS

An interdisciplinary team comprising physicians, nurses, and pharmacists conducted a study to identify the primary contributors to extended wait times. Inadequate preparation for patients with complex infusion needs and delays in administering premedications were identified as the key factors. Wait times were measured using two variables: Go To Label Print (GTLP) and Go To First Bag Scanned (GTFS). Baseline data were collected showing a median GTLP of 8 minutes and a median GTFS of 67 minutes.

DATA ANALYSIS

The team analyzed real-time data related to wait times and the impact of interventions.

RESULTS

Two interventions were implemented: 1) redistributing patients with complex needs across the schedule and 2) adding premedications to the automated medication dispensing system. Postintervention analysis revealed a significant improvement in wait times. The median GTLP decreased to 2 minutes, and the median GTFS reduced to 53 minutes, representing a 75% improvement in GTLP and a 21% improvement in GTFS. These changes are estimated to save 303 patient hours annually.

IMPLICATIONS

This quality improvement project highlighted the significance of addressing long wait times, as they can significantly impact patient care. The team’s efforts, including the analysis of real-time data, interprofessional collaboration, and the implementation of sustainable changes through Plan-Do- Study-Act cycles, successfully improved infusion drug delivery time. These findings and interventions can serve as a model for other healthcare facilities seeking to streamline workflow in infusion centers and enhance patient care.

PURPOSE

This quality improvement project aimed at addressing the issue of long waiting times in the hematology/ oncology clinic at Stratton VA Medical Center, aiming to improve the delivery time of infusion drugs and enhance patient care.

BACKGROUND

Patient feedback indicated that long waiting times were a significant barrier to care, with 32% of patients identifying this as an issue. Prolonged wait times in the healthcare setting can have various negative consequences, including increased patient dissatisfaction, reduced patient engagement, compromised patient safety, and increased healthcare costs.

METHODS

An interdisciplinary team comprising physicians, nurses, and pharmacists conducted a study to identify the primary contributors to extended wait times. Inadequate preparation for patients with complex infusion needs and delays in administering premedications were identified as the key factors. Wait times were measured using two variables: Go To Label Print (GTLP) and Go To First Bag Scanned (GTFS). Baseline data were collected showing a median GTLP of 8 minutes and a median GTFS of 67 minutes.

DATA ANALYSIS

The team analyzed real-time data related to wait times and the impact of interventions.

RESULTS

Two interventions were implemented: 1) redistributing patients with complex needs across the schedule and 2) adding premedications to the automated medication dispensing system. Postintervention analysis revealed a significant improvement in wait times. The median GTLP decreased to 2 minutes, and the median GTFS reduced to 53 minutes, representing a 75% improvement in GTLP and a 21% improvement in GTFS. These changes are estimated to save 303 patient hours annually.

IMPLICATIONS

This quality improvement project highlighted the significance of addressing long wait times, as they can significantly impact patient care. The team’s efforts, including the analysis of real-time data, interprofessional collaboration, and the implementation of sustainable changes through Plan-Do- Study-Act cycles, successfully improved infusion drug delivery time. These findings and interventions can serve as a model for other healthcare facilities seeking to streamline workflow in infusion centers and enhance patient care.

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Close to Me: Cost Savings Analysis and Improving Veteran Access

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Thu, 09/21/2023 - 12:35

BACKGROUND

While the MISSION Act for community care has increased Veteran access to specialty services, this has caused considerable fragmentation of care and financial cost to U.S. taxpayers. The VA Salt Lake City Health Care System (VA SLCHCS) referral area spans 125,000 square miles, one of the largest geographic regions in the VA health care system. Numerous VA Community- Based Outpatient Clinics (CBOCs) have been established in central and southern Utah, eastern Nevada, and southern Idaho; however, these clinics do not currently provide specialty services.

DISCUSSION

In conjunction with the National Oncology Program’s Close to Me project team, we conducted a cost analysis to determine financial feasibility of providing low-risk oncology parenteral therapies at rural CBOCs. Based on FY22 DO Paid Claim PowerBI and Pyramid Analytics Reports, VA SLCHCS paid claims for Community Care Hematology/Oncology community services in excess of $5.7 million for 380 unique Veterans (approximately $15,060 per unique Veteran). Comparatively, Veterans received high quality oncology care through VA SLCHCS with an estimated average cost of care of $5,424 per unique Veteran. Cost of parenteral therapies was estimated via review of Community Care Paid Claims Reports for individual drug claim costs (based on Jcode), VA drug pricing data from the VA National Acquisition Center Catalog, and drug unit claims data. The unit price of VA-care and community care costs were calculated and drug cost at the VA versus non- VA was compared. By retaining or re-establishing Hematology/Oncology Veteran care within VA, we estimate cost savings of approximately $9,636 per unique Veteran.

CONCLUSIONS

By re-establishing oncology care within VA SLCHCS the facility could net a substantial cost savings while simultaneously making Veterans lives easier, reduce need for transportation to/from the main SLC VA site, decrease costs due to VA pricing contracts, lessen Veteran out-of-pocket costs, improve care coordination through use of one electronic medical record, and maintain Veteran care within VA SLCHCS. Additionally, VA SLCHCS oncology will help lead the effort to launch a system within the CBOC’s to deliver high-cost parental therapies that could benefit other medical specialties such as gastroenterology, dermatology, and rheumatology.

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BACKGROUND

While the MISSION Act for community care has increased Veteran access to specialty services, this has caused considerable fragmentation of care and financial cost to U.S. taxpayers. The VA Salt Lake City Health Care System (VA SLCHCS) referral area spans 125,000 square miles, one of the largest geographic regions in the VA health care system. Numerous VA Community- Based Outpatient Clinics (CBOCs) have been established in central and southern Utah, eastern Nevada, and southern Idaho; however, these clinics do not currently provide specialty services.

DISCUSSION

In conjunction with the National Oncology Program’s Close to Me project team, we conducted a cost analysis to determine financial feasibility of providing low-risk oncology parenteral therapies at rural CBOCs. Based on FY22 DO Paid Claim PowerBI and Pyramid Analytics Reports, VA SLCHCS paid claims for Community Care Hematology/Oncology community services in excess of $5.7 million for 380 unique Veterans (approximately $15,060 per unique Veteran). Comparatively, Veterans received high quality oncology care through VA SLCHCS with an estimated average cost of care of $5,424 per unique Veteran. Cost of parenteral therapies was estimated via review of Community Care Paid Claims Reports for individual drug claim costs (based on Jcode), VA drug pricing data from the VA National Acquisition Center Catalog, and drug unit claims data. The unit price of VA-care and community care costs were calculated and drug cost at the VA versus non- VA was compared. By retaining or re-establishing Hematology/Oncology Veteran care within VA, we estimate cost savings of approximately $9,636 per unique Veteran.

CONCLUSIONS

By re-establishing oncology care within VA SLCHCS the facility could net a substantial cost savings while simultaneously making Veterans lives easier, reduce need for transportation to/from the main SLC VA site, decrease costs due to VA pricing contracts, lessen Veteran out-of-pocket costs, improve care coordination through use of one electronic medical record, and maintain Veteran care within VA SLCHCS. Additionally, VA SLCHCS oncology will help lead the effort to launch a system within the CBOC’s to deliver high-cost parental therapies that could benefit other medical specialties such as gastroenterology, dermatology, and rheumatology.

BACKGROUND

While the MISSION Act for community care has increased Veteran access to specialty services, this has caused considerable fragmentation of care and financial cost to U.S. taxpayers. The VA Salt Lake City Health Care System (VA SLCHCS) referral area spans 125,000 square miles, one of the largest geographic regions in the VA health care system. Numerous VA Community- Based Outpatient Clinics (CBOCs) have been established in central and southern Utah, eastern Nevada, and southern Idaho; however, these clinics do not currently provide specialty services.

DISCUSSION

In conjunction with the National Oncology Program’s Close to Me project team, we conducted a cost analysis to determine financial feasibility of providing low-risk oncology parenteral therapies at rural CBOCs. Based on FY22 DO Paid Claim PowerBI and Pyramid Analytics Reports, VA SLCHCS paid claims for Community Care Hematology/Oncology community services in excess of $5.7 million for 380 unique Veterans (approximately $15,060 per unique Veteran). Comparatively, Veterans received high quality oncology care through VA SLCHCS with an estimated average cost of care of $5,424 per unique Veteran. Cost of parenteral therapies was estimated via review of Community Care Paid Claims Reports for individual drug claim costs (based on Jcode), VA drug pricing data from the VA National Acquisition Center Catalog, and drug unit claims data. The unit price of VA-care and community care costs were calculated and drug cost at the VA versus non- VA was compared. By retaining or re-establishing Hematology/Oncology Veteran care within VA, we estimate cost savings of approximately $9,636 per unique Veteran.

CONCLUSIONS

By re-establishing oncology care within VA SLCHCS the facility could net a substantial cost savings while simultaneously making Veterans lives easier, reduce need for transportation to/from the main SLC VA site, decrease costs due to VA pricing contracts, lessen Veteran out-of-pocket costs, improve care coordination through use of one electronic medical record, and maintain Veteran care within VA SLCHCS. Additionally, VA SLCHCS oncology will help lead the effort to launch a system within the CBOC’s to deliver high-cost parental therapies that could benefit other medical specialties such as gastroenterology, dermatology, and rheumatology.

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ClonoSEQ Testing for Minimal Residual Disease in Multiple Myeloma: Cleveland VA Experience And Cost Analysis

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Thu, 09/21/2023 - 12:31

BACKGROUND

Minimal residual disease (MRD) testing in myeloma has been shown to be a strong prognostic marker for progression-free and overall survival. Limited data suggest MRD results may also be useful for therapy discontinuation decisions. The clonoSEQ Assay utilizes next generation sequencing involving a bone marrow sample, obtained at the time of diagnosis, to identify patient-specific sequence(s).

DISCUSSION

The same methodology is then applied later to assess for MRD. Although widely adopted at most US academic centers, there has been limited use of MRD across VA centers. In 2022 the Cleveland Louis Stokes VAMC partnered with Adaptive Biotechnologies to develop a process for MRD/clonoSEQ testing in myeloma pts. Hematology, Pathology, Medicine, Administration and Adaptive Biotechnologies representatives met to develop a streamlined process for ordering, sample procurement, billing and result documentation. In 5/2022 the 1st specimen was sent. EQUATE is a national cooperative group trial requiring baseline clono- SEQ testing with a positive sequence ID. Daratumumab hyaluronidase (part of standard treatment) is provided to the institution at no cost on the trial but otherwise would cost the VA $5,797.38/dose. clonoSEQ costs VA $1950/test. There have been 14 specimens sent involving 12 pts: 12 baseline marrow and 2 for MRD (posttransplant). All of the baseline specimens were found to have an identifiable sequence. Both of the MRD tracking specimens were positive. The average turnaround time for clonoSEQ results was 13.2 days (range 7 to 18 days). 4 of the 12 pts with a positive initial clonoSEQ ID qualified for the EQUATE trial but would not have been deemed eligible without the baseline clonoSEQ results. 2 of these pts have enrolled on the trial and started treatment. Costs for 14 clonoSEQ tests: $27,300. Estimated cost savings for the 2 pts enrolled onto EQUATE: $127, 542.36/pt/year= $255,084.72/year. Overall cost savings: $227,784.72.

CONCLUSIONS

An efficient process for baseline and post-treatment (MRD) clonoSEQ testing in myeloma pts was developed. Although expensive, use of this test resulted in significant overall cost savings by allowing enrollment onto a clinical trial. In addition, if studies determine that negative MRD results can guide therapeutic decisions, use of clonoSEQ testing may result in further benefits.

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S15
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BACKGROUND

Minimal residual disease (MRD) testing in myeloma has been shown to be a strong prognostic marker for progression-free and overall survival. Limited data suggest MRD results may also be useful for therapy discontinuation decisions. The clonoSEQ Assay utilizes next generation sequencing involving a bone marrow sample, obtained at the time of diagnosis, to identify patient-specific sequence(s).

DISCUSSION

The same methodology is then applied later to assess for MRD. Although widely adopted at most US academic centers, there has been limited use of MRD across VA centers. In 2022 the Cleveland Louis Stokes VAMC partnered with Adaptive Biotechnologies to develop a process for MRD/clonoSEQ testing in myeloma pts. Hematology, Pathology, Medicine, Administration and Adaptive Biotechnologies representatives met to develop a streamlined process for ordering, sample procurement, billing and result documentation. In 5/2022 the 1st specimen was sent. EQUATE is a national cooperative group trial requiring baseline clono- SEQ testing with a positive sequence ID. Daratumumab hyaluronidase (part of standard treatment) is provided to the institution at no cost on the trial but otherwise would cost the VA $5,797.38/dose. clonoSEQ costs VA $1950/test. There have been 14 specimens sent involving 12 pts: 12 baseline marrow and 2 for MRD (posttransplant). All of the baseline specimens were found to have an identifiable sequence. Both of the MRD tracking specimens were positive. The average turnaround time for clonoSEQ results was 13.2 days (range 7 to 18 days). 4 of the 12 pts with a positive initial clonoSEQ ID qualified for the EQUATE trial but would not have been deemed eligible without the baseline clonoSEQ results. 2 of these pts have enrolled on the trial and started treatment. Costs for 14 clonoSEQ tests: $27,300. Estimated cost savings for the 2 pts enrolled onto EQUATE: $127, 542.36/pt/year= $255,084.72/year. Overall cost savings: $227,784.72.

CONCLUSIONS

An efficient process for baseline and post-treatment (MRD) clonoSEQ testing in myeloma pts was developed. Although expensive, use of this test resulted in significant overall cost savings by allowing enrollment onto a clinical trial. In addition, if studies determine that negative MRD results can guide therapeutic decisions, use of clonoSEQ testing may result in further benefits.

BACKGROUND

Minimal residual disease (MRD) testing in myeloma has been shown to be a strong prognostic marker for progression-free and overall survival. Limited data suggest MRD results may also be useful for therapy discontinuation decisions. The clonoSEQ Assay utilizes next generation sequencing involving a bone marrow sample, obtained at the time of diagnosis, to identify patient-specific sequence(s).

DISCUSSION

The same methodology is then applied later to assess for MRD. Although widely adopted at most US academic centers, there has been limited use of MRD across VA centers. In 2022 the Cleveland Louis Stokes VAMC partnered with Adaptive Biotechnologies to develop a process for MRD/clonoSEQ testing in myeloma pts. Hematology, Pathology, Medicine, Administration and Adaptive Biotechnologies representatives met to develop a streamlined process for ordering, sample procurement, billing and result documentation. In 5/2022 the 1st specimen was sent. EQUATE is a national cooperative group trial requiring baseline clono- SEQ testing with a positive sequence ID. Daratumumab hyaluronidase (part of standard treatment) is provided to the institution at no cost on the trial but otherwise would cost the VA $5,797.38/dose. clonoSEQ costs VA $1950/test. There have been 14 specimens sent involving 12 pts: 12 baseline marrow and 2 for MRD (posttransplant). All of the baseline specimens were found to have an identifiable sequence. Both of the MRD tracking specimens were positive. The average turnaround time for clonoSEQ results was 13.2 days (range 7 to 18 days). 4 of the 12 pts with a positive initial clonoSEQ ID qualified for the EQUATE trial but would not have been deemed eligible without the baseline clonoSEQ results. 2 of these pts have enrolled on the trial and started treatment. Costs for 14 clonoSEQ tests: $27,300. Estimated cost savings for the 2 pts enrolled onto EQUATE: $127, 542.36/pt/year= $255,084.72/year. Overall cost savings: $227,784.72.

CONCLUSIONS

An efficient process for baseline and post-treatment (MRD) clonoSEQ testing in myeloma pts was developed. Although expensive, use of this test resulted in significant overall cost savings by allowing enrollment onto a clinical trial. In addition, if studies determine that negative MRD results can guide therapeutic decisions, use of clonoSEQ testing may result in further benefits.

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Revision of a Massive Transfusion Protocol to Allow for Verbal Orders

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Changed
Fri, 09/29/2023 - 08:27

PURPOSE

To improve the time to release of blood products for patients with severe or life-threatening bleeding.

BACKGROUND

Exsanguination, and the resultant coagulopathy, is the number one cause of trauma-related death. Massive transfusion protocols (MTP) improve mortality by shortening the time to transfusion and correcting coagulopathy. Many patients do not meet criteria for massive transfusion (> 10 units RBCs in 24 hours), yet present with clinical instability and require rapid release (RR) of uncrossmatched blood. A quality improvement initiative was performed to identify barriers to the MTP/RR protocol at a single institution.

METHODS/DATA

A multidisciplinary subcommittee was formed to evaluate the safety and efficacy of the current MTP/RR process. Timed mock-MTP/RR trials were conducted to identify areas of delay with a goal to achieve a blood to bedside (B2B) time of under 10 minutes.

RESULTS

Timed mock-MTP/RR trials were conducted, which revealed a baseline B2B time of approximately 30 minutes. We identified problems and categorized them in terms of ordering (phase 1) and processing (phase 2). We found significant delays in phase 1. Reasons for delay were varied and included difficulty logging into the computer, staff unavailable to place orders (involved in resuscitation efforts), orders entered incorrectly, etc. Once orders were received, the blood bank could process them quickly in phase 2. Using root cause analysis, we discovered a critical step was to remove the barrier of electronic ordering. For this, a new process was developed in which the blood bank could accept verbal orders to release uncrossmatched blood during a medical emergency. Over the course of one year, a new policy for MTP/RR was drafted, an education training video was recorded, informational flyers were printed, and training drills were conducted. A repeat mock-MTP/RR scenario was performed after the change showing the B2B time was reduced by 90% from pre-intervention values to under 3 minutes. Since implementation, no new safety signals have been received, and the staff have reported improved satisfaction with the MTP/RR process.

IMPLICATIONS

A critical piece of any MTP/RR is the immediate availability of blood. Allowing verbal orders for blood products reduced time to transfusion by 90%. Through multidisciplinary effort, safe and efficient release of uncrossmatched blood products for nontraumatic massive transfusion can be achieved.

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PURPOSE

To improve the time to release of blood products for patients with severe or life-threatening bleeding.

BACKGROUND

Exsanguination, and the resultant coagulopathy, is the number one cause of trauma-related death. Massive transfusion protocols (MTP) improve mortality by shortening the time to transfusion and correcting coagulopathy. Many patients do not meet criteria for massive transfusion (> 10 units RBCs in 24 hours), yet present with clinical instability and require rapid release (RR) of uncrossmatched blood. A quality improvement initiative was performed to identify barriers to the MTP/RR protocol at a single institution.

METHODS/DATA

A multidisciplinary subcommittee was formed to evaluate the safety and efficacy of the current MTP/RR process. Timed mock-MTP/RR trials were conducted to identify areas of delay with a goal to achieve a blood to bedside (B2B) time of under 10 minutes.

RESULTS

Timed mock-MTP/RR trials were conducted, which revealed a baseline B2B time of approximately 30 minutes. We identified problems and categorized them in terms of ordering (phase 1) and processing (phase 2). We found significant delays in phase 1. Reasons for delay were varied and included difficulty logging into the computer, staff unavailable to place orders (involved in resuscitation efforts), orders entered incorrectly, etc. Once orders were received, the blood bank could process them quickly in phase 2. Using root cause analysis, we discovered a critical step was to remove the barrier of electronic ordering. For this, a new process was developed in which the blood bank could accept verbal orders to release uncrossmatched blood during a medical emergency. Over the course of one year, a new policy for MTP/RR was drafted, an education training video was recorded, informational flyers were printed, and training drills were conducted. A repeat mock-MTP/RR scenario was performed after the change showing the B2B time was reduced by 90% from pre-intervention values to under 3 minutes. Since implementation, no new safety signals have been received, and the staff have reported improved satisfaction with the MTP/RR process.

IMPLICATIONS

A critical piece of any MTP/RR is the immediate availability of blood. Allowing verbal orders for blood products reduced time to transfusion by 90%. Through multidisciplinary effort, safe and efficient release of uncrossmatched blood products for nontraumatic massive transfusion can be achieved.

PURPOSE

To improve the time to release of blood products for patients with severe or life-threatening bleeding.

BACKGROUND

Exsanguination, and the resultant coagulopathy, is the number one cause of trauma-related death. Massive transfusion protocols (MTP) improve mortality by shortening the time to transfusion and correcting coagulopathy. Many patients do not meet criteria for massive transfusion (> 10 units RBCs in 24 hours), yet present with clinical instability and require rapid release (RR) of uncrossmatched blood. A quality improvement initiative was performed to identify barriers to the MTP/RR protocol at a single institution.

METHODS/DATA

A multidisciplinary subcommittee was formed to evaluate the safety and efficacy of the current MTP/RR process. Timed mock-MTP/RR trials were conducted to identify areas of delay with a goal to achieve a blood to bedside (B2B) time of under 10 minutes.

RESULTS

Timed mock-MTP/RR trials were conducted, which revealed a baseline B2B time of approximately 30 minutes. We identified problems and categorized them in terms of ordering (phase 1) and processing (phase 2). We found significant delays in phase 1. Reasons for delay were varied and included difficulty logging into the computer, staff unavailable to place orders (involved in resuscitation efforts), orders entered incorrectly, etc. Once orders were received, the blood bank could process them quickly in phase 2. Using root cause analysis, we discovered a critical step was to remove the barrier of electronic ordering. For this, a new process was developed in which the blood bank could accept verbal orders to release uncrossmatched blood during a medical emergency. Over the course of one year, a new policy for MTP/RR was drafted, an education training video was recorded, informational flyers were printed, and training drills were conducted. A repeat mock-MTP/RR scenario was performed after the change showing the B2B time was reduced by 90% from pre-intervention values to under 3 minutes. Since implementation, no new safety signals have been received, and the staff have reported improved satisfaction with the MTP/RR process.

IMPLICATIONS

A critical piece of any MTP/RR is the immediate availability of blood. Allowing verbal orders for blood products reduced time to transfusion by 90%. Through multidisciplinary effort, safe and efficient release of uncrossmatched blood products for nontraumatic massive transfusion can be achieved.

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Implementation of an Interfacility Telehealth Cancer Genetics Clinic

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BACKGROUND

Cancer risk assessment and genetic counseling are the processes to identify and counsel people at risk for familial or hereditary cancer syndromes. They serve to inform, educate and empower patients and family members to make informed decisions about testing, cancer screening, and prevention. Additionally, genetic testing can also provide therapeutic options and opportunities for research.

METHODS

Prior to this program initiative, there were no cancer genetics services available at the VA Pittsburgh Medical Center (VAPHS) and 100% of genetics consults were referred to the community. Each year over $100,000 was spent outside of VAPHS on genetic testing and counseling. Community care referral resulted in fragmented care, prolonged wait times of 3 to 5 months, communication issues, and added financial cost to the institution. Corporal Michael J. Crescenz VA Medical Center (CMCVAMC) had previously created a genetics consultation service staffed with an advanced practice nurse that increased access to genetics services and testing rates at the facility-level. VAPHS recently established an interfacility telegenetics clinic with CMCVAMC to provide virtual genetic counseling services to Veterans at VAPHS. Under this program, VAPHS providers place an interfacility consult for Veterans who need cancer genetics services. The consult is received and reviewed by the CMCVAMC team. VAPHS patients are then seen by CMCVAMC providers via VVC or CVT and provide recommendations regarding additional genetic testing and follow-up.

RESULTS

The telegenetics clinic opened in October 2022. The clinic initially focused on patients with metastatic prostate cancer but has since expanded to provide care for all patients for whom genetics testing and/ or counseling is recommended by NCCN guidelines. Since initiation, 29 consults have been placed and 26 have been completed or are in process (89.6%). In the year prior to creation of the clinic, only 31 of 67 (46%) of referred patients completed genetics evaluation.

CONCLUSIONS

Due to the success of the clinic, plans to expand services to the VISN-level and within VAPHS to include high risk breast cancer assessment are underway. Efforts to provide genetic counseling services via virtual care modalities have the potential to increase access to care and to improve outcomes for veterans with cancer.

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BACKGROUND

Cancer risk assessment and genetic counseling are the processes to identify and counsel people at risk for familial or hereditary cancer syndromes. They serve to inform, educate and empower patients and family members to make informed decisions about testing, cancer screening, and prevention. Additionally, genetic testing can also provide therapeutic options and opportunities for research.

METHODS

Prior to this program initiative, there were no cancer genetics services available at the VA Pittsburgh Medical Center (VAPHS) and 100% of genetics consults were referred to the community. Each year over $100,000 was spent outside of VAPHS on genetic testing and counseling. Community care referral resulted in fragmented care, prolonged wait times of 3 to 5 months, communication issues, and added financial cost to the institution. Corporal Michael J. Crescenz VA Medical Center (CMCVAMC) had previously created a genetics consultation service staffed with an advanced practice nurse that increased access to genetics services and testing rates at the facility-level. VAPHS recently established an interfacility telegenetics clinic with CMCVAMC to provide virtual genetic counseling services to Veterans at VAPHS. Under this program, VAPHS providers place an interfacility consult for Veterans who need cancer genetics services. The consult is received and reviewed by the CMCVAMC team. VAPHS patients are then seen by CMCVAMC providers via VVC or CVT and provide recommendations regarding additional genetic testing and follow-up.

RESULTS

The telegenetics clinic opened in October 2022. The clinic initially focused on patients with metastatic prostate cancer but has since expanded to provide care for all patients for whom genetics testing and/ or counseling is recommended by NCCN guidelines. Since initiation, 29 consults have been placed and 26 have been completed or are in process (89.6%). In the year prior to creation of the clinic, only 31 of 67 (46%) of referred patients completed genetics evaluation.

CONCLUSIONS

Due to the success of the clinic, plans to expand services to the VISN-level and within VAPHS to include high risk breast cancer assessment are underway. Efforts to provide genetic counseling services via virtual care modalities have the potential to increase access to care and to improve outcomes for veterans with cancer.

BACKGROUND

Cancer risk assessment and genetic counseling are the processes to identify and counsel people at risk for familial or hereditary cancer syndromes. They serve to inform, educate and empower patients and family members to make informed decisions about testing, cancer screening, and prevention. Additionally, genetic testing can also provide therapeutic options and opportunities for research.

METHODS

Prior to this program initiative, there were no cancer genetics services available at the VA Pittsburgh Medical Center (VAPHS) and 100% of genetics consults were referred to the community. Each year over $100,000 was spent outside of VAPHS on genetic testing and counseling. Community care referral resulted in fragmented care, prolonged wait times of 3 to 5 months, communication issues, and added financial cost to the institution. Corporal Michael J. Crescenz VA Medical Center (CMCVAMC) had previously created a genetics consultation service staffed with an advanced practice nurse that increased access to genetics services and testing rates at the facility-level. VAPHS recently established an interfacility telegenetics clinic with CMCVAMC to provide virtual genetic counseling services to Veterans at VAPHS. Under this program, VAPHS providers place an interfacility consult for Veterans who need cancer genetics services. The consult is received and reviewed by the CMCVAMC team. VAPHS patients are then seen by CMCVAMC providers via VVC or CVT and provide recommendations regarding additional genetic testing and follow-up.

RESULTS

The telegenetics clinic opened in October 2022. The clinic initially focused on patients with metastatic prostate cancer but has since expanded to provide care for all patients for whom genetics testing and/ or counseling is recommended by NCCN guidelines. Since initiation, 29 consults have been placed and 26 have been completed or are in process (89.6%). In the year prior to creation of the clinic, only 31 of 67 (46%) of referred patients completed genetics evaluation.

CONCLUSIONS

Due to the success of the clinic, plans to expand services to the VISN-level and within VAPHS to include high risk breast cancer assessment are underway. Efforts to provide genetic counseling services via virtual care modalities have the potential to increase access to care and to improve outcomes for veterans with cancer.

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Development of a National Precision Oncology Program (NPOP) Dashboard Suite and Data Mart For Monitoring Somatic Molecular Testing Use

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BACKGROUND

As of May 2023, the Veterans Affairs (VA) National Precision Oncology Program (NPOP) has provided somatic molecular testing for nearly 36,000 Veterans with cancer. Automated tools to monitor test use (locally and nationally) have only been available for NGS testing in advanced stage lung and prostate cancer. To better track utilization of NPOP supported testing across all cancer indications, and to support strategies to promote wider adoption, we developed an automated data architecture to monitor program operations. Here, we describe the development of the NPOP data mart and summarize the core components of the NPOP Somatic Molecular Testing dashboards.

METHODS

SQL Server Integration Services was used to build the backend data mart, which required the ingestion of vendor-specific XML data and subsequent harmonization with data found in the VA’s Corporate Data Warehouse (CDW). The NPOP somatic testing dashboards, developed using Power BI, are securely hosted in the cloud, and accessible through SharePoint.

DATA ANALYSIS

The NPOP dashboard suite displays key measures using descriptive statistics, including counts, proportions, means, medians, and standard deviations. To support the visualization of comparisons we leveraged stacked and clustered bar charts, and violin plots.

RESULTS

The NPOP data mart refreshes nightly providing users with near real-time data. The NPOP somatic testing dashboards include an all vendor combined report and sub-reports organized by vendors: Foundation Medicine, Personalis, and Personal Genome Diagnostics and Tempus. All reports contain four views with the ability to toggle between tests ordered or completed. For current NPOP vendors, patient level data and turnaround time views were developed. Data are stratified by test category (i.e., NGS Solid, NGS Liquid, Heme, IHC) and can be viewed longitudinally (i.e., across time) and filtered by test date, VA facility, patient demographics, and cancer characteristics (diagnosis, stage). To date, over 50,000 tests have been ordered (90% through Foundation Medicine).

IMPLICATIONS

The NPOP data mart and operational dashboards synthesizes vast amounts of data into a visually consumable format that supports monitoring the uptake and variation of somatic molecular testing services being offered across the VA.

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BACKGROUND

As of May 2023, the Veterans Affairs (VA) National Precision Oncology Program (NPOP) has provided somatic molecular testing for nearly 36,000 Veterans with cancer. Automated tools to monitor test use (locally and nationally) have only been available for NGS testing in advanced stage lung and prostate cancer. To better track utilization of NPOP supported testing across all cancer indications, and to support strategies to promote wider adoption, we developed an automated data architecture to monitor program operations. Here, we describe the development of the NPOP data mart and summarize the core components of the NPOP Somatic Molecular Testing dashboards.

METHODS

SQL Server Integration Services was used to build the backend data mart, which required the ingestion of vendor-specific XML data and subsequent harmonization with data found in the VA’s Corporate Data Warehouse (CDW). The NPOP somatic testing dashboards, developed using Power BI, are securely hosted in the cloud, and accessible through SharePoint.

DATA ANALYSIS

The NPOP dashboard suite displays key measures using descriptive statistics, including counts, proportions, means, medians, and standard deviations. To support the visualization of comparisons we leveraged stacked and clustered bar charts, and violin plots.

RESULTS

The NPOP data mart refreshes nightly providing users with near real-time data. The NPOP somatic testing dashboards include an all vendor combined report and sub-reports organized by vendors: Foundation Medicine, Personalis, and Personal Genome Diagnostics and Tempus. All reports contain four views with the ability to toggle between tests ordered or completed. For current NPOP vendors, patient level data and turnaround time views were developed. Data are stratified by test category (i.e., NGS Solid, NGS Liquid, Heme, IHC) and can be viewed longitudinally (i.e., across time) and filtered by test date, VA facility, patient demographics, and cancer characteristics (diagnosis, stage). To date, over 50,000 tests have been ordered (90% through Foundation Medicine).

IMPLICATIONS

The NPOP data mart and operational dashboards synthesizes vast amounts of data into a visually consumable format that supports monitoring the uptake and variation of somatic molecular testing services being offered across the VA.

BACKGROUND

As of May 2023, the Veterans Affairs (VA) National Precision Oncology Program (NPOP) has provided somatic molecular testing for nearly 36,000 Veterans with cancer. Automated tools to monitor test use (locally and nationally) have only been available for NGS testing in advanced stage lung and prostate cancer. To better track utilization of NPOP supported testing across all cancer indications, and to support strategies to promote wider adoption, we developed an automated data architecture to monitor program operations. Here, we describe the development of the NPOP data mart and summarize the core components of the NPOP Somatic Molecular Testing dashboards.

METHODS

SQL Server Integration Services was used to build the backend data mart, which required the ingestion of vendor-specific XML data and subsequent harmonization with data found in the VA’s Corporate Data Warehouse (CDW). The NPOP somatic testing dashboards, developed using Power BI, are securely hosted in the cloud, and accessible through SharePoint.

DATA ANALYSIS

The NPOP dashboard suite displays key measures using descriptive statistics, including counts, proportions, means, medians, and standard deviations. To support the visualization of comparisons we leveraged stacked and clustered bar charts, and violin plots.

RESULTS

The NPOP data mart refreshes nightly providing users with near real-time data. The NPOP somatic testing dashboards include an all vendor combined report and sub-reports organized by vendors: Foundation Medicine, Personalis, and Personal Genome Diagnostics and Tempus. All reports contain four views with the ability to toggle between tests ordered or completed. For current NPOP vendors, patient level data and turnaround time views were developed. Data are stratified by test category (i.e., NGS Solid, NGS Liquid, Heme, IHC) and can be viewed longitudinally (i.e., across time) and filtered by test date, VA facility, patient demographics, and cancer characteristics (diagnosis, stage). To date, over 50,000 tests have been ordered (90% through Foundation Medicine).

IMPLICATIONS

The NPOP data mart and operational dashboards synthesizes vast amounts of data into a visually consumable format that supports monitoring the uptake and variation of somatic molecular testing services being offered across the VA.

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A Multi-Disciplinary Approach to Increasing Germline Genetic Testing for Prostate Cancer

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PURPOSE

This quality improvement project aims to enhance the rate of germline genetic testing for prostate cancer at the Stratton VA Medical Center, improving risk reduction strategies and therapeutic options for patients.

BACKGROUND

Prostate cancer is prevalent at the Stratton VA Medical Center, yet the rate of genetic evaluation for prostate cancer remains suboptimal. National guidelines recommend genetic counseling and testing in specific patient populations. To address this gap, an interdisciplinary working group conducted gap analysis and root cause analysis, identifying four significant barriers.

METHODS 

The working group comprised medical oncologists, urologists, primary care physicians, genetics counselors, data experts, and a LEAN coach. Interventions included implementing a prostate cancer pathway to educate staff on genetic testing indications and integrating genetic testing screening into clinic visits. After the interventions were implemented in January 2022, patient charts were reviewed for all genetic referrals and new prostate cancer diagnoses from January to December 2022.

DATA ANALYSIS

Descriptive analysis was conducted on referral rates, evaluation visit completion rates, and genetic testing outcomes among prostate cancer patients.

RESULTS

During the study period, 59 prostate cancer patients were referred for genetic evaluation. Notably, this was a large increase from no genetic referrals for prostate cancer in the previous year. Among them, 43 completed the evaluation visit, and 34 underwent genetic testing. Noteworthy findings were observed in 5 patients, including 3 variants of unknown significance and 2 pathogenic germline variants: HOXB13 and BRCA2 mutations.

IMPLICATIONS

This project highlights the power of a collaborative, multidisciplinary approach to overcome barriers and enhance the quality of care for prostate cancer patients. The team’s use of gap analysis and root cause analysis successfully identified barriers and proposed solutions, leading to increased referrals and the identification of significant genetic findings. Continued efforts to improve access to germline genetic testing are crucial for enhanced patient care and improved outcomes.

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PURPOSE

This quality improvement project aims to enhance the rate of germline genetic testing for prostate cancer at the Stratton VA Medical Center, improving risk reduction strategies and therapeutic options for patients.

BACKGROUND

Prostate cancer is prevalent at the Stratton VA Medical Center, yet the rate of genetic evaluation for prostate cancer remains suboptimal. National guidelines recommend genetic counseling and testing in specific patient populations. To address this gap, an interdisciplinary working group conducted gap analysis and root cause analysis, identifying four significant barriers.

METHODS 

The working group comprised medical oncologists, urologists, primary care physicians, genetics counselors, data experts, and a LEAN coach. Interventions included implementing a prostate cancer pathway to educate staff on genetic testing indications and integrating genetic testing screening into clinic visits. After the interventions were implemented in January 2022, patient charts were reviewed for all genetic referrals and new prostate cancer diagnoses from January to December 2022.

DATA ANALYSIS

Descriptive analysis was conducted on referral rates, evaluation visit completion rates, and genetic testing outcomes among prostate cancer patients.

RESULTS

During the study period, 59 prostate cancer patients were referred for genetic evaluation. Notably, this was a large increase from no genetic referrals for prostate cancer in the previous year. Among them, 43 completed the evaluation visit, and 34 underwent genetic testing. Noteworthy findings were observed in 5 patients, including 3 variants of unknown significance and 2 pathogenic germline variants: HOXB13 and BRCA2 mutations.

IMPLICATIONS

This project highlights the power of a collaborative, multidisciplinary approach to overcome barriers and enhance the quality of care for prostate cancer patients. The team’s use of gap analysis and root cause analysis successfully identified barriers and proposed solutions, leading to increased referrals and the identification of significant genetic findings. Continued efforts to improve access to germline genetic testing are crucial for enhanced patient care and improved outcomes.

PURPOSE

This quality improvement project aims to enhance the rate of germline genetic testing for prostate cancer at the Stratton VA Medical Center, improving risk reduction strategies and therapeutic options for patients.

BACKGROUND

Prostate cancer is prevalent at the Stratton VA Medical Center, yet the rate of genetic evaluation for prostate cancer remains suboptimal. National guidelines recommend genetic counseling and testing in specific patient populations. To address this gap, an interdisciplinary working group conducted gap analysis and root cause analysis, identifying four significant barriers.

METHODS 

The working group comprised medical oncologists, urologists, primary care physicians, genetics counselors, data experts, and a LEAN coach. Interventions included implementing a prostate cancer pathway to educate staff on genetic testing indications and integrating genetic testing screening into clinic visits. After the interventions were implemented in January 2022, patient charts were reviewed for all genetic referrals and new prostate cancer diagnoses from January to December 2022.

DATA ANALYSIS

Descriptive analysis was conducted on referral rates, evaluation visit completion rates, and genetic testing outcomes among prostate cancer patients.

RESULTS

During the study period, 59 prostate cancer patients were referred for genetic evaluation. Notably, this was a large increase from no genetic referrals for prostate cancer in the previous year. Among them, 43 completed the evaluation visit, and 34 underwent genetic testing. Noteworthy findings were observed in 5 patients, including 3 variants of unknown significance and 2 pathogenic germline variants: HOXB13 and BRCA2 mutations.

IMPLICATIONS

This project highlights the power of a collaborative, multidisciplinary approach to overcome barriers and enhance the quality of care for prostate cancer patients. The team’s use of gap analysis and root cause analysis successfully identified barriers and proposed solutions, leading to increased referrals and the identification of significant genetic findings. Continued efforts to improve access to germline genetic testing are crucial for enhanced patient care and improved outcomes.

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Improving Germline Genetic Testing Among Veterans With High Risk, Very High Risk and Metastatic Prostate Cancer

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PURPOSE

To improve germline genetic testing among Veterans with high risk, very high risk and metastatic prostate cancer.

BACKGROUND

During our Commission on Cancer survey in 2021, it was noted that the Detroit VA’s referrals for germline genetic testing and counseling were extremely low. In 2020, only 1 Veteran was referred for prostate germline genetic testing and counseling and only 8 Veterans were referred in 2021. It was felt that the need to refer Veterans outside of the Detroit VA may have contributed to these low numbers. Our Cancer Committee chose prostate cancer as a disease to focus on. We chose a timeline of one year to implement our process.

METHODS

We made testing and counseling locally accessible to Veterans and encouraged medical oncology providers to make it part of the care of Veterans with high risk, very high risk and metastatic prostate cancer. We sought the assistance of the VA’s National Precision Oncology Program and were able to secure financial and logistical support to perform germline molecular prostate panel testing at the Detroit VA. We were also able to identify a cancer genetic specialist at the Ann Arbor VA that would perform genetic counseling among this group of patients based on their test results. Our medical oncology providers identified Veterans meeting the criteria for testing. Education regarding germline testing, its benefits and implications were conducted with Veterans, and performed after obtaining their informed consent in collaboration with our pathology department. The specimen is then sent to a VA central laboratory for processing. Detroit VA providers are alerted by the local laboratory once results are available. Veterans are then referred to the genetic counseling specialist based on the results. Some of these counseling visits are done virtually for the Veteran’s convenience.

DATA ANALYSIS

A retrospective chart analysis was used to collect the data.

RESULTS

After the implementation of our initiative, 97 Veterans with high risk, very high risk or metastatic prostate cancer were educated on the benefits of germline genetic testing, 87 of whom agreed to be tested. As of 4/2/23, 48 tests have already been performed. Pathogenic variants were recorded on 2 Veterans so far. One was for BRCA2 and KDM6A, and the other was for ATM. Data collection and recording is on-going.

IMPLICATIONS

Improving accessibility and incorporating genetic testing and counseling in cancer care can improve their utilization.

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PURPOSE

To improve germline genetic testing among Veterans with high risk, very high risk and metastatic prostate cancer.

BACKGROUND

During our Commission on Cancer survey in 2021, it was noted that the Detroit VA’s referrals for germline genetic testing and counseling were extremely low. In 2020, only 1 Veteran was referred for prostate germline genetic testing and counseling and only 8 Veterans were referred in 2021. It was felt that the need to refer Veterans outside of the Detroit VA may have contributed to these low numbers. Our Cancer Committee chose prostate cancer as a disease to focus on. We chose a timeline of one year to implement our process.

METHODS

We made testing and counseling locally accessible to Veterans and encouraged medical oncology providers to make it part of the care of Veterans with high risk, very high risk and metastatic prostate cancer. We sought the assistance of the VA’s National Precision Oncology Program and were able to secure financial and logistical support to perform germline molecular prostate panel testing at the Detroit VA. We were also able to identify a cancer genetic specialist at the Ann Arbor VA that would perform genetic counseling among this group of patients based on their test results. Our medical oncology providers identified Veterans meeting the criteria for testing. Education regarding germline testing, its benefits and implications were conducted with Veterans, and performed after obtaining their informed consent in collaboration with our pathology department. The specimen is then sent to a VA central laboratory for processing. Detroit VA providers are alerted by the local laboratory once results are available. Veterans are then referred to the genetic counseling specialist based on the results. Some of these counseling visits are done virtually for the Veteran’s convenience.

DATA ANALYSIS

A retrospective chart analysis was used to collect the data.

RESULTS

After the implementation of our initiative, 97 Veterans with high risk, very high risk or metastatic prostate cancer were educated on the benefits of germline genetic testing, 87 of whom agreed to be tested. As of 4/2/23, 48 tests have already been performed. Pathogenic variants were recorded on 2 Veterans so far. One was for BRCA2 and KDM6A, and the other was for ATM. Data collection and recording is on-going.

IMPLICATIONS

Improving accessibility and incorporating genetic testing and counseling in cancer care can improve their utilization.

PURPOSE

To improve germline genetic testing among Veterans with high risk, very high risk and metastatic prostate cancer.

BACKGROUND

During our Commission on Cancer survey in 2021, it was noted that the Detroit VA’s referrals for germline genetic testing and counseling were extremely low. In 2020, only 1 Veteran was referred for prostate germline genetic testing and counseling and only 8 Veterans were referred in 2021. It was felt that the need to refer Veterans outside of the Detroit VA may have contributed to these low numbers. Our Cancer Committee chose prostate cancer as a disease to focus on. We chose a timeline of one year to implement our process.

METHODS

We made testing and counseling locally accessible to Veterans and encouraged medical oncology providers to make it part of the care of Veterans with high risk, very high risk and metastatic prostate cancer. We sought the assistance of the VA’s National Precision Oncology Program and were able to secure financial and logistical support to perform germline molecular prostate panel testing at the Detroit VA. We were also able to identify a cancer genetic specialist at the Ann Arbor VA that would perform genetic counseling among this group of patients based on their test results. Our medical oncology providers identified Veterans meeting the criteria for testing. Education regarding germline testing, its benefits and implications were conducted with Veterans, and performed after obtaining their informed consent in collaboration with our pathology department. The specimen is then sent to a VA central laboratory for processing. Detroit VA providers are alerted by the local laboratory once results are available. Veterans are then referred to the genetic counseling specialist based on the results. Some of these counseling visits are done virtually for the Veteran’s convenience.

DATA ANALYSIS

A retrospective chart analysis was used to collect the data.

RESULTS

After the implementation of our initiative, 97 Veterans with high risk, very high risk or metastatic prostate cancer were educated on the benefits of germline genetic testing, 87 of whom agreed to be tested. As of 4/2/23, 48 tests have already been performed. Pathogenic variants were recorded on 2 Veterans so far. One was for BRCA2 and KDM6A, and the other was for ATM. Data collection and recording is on-going.

IMPLICATIONS

Improving accessibility and incorporating genetic testing and counseling in cancer care can improve their utilization.

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Implementing a Telehealth Shared Counseling and Decision-Making Visit for Lung Cancer Screening in a Veterans Affairs Medical Center

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Lung cancer is the second most frequently diagnosed cancer among US veterans and the leading cause of cancer death.1 Clinical trials have shown that annual screening of high-risk persons with low-dose computed tomography (LDCT) can reduce the risk of dying of lung cancer.2 In 2011, the National Lung Screening Trial (NLST) reported that over a 3-year period, annual LDCT screening reduced the risk of dying of lung cancer by 20% compared with chest radiograph screening.3 Lung cancer screening (LCS), however, was associated with harms, including false-positive results, complications from invasive diagnostic procedures, incidental findings, overdiagnosis, and radiation exposure.

The US Preventive Services Task Force (USPSTF) began recommending annual screening of high-risk persons after publication of the NLST results.4 The Veterans Health Administration (VHA) recommended implementing LCS in 2017.5 Guidelines, however, have consistently highlighted the complexity of the decision and the importance of engaging patients in thorough discussions about the potential benefits and harms of screening (shared decision making [SDM]). The Centers for Medicare and Medicaid Services (CMS) has issued coverage determinations mandating that eligible patients undergo a counseling visit that uses a decision aid to support SDM for LCS and addresses tobacco use.6,7 However, primary care practitioners (PCPs) face many challenges in delivering SDM, including a lack of awareness of clinical trial results and screening guidelines, competing clinical demands, being untrained in SDM, and not having educational resources.8 Patients in rural locations face travel burdens in attending counseling visits.9

We conducted a pilot study to address concerns with delivering SDM for LCS to veterans. We implemented a centralized screening model in which veterans were referred by clinicians to a trained decision coach who conducted telephone visits to discuss the initial LCS decision, addressed tobacco cessation, and placed LDCT orders. We evaluated the outcomes of this telemedicine visit by using decision quality metrics and tracking LCS uptake, referrals for tobacco cessation, and clinical outcomes. The University of Iowa Institutional Review Board considered this study to be a quality improvement project and waived informed consent and HIPAA (Health Insurance Portability and Accountability Act) authorization requirements.

 

 

Implementation

We implemented the LCS program at the Iowa City Veterans Affairs Health Care System (ICVAHCS), which has both resident and staff clinicians, and 2 community-based outpatient clinics (Coralville, Cedar Rapids) with staff clinicians. The pilot study, conducted from November 2020 through July 2022, was led by a multidisciplinary team that included a nurse, primary care physician, pulmonologist, and radiologist. The team conducted online presentations to educate PCPs about the epidemiology of lung cancer, results of screening trials, LCS guidelines, the rationale for a centralized model of SDM, and the ICVAHCS screening protocols.

Screening Referrals

When the study began in 2020, we used the 2015 USPSTF criteria for annual LCS: individuals aged 55 to 80 years with a 30 pack-year smoking history and current tobacco user or who had quit within 15 years.4 We lowered the starting age to 50 years and the pack-year requirement to 20 after the USPSTF issued updated guidelines in 2021.10 Clinicians were notified about potentially eligible patients through the US Department of Veterans Affairs (VA) Computerized Personal Record System (CPRS) reminders or by the nurse program coordinator (NPC) who reviewed health records of patients with upcoming appointments. If the clinician determined that screening was appropriate, they ordered an LCS consult. The NPC called the veteran to confirm eligibility, mailed a decision aid, and scheduled a telephone visit to conduct SDM. We used the VA decision aid developed for the LCS demonstration project conducted at 8 academic VA medical centers between 2013 and 2017.11

Shared Decision-Making Telephone Visit

The NPC adapted a telephone script developed for a Cancer Prevention and Research Institute of Texas–funded project conducted by 2 coauthors (RJV and LML).12 The NPC asked about receipt/review of the decision aid, described the screening process, and addressed benefits and potential harms of screening. The NPC also offered smoking cessation interventions for veterans who were currently smoking, including referrals to the VA patient aligned care team clinical pharmacist for management of tobacco cessation or to the national VA Quit Line. The encounter ended by assessing the veteran’s understanding of screening issues and eliciting the veteran’s preferences for LDCT and willingness to adhere with the LCS program.

LDCT Imaging

The NPC placed LDCT orders for veterans interested in screening and alerted the referring clinician to sign the order. Veterans who agreed to be screened were placed in an LCS dashboard developed by the Veterans Integrated Services Network (VISN) 23 LCS program that was used as a patient management tool. The dashboard allowed the NPC to track patients, ensuring that veterans were being scheduled for and completing initial and follow-up testing. Radiologists used the Lung-RADS (Lung Imaging Reporting and Data System) to categorize LDCT results (1, normal; 2, benign nodule; 3, probably benign nodule; 4, suspicious nodule).13 Veterans with Lung-RADS 1 or 2 results were scheduled for an annual LDCT (if they remained eligible). Veterans with Lung-RADS 3 results were scheduled for a 6-month follow-up CT. The screening program sent electronic consults to pulmonary for veterans with Lung-RADS 4 to determine whether they should undergo additional imaging or be evaluated in the pulmonary clinic.

 

 

Evaluating Shared Decision Making

We audio taped and transcribed randomly selected SDM encounters to assess fidelity with the 2016 CMS required discussion elements for counseling about lung cancer, including the benefit of reducing lung cancer mortality; the potential for harms from false alarms, incidental findings, overdiagnosis, and radiation exposure; the need for annual screening; the importance of smoking cessation; and the possibility of undergoing follow-up testing and diagnostic procedures. An investigator coded the transcripts to assess for the presence of each required element and scored the encounter from 0 to 7.

We also surveyed veterans completing SDM, using a convenience sampling strategy to evaluate knowledge, the quality of the SDM process, and decisional conflict. Initially, we sent mailed surveys to subjects to be completed 1 week after the SDM visit. To increase the response rate, we subsequently called patients to complete the surveys by telephone 1 week after the SDM visit.

We used the validated LCS-12 knowledge measure to assess awareness of lung cancer risks, screening eligibility, and the benefits and harms of screening.14 We evaluated the quality of the SDM visit by using the 3-item CollaboRATE scale (Table 1).15

table 1
The response items were scored on a 9-point Likert scale (0, no effort; 9, every effort). The CollaboRATE developers recommend reporting the top score (ie, the proportion of subjects whose response to all 3 questions was 9).16 We used the 4-item SURE scale to assess decisional conflict, a measure of uncertainty about choosing an option.17 A yes response received 1 point; patients with scores of 4 were considered to have no decisional conflict.

The NPC also took field notes during interviews to help identify additional SDM issues. After each call, the NPC noted her impressions of the veteran’s engagement with SDM and understanding of the screening issues.

Clinical Outcomes

We used the screening dashboard and CPRS to track clinical outcomes, including screening uptake, referrals for tobacco cessation, appropriate (screening or diagnostic) follow-up testing, and cancer diagnoses. We used descriptive statistics to characterize demographic data and survey responses.

Initial Findings

We conducted 105 SDM telephone visits from November 2020 through July 2022 (Table 2).

table 2
We audio taped 27 encounters. Measures of SDM showed good fidelity with addressing required CMS elements. The mean number of elements addressed was 6.2 of 7. Reduction in lung cancer mortality was the issue least likely to be addressed (59%).

We surveyed 47 of the veterans completing SDM visits (45%) and received 37 completed surveys (79%). All respondents were male, mean age 61.9 years, 89% White, 38% married/partnered, 70% rural, 65% currently smoking, with a mean 44.8 pack-years smoking history. On average, veterans answered 6.3 (53%) of knowledge questions correctly (Table 3).

table 3
They were most likely to correctly answer questions about the harms of radiation exposure (65%), false-positive results (84%), false-negative results (78%), and overdiagnosis (86%).

Only 1 respondent (3%) correctly answered the multiple-choice question about indications for stopping screening. Two (5%) correctly answered the question on the magnitude of benefit, most overestimated or did not know. Similarly, 23 (62%) overestimated or did not know the predictive value of an abnormal scan. About two-thirds of veterans underestimated or did not know the attributable risk of lung cancer from tobacco, and about four-fifths did not know the mortality rank of lung cancer. Among the 37 respondents, 31 (84%) indicated not having any decisional conflict as defined by a score of 4 on the SURE scale.
table 4
Overall, 59% of respondents had a top box score on the CollaboRATE scale. Ratings for individual domains ranged from 65% to 73% (Table 4).

 

 

Implementing SDM

The NPC’s field notes indicated that many veterans did not perceive any need to discuss the screening decision and believed that their PCP had referred them just for screening. However, they reported having cursory discussions with their PCP, being told that only their history of heavy tobacco use meant they should be screened. For veterans who had not read the decision aid, the NPC attempted to summarize benefits and harms. However, the discussions were often inadequate because the veterans were not interested in receiving information, particularly numerical data, or indicated that they had limited time for the call.

Seventy-two (69%) of the veterans who met with the NPC were currently smoking. Tobacco cessation counseling was offered to 66; 29 were referred to the VA Quit Line, 10 were referred to the tobacco cessation pharmacist, and the NPC contacted the PCPs for 9 patients who wanted prescriptions for nicotine replacement therapy.

After the SDM visit, 91 veterans (87%) agreed to screening. By the end of the study period, 73 veterans (80%) completed testing. Most veterans had Lung-RADS 1 or 2 results, 11 (1%) had a Lung-RADS 3, and 7 (10%) had a Lung-RADS 4. All 9 veterans with Lung-RADS 3 results and at least 6 months of follow-up underwent repeat imaging within 4 to 13 months (median, 7). All veterans with a Lung-RADS 4 result were referred to pulmonary. One patient was diagnosed with an early-stage non–small cell lung cancer.

We identified several problems with LDCT coding. Radiologists did not consistently use Lung-RADS when interpreting screening LDCTs; some used the Fleischner lung nodule criteria.18 We also found discordant readings for abnormal LDCTs, where the assigned Lung-RADS score was not consistent with the nodule description in the radiology report.

Discussion

Efforts to implement LCS with a telemedicine SDM intervention were mixed. An NPC-led SDM phone call was successfully incorporated into the clinical workflow. Most veterans identified as being eligible for screening participated in the counseling visit and underwent screening. However, they were often reluctant to engage in SDM, feeling that their clinician had already recommended screening and that there was no need for further discussion. Unfortunately, many veterans had not received or reviewed the decision aid and were not interested in receiving information about benefits and harms. Because we relied on telephone calls, we could not share visual information in real time.

Overall, the surveys indicated that most veterans were very satisfied with the quality of the discussion and reported feeling no decisional conflict. However, based on the NPC’s field notes and audio recordings, we believe that the responses may have reflected earlier discussions with the PCP that reportedly emphasized only the veteran’s eligibility for screening. The fidelity assessments indicated that the NPC consistently addressed the harms and benefits of screening.

Nonetheless, the performance on knowledge measures was uneven. Veterans were generally aware of harms, including false alarms, overdiagnosis, radiation exposure, and incidental findings. They did not, however, appreciate when screening should stop. They also underestimated the risks of developing lung cancer and the portion of that risk attributable to tobacco use, and overestimated the benefits of screening. These results suggest that the veterans, at least those who completed the surveys, may not be making well-informed decisions.

Our findings echo those of other VA investigators in finding knowledge deficits among screened veterans, including being unaware that LDCT was for LCS, believing that screening could prevent cancer, receiving little information about screening harms, and feeling that negative tests meant they were among the “lucky ones” who would avoid harm from continued smoking.19,20

The VA is currently implementing centralized screening models with the Lung Precision Oncology Program and the VA partnership to increase access to lung screening (VA-PALS).5 The centralized model, which readily supports the tracking, monitoring, and reporting needs of a screening program, also has advantages in delivering SDM because counselors have been trained in SDM, are more familiar with LCS evidence and processes, can better incorporate decision tools, and do not face the same time constraints as clinicians.21 However, studies have shown that most patients have already decided to be screened when they show up for the SDM visit.22 In contrast, about one-third of patients in primary care settings who receive decision support chose not to be screened.23,24 We found that 13% of our patients decided against screening after a telephone discussion, suggesting that a virtually conducted SDM visit can meaningfully support decision making. Telemedicine also may reduce health inequities in centralized models arising from patients having limited access to screening centers.

Our results suggest that PCPs referring patients to a centralized program, even for virtual visits, should frame the decision to initiate LCS as SDM, where an informed patient is being supported in making a decision consistent with their values and preferences. Furthermore, engaging patients in SDM should not be construed as endorsing screening. When centralized support is less available, individual clinics may need to provide SDM, perhaps using a nonclinician decision coach if clinicians lack the time to lead the discussions. Decision coaches have been effectively used to increase patients’ knowledge about the benefits and harms of screening.12 Regardless of the program model, PCPs will also be responsible for determining whether patients are healthy enough to undergo invasive diagnostic testing and treatment and ensuring that tobacco use is addressed.

SDM delivered in any setting will be enhanced by ensuring that patients are provided with decision aids before a counseling visit. This will help them better understand the benefits and harms of screening and the need to elicit values. The discussion can then focus on areas of concern or questions raised by reviewing the decision aid. The clinician and patient could also use a decision aid during either a face-to-face or video clinical encounter to facilitate SDM. A Cochrane review has shown that using decision aids for people facing screening decisions increases knowledge, reduces decisional conflict, and effectively elicits values and preferences.25 Providing high-quality decision support is a patient-centered approach that respects a patient’s autonomy and may promote health equity and improve adherence.

We recognized the importance of having a multidisciplinary team, involving primary care, radiology, pulmonary, and nursing, with a shared understanding of the screening processes. These are essential features for a high-quality screening program where eligible veterans are readily identified and receive prompt and appropriate follow-up. Radiologists need to use Lung-RADS categories consistently and appropriately when reading LDCTs. This may require ongoing educational efforts, particularly given the new CMS guidelines accepting nonsubspecialist chest readers.7 Additionally, fellows and board-eligible residents may interpret images in academic settings and at VA facilities. The program needs to work closely with the pulmonary service to ensure that Lung-RADS 4 patients are promptly assessed. Radiologists and pulmonologists should calibrate the application of Lung-RADS categories to pulmonary nodules through jointly participating in meetings to review selected cases.

 

 

Challenges and Limitations

We faced some notable implementation challenges. The COVID-19 pandemic was extremely disruptive to LCS as it was to all health care. In addition, screening workflow processes were hampered by a lack of clinical reminders, which ideally would trigger for clinicians based on the tobacco history. The absence of this reminder meant that numerous patients were found to be ineligible for screening. We have a long-standing lung nodule clinic, and clinicians were confused about whether to order a surveillance imaging for an incidental nodule or a screening LDCT.

The radiology service was able to update order sets in CPRS to help guide clinicians in distinguishing indications and prerequisites for enrolling in LCS. This helped reduce the number of inappropriate orders and crossover orders between the VISN nodule tracking program and the LCS program.

Our results were preliminary and based on a small sample. We did not survey all veterans who underwent SDM, though the response rate was 79% and patient characteristics were similar to the larger cohort. Our results were potentially subject to selection bias, which could inflate the positive responses about decision quality and decisional conflict. However, the knowledge deficits are likely to be valid and suggest a need to better inform eligible veterans about the benefits and harms of screening. We did not have sufficient follow-up time to determine whether veterans were adherent to annual screenings. We showed that almost all those with abnormal imaging results completed diagnostic evaluations and/or were evaluated by pulmonary. As the program matures, we will be able to track outcomes related to cancer diagnoses and treatment.

Conclusions

A centralized LCS program was able to deliver SDM and enroll veterans in a screening program. While veterans were confident in their decision to screen and felt that they participated in decision making, knowledge testing indicated important deficits. Furthermore, we observed that many veterans did not meaningfully engage in SDM. Clinicians will need to frame the decision as patient centered at the time of referral, highlight the role of the NPC and importance of SDM, and be able to provide adequate decision support. The SDM visits can be enhanced by ensuring that veterans are able to review decision aids. Telemedicine is an acceptable and effective approach for supporting screening discussions, particularly for rural veterans.26

Acknowledgments

The authors thank the following individuals for their contributions to the study: John Paul Hornbeck, program support specialist; Kelly Miell, PhD; Bradley Mecham, PhD; Christopher C. Richards, MA; Bailey Noble, NP; Rebecca Barnhart, program analyst.

References

1. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701. doi:10.7205/milmed-d-11-00434

2. Hoffman RM, Atallah RP, Struble RD, Badgett RG. Lung cancer screening with low-dose CT: a meta-analysis. J Gen Intern Med. 2020;35(10):3015-3025. doi:10.1007/s11606-020-05951-7

3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. doi:10.1056/NEJMoa1102873

4. Moyer VA, US Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338. doi:10.7326/M13-2771

5. Maurice NM, Tanner NT. Lung cancer screening at the VA: past, present and future. Semin Oncol. 2022;S0093-7754(22)00041-0. doi:10.1053/j.seminoncol.2022.06.001

6. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439N). Published 2015. Accessed July 10, 2023. http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274

7. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439R). Published 2022. Accessed July 10, 2023. https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304

8. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; National Cancer Policy Forum. Implementation of Lung Cancer Screening: Proceedings of a Workshop. The National Academies Press; November 17, 2016. doi:10.172216/23680

9. Bernstein E, Bade BC, Akgün KM, Rose MG, Cain HC. Barriers and facilitators to lung cancer screening and follow-up. Semin Oncol. 2022;S0093-7754(22)00058-6. doi:10.1053/j.seminoncol.2022.07.004

10. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(10):962-970. doi:10.1001/jama.2021.1117

11. Kinsinger LS, Atkins D, Provenzale D, Anderson C, Petzel R. Implementation of a new screening recommendation in health care: the Veterans Health Administration’s approach to lung cancer screening. Ann Intern Med. 2014;161(8):597-598. doi:10.7326/M14-1070

12. Lowenstein LM, Godoy MCB, Erasmus JJ, et al. Implementing decision coaching for lung cancer screening in the low-dose computed tomography setting. JCO Oncol Pract. 2020;16(8):e703-e725. doi:10.1200/JOP.19.00453

13. American College of Radiology Committee on Lung-RADS. Lung-RADS assessment categories 2022. Published November 2022. Accessed July 3, 2023. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung-RADS-2022.pdf

14. Lowenstein LM, Richards VF, Leal VB, et al. A brief measure of smokers’ knowledge of lung cancer screening with low-dose computed tomography. Prev Med Rep. 2016;4:351-356. doi:10.1016/j.pmedr.2016.07.008

15. Elwyn G, Barr PJ, Grande SW, Thompson R, Walsh T, Ozanne EM. Developing CollaboRATE: a fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Educ Couns. 2013;93(1):102-107. doi:10.1016/j.pec.2013.05.009

16. Barr PJ, Thompson R, Walsh T, Grande SW, Ozanne EM, Elwyn G. The psychometric properties of CollaboRATE: a fast and frugal patient-reported measure of the shared decision-making process. J Med Internet Res. 2014;16(1):e2. doi:10.2196/jmir.3085

17. Légaré F, Kearing S, Clay K, et al. Are you SURE?: Assessing patient decisional conflict with a 4-item screening test. Can Fam Physician. 2010;56(8):e308-e314.

18. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243. doi:10.1148/radiol.2017161659

19. Wiener RS, Koppelman E, Bolton R, et al. Patient and clinician perspectives on shared decision-making in early adopting lung cancer screening programs: a qualitative study. J Gen Intern Med. 2018;33(7):1035-1042. doi:10.1007/s11606-018-4350-9

20. Zeliadt SB, Heffner JL, Sayre G, et al. Attitudes and perceptions about smoking cessation in the context of lung cancer screening. JAMA Intern Med. 2015;175(9):1530-1537. doi:10.1001/jamainternmed.2015.3558

21. Mazzone PJ, White CS, Kazerooni EA, Smith RA, Thomson CC. Proposed quality metrics for lung cancer screening programs: a National Lung Cancer Roundtable Project. Chest. 2021;160(1):368-378. doi:10.1016/j.chest.2021.01.063

22. Mazzone PJ, Tenenbaum A, Seeley M, et al. Impact of a lung cancer screening counseling and shared decision-making visit. Chest. 2017;151(3):572-578. doi:10.1016/j.chest.2016.10.027

23. Reuland DS, Cubillos L, Brenner AT, Harris RP, Minish B, Pignone MP. A pre-post study testing a lung cancer screening decision aid in primary care. BMC Med Inform Decis Mak. 2018;18(1):5. doi:10.1186/s12911-018-0582-1

24. Dharod A, Bellinger C, Foley K, Case LD, Miller D. The reach and feasibility of an interactive lung cancer screening decision aid delivered by patient portal. Appl Clin Inform. 2019;10(1):19-27. doi:10.1055/s-0038-1676807

25. Stacey D, Légaré F, Lewis K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431. doi:10.1002/14651858.CD001431.pub5

26. Tanner NT, Banas E, Yeager D, Dai L, Hughes Halbert C, Silvestri GA. In-person and telephonic shared decision-making visits for people considering lung cancer screening: an assessment of decision quality. Chest. 2019;155(1):236-238. doi:10.1016/j.chest.2018.07.046

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Author and Disclosure Information

Richard M. Hoffman, MD, MPHa,b,c; Julie A. Lang, RN, BSN, MBAd; George J. Baileyd; James A. Merchant, MSd;  Aaron S. Seaman, PhDa,b,c; Elizabeth A. Newbury, MAd; Rolando Sanchez, MD, MSa,b; Robert J. Volk, PhDe;  Lisa M. Lowenstein, PhDe; Sarah L. Averill, MDf

Correspondence:  Richard M. Hoffman  (richard-m-hoffman @uiowa.edu)

aIowa City Veterans Affairs Medical Center, Iowa

bUniversity of Iowa Carver College of Medicine, Iowa City

cHolden Comprehensive Cancer Center, University of Iowa, Iowa City

dVeterans Rural Health Resource Center, Office of Rural Health, Veterans Health Administration, Iowa City, Iowa

eThe University of Texas MD Anderson Cancer Center, HoustonfRoswell Park Comprehensive Cancer Center, Buffalo, New York

Author disclosures

The study was supported by a grant from the Office of Rural Health (ORH) (NOMAD #03526) awarded to Richard Hoffman. The funding body did not play a role in the design of the study or the collection and analysis of data. Lisa Lowenstein and Robert Volk are supported by a grant funded by the National Institutes of Health, National Cancer Institute, USA, under award number P30CA016672, using the Shared Decision-Making Core, and by a grant from the Cancer Prevention and Research Institute of Texas (RP160674). None of the other authors have any disclosures. None of the authors have conflicts of interest with the work.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.


Ethics and consent

The University of Iowa Hawk Institutional Review Board determined that this study did not include research on human subjects and was exempt from oversight.

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Author and Disclosure Information

Richard M. Hoffman, MD, MPHa,b,c; Julie A. Lang, RN, BSN, MBAd; George J. Baileyd; James A. Merchant, MSd;  Aaron S. Seaman, PhDa,b,c; Elizabeth A. Newbury, MAd; Rolando Sanchez, MD, MSa,b; Robert J. Volk, PhDe;  Lisa M. Lowenstein, PhDe; Sarah L. Averill, MDf

Correspondence:  Richard M. Hoffman  (richard-m-hoffman @uiowa.edu)

aIowa City Veterans Affairs Medical Center, Iowa

bUniversity of Iowa Carver College of Medicine, Iowa City

cHolden Comprehensive Cancer Center, University of Iowa, Iowa City

dVeterans Rural Health Resource Center, Office of Rural Health, Veterans Health Administration, Iowa City, Iowa

eThe University of Texas MD Anderson Cancer Center, HoustonfRoswell Park Comprehensive Cancer Center, Buffalo, New York

Author disclosures

The study was supported by a grant from the Office of Rural Health (ORH) (NOMAD #03526) awarded to Richard Hoffman. The funding body did not play a role in the design of the study or the collection and analysis of data. Lisa Lowenstein and Robert Volk are supported by a grant funded by the National Institutes of Health, National Cancer Institute, USA, under award number P30CA016672, using the Shared Decision-Making Core, and by a grant from the Cancer Prevention and Research Institute of Texas (RP160674). None of the other authors have any disclosures. None of the authors have conflicts of interest with the work.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.


Ethics and consent

The University of Iowa Hawk Institutional Review Board determined that this study did not include research on human subjects and was exempt from oversight.

Author and Disclosure Information

Richard M. Hoffman, MD, MPHa,b,c; Julie A. Lang, RN, BSN, MBAd; George J. Baileyd; James A. Merchant, MSd;  Aaron S. Seaman, PhDa,b,c; Elizabeth A. Newbury, MAd; Rolando Sanchez, MD, MSa,b; Robert J. Volk, PhDe;  Lisa M. Lowenstein, PhDe; Sarah L. Averill, MDf

Correspondence:  Richard M. Hoffman  (richard-m-hoffman @uiowa.edu)

aIowa City Veterans Affairs Medical Center, Iowa

bUniversity of Iowa Carver College of Medicine, Iowa City

cHolden Comprehensive Cancer Center, University of Iowa, Iowa City

dVeterans Rural Health Resource Center, Office of Rural Health, Veterans Health Administration, Iowa City, Iowa

eThe University of Texas MD Anderson Cancer Center, HoustonfRoswell Park Comprehensive Cancer Center, Buffalo, New York

Author disclosures

The study was supported by a grant from the Office of Rural Health (ORH) (NOMAD #03526) awarded to Richard Hoffman. The funding body did not play a role in the design of the study or the collection and analysis of data. Lisa Lowenstein and Robert Volk are supported by a grant funded by the National Institutes of Health, National Cancer Institute, USA, under award number P30CA016672, using the Shared Decision-Making Core, and by a grant from the Cancer Prevention and Research Institute of Texas (RP160674). None of the other authors have any disclosures. None of the authors have conflicts of interest with the work.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.


Ethics and consent

The University of Iowa Hawk Institutional Review Board determined that this study did not include research on human subjects and was exempt from oversight.

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Lung cancer is the second most frequently diagnosed cancer among US veterans and the leading cause of cancer death.1 Clinical trials have shown that annual screening of high-risk persons with low-dose computed tomography (LDCT) can reduce the risk of dying of lung cancer.2 In 2011, the National Lung Screening Trial (NLST) reported that over a 3-year period, annual LDCT screening reduced the risk of dying of lung cancer by 20% compared with chest radiograph screening.3 Lung cancer screening (LCS), however, was associated with harms, including false-positive results, complications from invasive diagnostic procedures, incidental findings, overdiagnosis, and radiation exposure.

The US Preventive Services Task Force (USPSTF) began recommending annual screening of high-risk persons after publication of the NLST results.4 The Veterans Health Administration (VHA) recommended implementing LCS in 2017.5 Guidelines, however, have consistently highlighted the complexity of the decision and the importance of engaging patients in thorough discussions about the potential benefits and harms of screening (shared decision making [SDM]). The Centers for Medicare and Medicaid Services (CMS) has issued coverage determinations mandating that eligible patients undergo a counseling visit that uses a decision aid to support SDM for LCS and addresses tobacco use.6,7 However, primary care practitioners (PCPs) face many challenges in delivering SDM, including a lack of awareness of clinical trial results and screening guidelines, competing clinical demands, being untrained in SDM, and not having educational resources.8 Patients in rural locations face travel burdens in attending counseling visits.9

We conducted a pilot study to address concerns with delivering SDM for LCS to veterans. We implemented a centralized screening model in which veterans were referred by clinicians to a trained decision coach who conducted telephone visits to discuss the initial LCS decision, addressed tobacco cessation, and placed LDCT orders. We evaluated the outcomes of this telemedicine visit by using decision quality metrics and tracking LCS uptake, referrals for tobacco cessation, and clinical outcomes. The University of Iowa Institutional Review Board considered this study to be a quality improvement project and waived informed consent and HIPAA (Health Insurance Portability and Accountability Act) authorization requirements.

 

 

Implementation

We implemented the LCS program at the Iowa City Veterans Affairs Health Care System (ICVAHCS), which has both resident and staff clinicians, and 2 community-based outpatient clinics (Coralville, Cedar Rapids) with staff clinicians. The pilot study, conducted from November 2020 through July 2022, was led by a multidisciplinary team that included a nurse, primary care physician, pulmonologist, and radiologist. The team conducted online presentations to educate PCPs about the epidemiology of lung cancer, results of screening trials, LCS guidelines, the rationale for a centralized model of SDM, and the ICVAHCS screening protocols.

Screening Referrals

When the study began in 2020, we used the 2015 USPSTF criteria for annual LCS: individuals aged 55 to 80 years with a 30 pack-year smoking history and current tobacco user or who had quit within 15 years.4 We lowered the starting age to 50 years and the pack-year requirement to 20 after the USPSTF issued updated guidelines in 2021.10 Clinicians were notified about potentially eligible patients through the US Department of Veterans Affairs (VA) Computerized Personal Record System (CPRS) reminders or by the nurse program coordinator (NPC) who reviewed health records of patients with upcoming appointments. If the clinician determined that screening was appropriate, they ordered an LCS consult. The NPC called the veteran to confirm eligibility, mailed a decision aid, and scheduled a telephone visit to conduct SDM. We used the VA decision aid developed for the LCS demonstration project conducted at 8 academic VA medical centers between 2013 and 2017.11

Shared Decision-Making Telephone Visit

The NPC adapted a telephone script developed for a Cancer Prevention and Research Institute of Texas–funded project conducted by 2 coauthors (RJV and LML).12 The NPC asked about receipt/review of the decision aid, described the screening process, and addressed benefits and potential harms of screening. The NPC also offered smoking cessation interventions for veterans who were currently smoking, including referrals to the VA patient aligned care team clinical pharmacist for management of tobacco cessation or to the national VA Quit Line. The encounter ended by assessing the veteran’s understanding of screening issues and eliciting the veteran’s preferences for LDCT and willingness to adhere with the LCS program.

LDCT Imaging

The NPC placed LDCT orders for veterans interested in screening and alerted the referring clinician to sign the order. Veterans who agreed to be screened were placed in an LCS dashboard developed by the Veterans Integrated Services Network (VISN) 23 LCS program that was used as a patient management tool. The dashboard allowed the NPC to track patients, ensuring that veterans were being scheduled for and completing initial and follow-up testing. Radiologists used the Lung-RADS (Lung Imaging Reporting and Data System) to categorize LDCT results (1, normal; 2, benign nodule; 3, probably benign nodule; 4, suspicious nodule).13 Veterans with Lung-RADS 1 or 2 results were scheduled for an annual LDCT (if they remained eligible). Veterans with Lung-RADS 3 results were scheduled for a 6-month follow-up CT. The screening program sent electronic consults to pulmonary for veterans with Lung-RADS 4 to determine whether they should undergo additional imaging or be evaluated in the pulmonary clinic.

 

 

Evaluating Shared Decision Making

We audio taped and transcribed randomly selected SDM encounters to assess fidelity with the 2016 CMS required discussion elements for counseling about lung cancer, including the benefit of reducing lung cancer mortality; the potential for harms from false alarms, incidental findings, overdiagnosis, and radiation exposure; the need for annual screening; the importance of smoking cessation; and the possibility of undergoing follow-up testing and diagnostic procedures. An investigator coded the transcripts to assess for the presence of each required element and scored the encounter from 0 to 7.

We also surveyed veterans completing SDM, using a convenience sampling strategy to evaluate knowledge, the quality of the SDM process, and decisional conflict. Initially, we sent mailed surveys to subjects to be completed 1 week after the SDM visit. To increase the response rate, we subsequently called patients to complete the surveys by telephone 1 week after the SDM visit.

We used the validated LCS-12 knowledge measure to assess awareness of lung cancer risks, screening eligibility, and the benefits and harms of screening.14 We evaluated the quality of the SDM visit by using the 3-item CollaboRATE scale (Table 1).15

table 1
The response items were scored on a 9-point Likert scale (0, no effort; 9, every effort). The CollaboRATE developers recommend reporting the top score (ie, the proportion of subjects whose response to all 3 questions was 9).16 We used the 4-item SURE scale to assess decisional conflict, a measure of uncertainty about choosing an option.17 A yes response received 1 point; patients with scores of 4 were considered to have no decisional conflict.

The NPC also took field notes during interviews to help identify additional SDM issues. After each call, the NPC noted her impressions of the veteran’s engagement with SDM and understanding of the screening issues.

Clinical Outcomes

We used the screening dashboard and CPRS to track clinical outcomes, including screening uptake, referrals for tobacco cessation, appropriate (screening or diagnostic) follow-up testing, and cancer diagnoses. We used descriptive statistics to characterize demographic data and survey responses.

Initial Findings

We conducted 105 SDM telephone visits from November 2020 through July 2022 (Table 2).

table 2
We audio taped 27 encounters. Measures of SDM showed good fidelity with addressing required CMS elements. The mean number of elements addressed was 6.2 of 7. Reduction in lung cancer mortality was the issue least likely to be addressed (59%).

We surveyed 47 of the veterans completing SDM visits (45%) and received 37 completed surveys (79%). All respondents were male, mean age 61.9 years, 89% White, 38% married/partnered, 70% rural, 65% currently smoking, with a mean 44.8 pack-years smoking history. On average, veterans answered 6.3 (53%) of knowledge questions correctly (Table 3).

table 3
They were most likely to correctly answer questions about the harms of radiation exposure (65%), false-positive results (84%), false-negative results (78%), and overdiagnosis (86%).

Only 1 respondent (3%) correctly answered the multiple-choice question about indications for stopping screening. Two (5%) correctly answered the question on the magnitude of benefit, most overestimated or did not know. Similarly, 23 (62%) overestimated or did not know the predictive value of an abnormal scan. About two-thirds of veterans underestimated or did not know the attributable risk of lung cancer from tobacco, and about four-fifths did not know the mortality rank of lung cancer. Among the 37 respondents, 31 (84%) indicated not having any decisional conflict as defined by a score of 4 on the SURE scale.
table 4
Overall, 59% of respondents had a top box score on the CollaboRATE scale. Ratings for individual domains ranged from 65% to 73% (Table 4).

 

 

Implementing SDM

The NPC’s field notes indicated that many veterans did not perceive any need to discuss the screening decision and believed that their PCP had referred them just for screening. However, they reported having cursory discussions with their PCP, being told that only their history of heavy tobacco use meant they should be screened. For veterans who had not read the decision aid, the NPC attempted to summarize benefits and harms. However, the discussions were often inadequate because the veterans were not interested in receiving information, particularly numerical data, or indicated that they had limited time for the call.

Seventy-two (69%) of the veterans who met with the NPC were currently smoking. Tobacco cessation counseling was offered to 66; 29 were referred to the VA Quit Line, 10 were referred to the tobacco cessation pharmacist, and the NPC contacted the PCPs for 9 patients who wanted prescriptions for nicotine replacement therapy.

After the SDM visit, 91 veterans (87%) agreed to screening. By the end of the study period, 73 veterans (80%) completed testing. Most veterans had Lung-RADS 1 or 2 results, 11 (1%) had a Lung-RADS 3, and 7 (10%) had a Lung-RADS 4. All 9 veterans with Lung-RADS 3 results and at least 6 months of follow-up underwent repeat imaging within 4 to 13 months (median, 7). All veterans with a Lung-RADS 4 result were referred to pulmonary. One patient was diagnosed with an early-stage non–small cell lung cancer.

We identified several problems with LDCT coding. Radiologists did not consistently use Lung-RADS when interpreting screening LDCTs; some used the Fleischner lung nodule criteria.18 We also found discordant readings for abnormal LDCTs, where the assigned Lung-RADS score was not consistent with the nodule description in the radiology report.

Discussion

Efforts to implement LCS with a telemedicine SDM intervention were mixed. An NPC-led SDM phone call was successfully incorporated into the clinical workflow. Most veterans identified as being eligible for screening participated in the counseling visit and underwent screening. However, they were often reluctant to engage in SDM, feeling that their clinician had already recommended screening and that there was no need for further discussion. Unfortunately, many veterans had not received or reviewed the decision aid and were not interested in receiving information about benefits and harms. Because we relied on telephone calls, we could not share visual information in real time.

Overall, the surveys indicated that most veterans were very satisfied with the quality of the discussion and reported feeling no decisional conflict. However, based on the NPC’s field notes and audio recordings, we believe that the responses may have reflected earlier discussions with the PCP that reportedly emphasized only the veteran’s eligibility for screening. The fidelity assessments indicated that the NPC consistently addressed the harms and benefits of screening.

Nonetheless, the performance on knowledge measures was uneven. Veterans were generally aware of harms, including false alarms, overdiagnosis, radiation exposure, and incidental findings. They did not, however, appreciate when screening should stop. They also underestimated the risks of developing lung cancer and the portion of that risk attributable to tobacco use, and overestimated the benefits of screening. These results suggest that the veterans, at least those who completed the surveys, may not be making well-informed decisions.

Our findings echo those of other VA investigators in finding knowledge deficits among screened veterans, including being unaware that LDCT was for LCS, believing that screening could prevent cancer, receiving little information about screening harms, and feeling that negative tests meant they were among the “lucky ones” who would avoid harm from continued smoking.19,20

The VA is currently implementing centralized screening models with the Lung Precision Oncology Program and the VA partnership to increase access to lung screening (VA-PALS).5 The centralized model, which readily supports the tracking, monitoring, and reporting needs of a screening program, also has advantages in delivering SDM because counselors have been trained in SDM, are more familiar with LCS evidence and processes, can better incorporate decision tools, and do not face the same time constraints as clinicians.21 However, studies have shown that most patients have already decided to be screened when they show up for the SDM visit.22 In contrast, about one-third of patients in primary care settings who receive decision support chose not to be screened.23,24 We found that 13% of our patients decided against screening after a telephone discussion, suggesting that a virtually conducted SDM visit can meaningfully support decision making. Telemedicine also may reduce health inequities in centralized models arising from patients having limited access to screening centers.

Our results suggest that PCPs referring patients to a centralized program, even for virtual visits, should frame the decision to initiate LCS as SDM, where an informed patient is being supported in making a decision consistent with their values and preferences. Furthermore, engaging patients in SDM should not be construed as endorsing screening. When centralized support is less available, individual clinics may need to provide SDM, perhaps using a nonclinician decision coach if clinicians lack the time to lead the discussions. Decision coaches have been effectively used to increase patients’ knowledge about the benefits and harms of screening.12 Regardless of the program model, PCPs will also be responsible for determining whether patients are healthy enough to undergo invasive diagnostic testing and treatment and ensuring that tobacco use is addressed.

SDM delivered in any setting will be enhanced by ensuring that patients are provided with decision aids before a counseling visit. This will help them better understand the benefits and harms of screening and the need to elicit values. The discussion can then focus on areas of concern or questions raised by reviewing the decision aid. The clinician and patient could also use a decision aid during either a face-to-face or video clinical encounter to facilitate SDM. A Cochrane review has shown that using decision aids for people facing screening decisions increases knowledge, reduces decisional conflict, and effectively elicits values and preferences.25 Providing high-quality decision support is a patient-centered approach that respects a patient’s autonomy and may promote health equity and improve adherence.

We recognized the importance of having a multidisciplinary team, involving primary care, radiology, pulmonary, and nursing, with a shared understanding of the screening processes. These are essential features for a high-quality screening program where eligible veterans are readily identified and receive prompt and appropriate follow-up. Radiologists need to use Lung-RADS categories consistently and appropriately when reading LDCTs. This may require ongoing educational efforts, particularly given the new CMS guidelines accepting nonsubspecialist chest readers.7 Additionally, fellows and board-eligible residents may interpret images in academic settings and at VA facilities. The program needs to work closely with the pulmonary service to ensure that Lung-RADS 4 patients are promptly assessed. Radiologists and pulmonologists should calibrate the application of Lung-RADS categories to pulmonary nodules through jointly participating in meetings to review selected cases.

 

 

Challenges and Limitations

We faced some notable implementation challenges. The COVID-19 pandemic was extremely disruptive to LCS as it was to all health care. In addition, screening workflow processes were hampered by a lack of clinical reminders, which ideally would trigger for clinicians based on the tobacco history. The absence of this reminder meant that numerous patients were found to be ineligible for screening. We have a long-standing lung nodule clinic, and clinicians were confused about whether to order a surveillance imaging for an incidental nodule or a screening LDCT.

The radiology service was able to update order sets in CPRS to help guide clinicians in distinguishing indications and prerequisites for enrolling in LCS. This helped reduce the number of inappropriate orders and crossover orders between the VISN nodule tracking program and the LCS program.

Our results were preliminary and based on a small sample. We did not survey all veterans who underwent SDM, though the response rate was 79% and patient characteristics were similar to the larger cohort. Our results were potentially subject to selection bias, which could inflate the positive responses about decision quality and decisional conflict. However, the knowledge deficits are likely to be valid and suggest a need to better inform eligible veterans about the benefits and harms of screening. We did not have sufficient follow-up time to determine whether veterans were adherent to annual screenings. We showed that almost all those with abnormal imaging results completed diagnostic evaluations and/or were evaluated by pulmonary. As the program matures, we will be able to track outcomes related to cancer diagnoses and treatment.

Conclusions

A centralized LCS program was able to deliver SDM and enroll veterans in a screening program. While veterans were confident in their decision to screen and felt that they participated in decision making, knowledge testing indicated important deficits. Furthermore, we observed that many veterans did not meaningfully engage in SDM. Clinicians will need to frame the decision as patient centered at the time of referral, highlight the role of the NPC and importance of SDM, and be able to provide adequate decision support. The SDM visits can be enhanced by ensuring that veterans are able to review decision aids. Telemedicine is an acceptable and effective approach for supporting screening discussions, particularly for rural veterans.26

Acknowledgments

The authors thank the following individuals for their contributions to the study: John Paul Hornbeck, program support specialist; Kelly Miell, PhD; Bradley Mecham, PhD; Christopher C. Richards, MA; Bailey Noble, NP; Rebecca Barnhart, program analyst.

Lung cancer is the second most frequently diagnosed cancer among US veterans and the leading cause of cancer death.1 Clinical trials have shown that annual screening of high-risk persons with low-dose computed tomography (LDCT) can reduce the risk of dying of lung cancer.2 In 2011, the National Lung Screening Trial (NLST) reported that over a 3-year period, annual LDCT screening reduced the risk of dying of lung cancer by 20% compared with chest radiograph screening.3 Lung cancer screening (LCS), however, was associated with harms, including false-positive results, complications from invasive diagnostic procedures, incidental findings, overdiagnosis, and radiation exposure.

The US Preventive Services Task Force (USPSTF) began recommending annual screening of high-risk persons after publication of the NLST results.4 The Veterans Health Administration (VHA) recommended implementing LCS in 2017.5 Guidelines, however, have consistently highlighted the complexity of the decision and the importance of engaging patients in thorough discussions about the potential benefits and harms of screening (shared decision making [SDM]). The Centers for Medicare and Medicaid Services (CMS) has issued coverage determinations mandating that eligible patients undergo a counseling visit that uses a decision aid to support SDM for LCS and addresses tobacco use.6,7 However, primary care practitioners (PCPs) face many challenges in delivering SDM, including a lack of awareness of clinical trial results and screening guidelines, competing clinical demands, being untrained in SDM, and not having educational resources.8 Patients in rural locations face travel burdens in attending counseling visits.9

We conducted a pilot study to address concerns with delivering SDM for LCS to veterans. We implemented a centralized screening model in which veterans were referred by clinicians to a trained decision coach who conducted telephone visits to discuss the initial LCS decision, addressed tobacco cessation, and placed LDCT orders. We evaluated the outcomes of this telemedicine visit by using decision quality metrics and tracking LCS uptake, referrals for tobacco cessation, and clinical outcomes. The University of Iowa Institutional Review Board considered this study to be a quality improvement project and waived informed consent and HIPAA (Health Insurance Portability and Accountability Act) authorization requirements.

 

 

Implementation

We implemented the LCS program at the Iowa City Veterans Affairs Health Care System (ICVAHCS), which has both resident and staff clinicians, and 2 community-based outpatient clinics (Coralville, Cedar Rapids) with staff clinicians. The pilot study, conducted from November 2020 through July 2022, was led by a multidisciplinary team that included a nurse, primary care physician, pulmonologist, and radiologist. The team conducted online presentations to educate PCPs about the epidemiology of lung cancer, results of screening trials, LCS guidelines, the rationale for a centralized model of SDM, and the ICVAHCS screening protocols.

Screening Referrals

When the study began in 2020, we used the 2015 USPSTF criteria for annual LCS: individuals aged 55 to 80 years with a 30 pack-year smoking history and current tobacco user or who had quit within 15 years.4 We lowered the starting age to 50 years and the pack-year requirement to 20 after the USPSTF issued updated guidelines in 2021.10 Clinicians were notified about potentially eligible patients through the US Department of Veterans Affairs (VA) Computerized Personal Record System (CPRS) reminders or by the nurse program coordinator (NPC) who reviewed health records of patients with upcoming appointments. If the clinician determined that screening was appropriate, they ordered an LCS consult. The NPC called the veteran to confirm eligibility, mailed a decision aid, and scheduled a telephone visit to conduct SDM. We used the VA decision aid developed for the LCS demonstration project conducted at 8 academic VA medical centers between 2013 and 2017.11

Shared Decision-Making Telephone Visit

The NPC adapted a telephone script developed for a Cancer Prevention and Research Institute of Texas–funded project conducted by 2 coauthors (RJV and LML).12 The NPC asked about receipt/review of the decision aid, described the screening process, and addressed benefits and potential harms of screening. The NPC also offered smoking cessation interventions for veterans who were currently smoking, including referrals to the VA patient aligned care team clinical pharmacist for management of tobacco cessation or to the national VA Quit Line. The encounter ended by assessing the veteran’s understanding of screening issues and eliciting the veteran’s preferences for LDCT and willingness to adhere with the LCS program.

LDCT Imaging

The NPC placed LDCT orders for veterans interested in screening and alerted the referring clinician to sign the order. Veterans who agreed to be screened were placed in an LCS dashboard developed by the Veterans Integrated Services Network (VISN) 23 LCS program that was used as a patient management tool. The dashboard allowed the NPC to track patients, ensuring that veterans were being scheduled for and completing initial and follow-up testing. Radiologists used the Lung-RADS (Lung Imaging Reporting and Data System) to categorize LDCT results (1, normal; 2, benign nodule; 3, probably benign nodule; 4, suspicious nodule).13 Veterans with Lung-RADS 1 or 2 results were scheduled for an annual LDCT (if they remained eligible). Veterans with Lung-RADS 3 results were scheduled for a 6-month follow-up CT. The screening program sent electronic consults to pulmonary for veterans with Lung-RADS 4 to determine whether they should undergo additional imaging or be evaluated in the pulmonary clinic.

 

 

Evaluating Shared Decision Making

We audio taped and transcribed randomly selected SDM encounters to assess fidelity with the 2016 CMS required discussion elements for counseling about lung cancer, including the benefit of reducing lung cancer mortality; the potential for harms from false alarms, incidental findings, overdiagnosis, and radiation exposure; the need for annual screening; the importance of smoking cessation; and the possibility of undergoing follow-up testing and diagnostic procedures. An investigator coded the transcripts to assess for the presence of each required element and scored the encounter from 0 to 7.

We also surveyed veterans completing SDM, using a convenience sampling strategy to evaluate knowledge, the quality of the SDM process, and decisional conflict. Initially, we sent mailed surveys to subjects to be completed 1 week after the SDM visit. To increase the response rate, we subsequently called patients to complete the surveys by telephone 1 week after the SDM visit.

We used the validated LCS-12 knowledge measure to assess awareness of lung cancer risks, screening eligibility, and the benefits and harms of screening.14 We evaluated the quality of the SDM visit by using the 3-item CollaboRATE scale (Table 1).15

table 1
The response items were scored on a 9-point Likert scale (0, no effort; 9, every effort). The CollaboRATE developers recommend reporting the top score (ie, the proportion of subjects whose response to all 3 questions was 9).16 We used the 4-item SURE scale to assess decisional conflict, a measure of uncertainty about choosing an option.17 A yes response received 1 point; patients with scores of 4 were considered to have no decisional conflict.

The NPC also took field notes during interviews to help identify additional SDM issues. After each call, the NPC noted her impressions of the veteran’s engagement with SDM and understanding of the screening issues.

Clinical Outcomes

We used the screening dashboard and CPRS to track clinical outcomes, including screening uptake, referrals for tobacco cessation, appropriate (screening or diagnostic) follow-up testing, and cancer diagnoses. We used descriptive statistics to characterize demographic data and survey responses.

Initial Findings

We conducted 105 SDM telephone visits from November 2020 through July 2022 (Table 2).

table 2
We audio taped 27 encounters. Measures of SDM showed good fidelity with addressing required CMS elements. The mean number of elements addressed was 6.2 of 7. Reduction in lung cancer mortality was the issue least likely to be addressed (59%).

We surveyed 47 of the veterans completing SDM visits (45%) and received 37 completed surveys (79%). All respondents were male, mean age 61.9 years, 89% White, 38% married/partnered, 70% rural, 65% currently smoking, with a mean 44.8 pack-years smoking history. On average, veterans answered 6.3 (53%) of knowledge questions correctly (Table 3).

table 3
They were most likely to correctly answer questions about the harms of radiation exposure (65%), false-positive results (84%), false-negative results (78%), and overdiagnosis (86%).

Only 1 respondent (3%) correctly answered the multiple-choice question about indications for stopping screening. Two (5%) correctly answered the question on the magnitude of benefit, most overestimated or did not know. Similarly, 23 (62%) overestimated or did not know the predictive value of an abnormal scan. About two-thirds of veterans underestimated or did not know the attributable risk of lung cancer from tobacco, and about four-fifths did not know the mortality rank of lung cancer. Among the 37 respondents, 31 (84%) indicated not having any decisional conflict as defined by a score of 4 on the SURE scale.
table 4
Overall, 59% of respondents had a top box score on the CollaboRATE scale. Ratings for individual domains ranged from 65% to 73% (Table 4).

 

 

Implementing SDM

The NPC’s field notes indicated that many veterans did not perceive any need to discuss the screening decision and believed that their PCP had referred them just for screening. However, they reported having cursory discussions with their PCP, being told that only their history of heavy tobacco use meant they should be screened. For veterans who had not read the decision aid, the NPC attempted to summarize benefits and harms. However, the discussions were often inadequate because the veterans were not interested in receiving information, particularly numerical data, or indicated that they had limited time for the call.

Seventy-two (69%) of the veterans who met with the NPC were currently smoking. Tobacco cessation counseling was offered to 66; 29 were referred to the VA Quit Line, 10 were referred to the tobacco cessation pharmacist, and the NPC contacted the PCPs for 9 patients who wanted prescriptions for nicotine replacement therapy.

After the SDM visit, 91 veterans (87%) agreed to screening. By the end of the study period, 73 veterans (80%) completed testing. Most veterans had Lung-RADS 1 or 2 results, 11 (1%) had a Lung-RADS 3, and 7 (10%) had a Lung-RADS 4. All 9 veterans with Lung-RADS 3 results and at least 6 months of follow-up underwent repeat imaging within 4 to 13 months (median, 7). All veterans with a Lung-RADS 4 result were referred to pulmonary. One patient was diagnosed with an early-stage non–small cell lung cancer.

We identified several problems with LDCT coding. Radiologists did not consistently use Lung-RADS when interpreting screening LDCTs; some used the Fleischner lung nodule criteria.18 We also found discordant readings for abnormal LDCTs, where the assigned Lung-RADS score was not consistent with the nodule description in the radiology report.

Discussion

Efforts to implement LCS with a telemedicine SDM intervention were mixed. An NPC-led SDM phone call was successfully incorporated into the clinical workflow. Most veterans identified as being eligible for screening participated in the counseling visit and underwent screening. However, they were often reluctant to engage in SDM, feeling that their clinician had already recommended screening and that there was no need for further discussion. Unfortunately, many veterans had not received or reviewed the decision aid and were not interested in receiving information about benefits and harms. Because we relied on telephone calls, we could not share visual information in real time.

Overall, the surveys indicated that most veterans were very satisfied with the quality of the discussion and reported feeling no decisional conflict. However, based on the NPC’s field notes and audio recordings, we believe that the responses may have reflected earlier discussions with the PCP that reportedly emphasized only the veteran’s eligibility for screening. The fidelity assessments indicated that the NPC consistently addressed the harms and benefits of screening.

Nonetheless, the performance on knowledge measures was uneven. Veterans were generally aware of harms, including false alarms, overdiagnosis, radiation exposure, and incidental findings. They did not, however, appreciate when screening should stop. They also underestimated the risks of developing lung cancer and the portion of that risk attributable to tobacco use, and overestimated the benefits of screening. These results suggest that the veterans, at least those who completed the surveys, may not be making well-informed decisions.

Our findings echo those of other VA investigators in finding knowledge deficits among screened veterans, including being unaware that LDCT was for LCS, believing that screening could prevent cancer, receiving little information about screening harms, and feeling that negative tests meant they were among the “lucky ones” who would avoid harm from continued smoking.19,20

The VA is currently implementing centralized screening models with the Lung Precision Oncology Program and the VA partnership to increase access to lung screening (VA-PALS).5 The centralized model, which readily supports the tracking, monitoring, and reporting needs of a screening program, also has advantages in delivering SDM because counselors have been trained in SDM, are more familiar with LCS evidence and processes, can better incorporate decision tools, and do not face the same time constraints as clinicians.21 However, studies have shown that most patients have already decided to be screened when they show up for the SDM visit.22 In contrast, about one-third of patients in primary care settings who receive decision support chose not to be screened.23,24 We found that 13% of our patients decided against screening after a telephone discussion, suggesting that a virtually conducted SDM visit can meaningfully support decision making. Telemedicine also may reduce health inequities in centralized models arising from patients having limited access to screening centers.

Our results suggest that PCPs referring patients to a centralized program, even for virtual visits, should frame the decision to initiate LCS as SDM, where an informed patient is being supported in making a decision consistent with their values and preferences. Furthermore, engaging patients in SDM should not be construed as endorsing screening. When centralized support is less available, individual clinics may need to provide SDM, perhaps using a nonclinician decision coach if clinicians lack the time to lead the discussions. Decision coaches have been effectively used to increase patients’ knowledge about the benefits and harms of screening.12 Regardless of the program model, PCPs will also be responsible for determining whether patients are healthy enough to undergo invasive diagnostic testing and treatment and ensuring that tobacco use is addressed.

SDM delivered in any setting will be enhanced by ensuring that patients are provided with decision aids before a counseling visit. This will help them better understand the benefits and harms of screening and the need to elicit values. The discussion can then focus on areas of concern or questions raised by reviewing the decision aid. The clinician and patient could also use a decision aid during either a face-to-face or video clinical encounter to facilitate SDM. A Cochrane review has shown that using decision aids for people facing screening decisions increases knowledge, reduces decisional conflict, and effectively elicits values and preferences.25 Providing high-quality decision support is a patient-centered approach that respects a patient’s autonomy and may promote health equity and improve adherence.

We recognized the importance of having a multidisciplinary team, involving primary care, radiology, pulmonary, and nursing, with a shared understanding of the screening processes. These are essential features for a high-quality screening program where eligible veterans are readily identified and receive prompt and appropriate follow-up. Radiologists need to use Lung-RADS categories consistently and appropriately when reading LDCTs. This may require ongoing educational efforts, particularly given the new CMS guidelines accepting nonsubspecialist chest readers.7 Additionally, fellows and board-eligible residents may interpret images in academic settings and at VA facilities. The program needs to work closely with the pulmonary service to ensure that Lung-RADS 4 patients are promptly assessed. Radiologists and pulmonologists should calibrate the application of Lung-RADS categories to pulmonary nodules through jointly participating in meetings to review selected cases.

 

 

Challenges and Limitations

We faced some notable implementation challenges. The COVID-19 pandemic was extremely disruptive to LCS as it was to all health care. In addition, screening workflow processes were hampered by a lack of clinical reminders, which ideally would trigger for clinicians based on the tobacco history. The absence of this reminder meant that numerous patients were found to be ineligible for screening. We have a long-standing lung nodule clinic, and clinicians were confused about whether to order a surveillance imaging for an incidental nodule or a screening LDCT.

The radiology service was able to update order sets in CPRS to help guide clinicians in distinguishing indications and prerequisites for enrolling in LCS. This helped reduce the number of inappropriate orders and crossover orders between the VISN nodule tracking program and the LCS program.

Our results were preliminary and based on a small sample. We did not survey all veterans who underwent SDM, though the response rate was 79% and patient characteristics were similar to the larger cohort. Our results were potentially subject to selection bias, which could inflate the positive responses about decision quality and decisional conflict. However, the knowledge deficits are likely to be valid and suggest a need to better inform eligible veterans about the benefits and harms of screening. We did not have sufficient follow-up time to determine whether veterans were adherent to annual screenings. We showed that almost all those with abnormal imaging results completed diagnostic evaluations and/or were evaluated by pulmonary. As the program matures, we will be able to track outcomes related to cancer diagnoses and treatment.

Conclusions

A centralized LCS program was able to deliver SDM and enroll veterans in a screening program. While veterans were confident in their decision to screen and felt that they participated in decision making, knowledge testing indicated important deficits. Furthermore, we observed that many veterans did not meaningfully engage in SDM. Clinicians will need to frame the decision as patient centered at the time of referral, highlight the role of the NPC and importance of SDM, and be able to provide adequate decision support. The SDM visits can be enhanced by ensuring that veterans are able to review decision aids. Telemedicine is an acceptable and effective approach for supporting screening discussions, particularly for rural veterans.26

Acknowledgments

The authors thank the following individuals for their contributions to the study: John Paul Hornbeck, program support specialist; Kelly Miell, PhD; Bradley Mecham, PhD; Christopher C. Richards, MA; Bailey Noble, NP; Rebecca Barnhart, program analyst.

References

1. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701. doi:10.7205/milmed-d-11-00434

2. Hoffman RM, Atallah RP, Struble RD, Badgett RG. Lung cancer screening with low-dose CT: a meta-analysis. J Gen Intern Med. 2020;35(10):3015-3025. doi:10.1007/s11606-020-05951-7

3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. doi:10.1056/NEJMoa1102873

4. Moyer VA, US Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338. doi:10.7326/M13-2771

5. Maurice NM, Tanner NT. Lung cancer screening at the VA: past, present and future. Semin Oncol. 2022;S0093-7754(22)00041-0. doi:10.1053/j.seminoncol.2022.06.001

6. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439N). Published 2015. Accessed July 10, 2023. http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274

7. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439R). Published 2022. Accessed July 10, 2023. https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304

8. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; National Cancer Policy Forum. Implementation of Lung Cancer Screening: Proceedings of a Workshop. The National Academies Press; November 17, 2016. doi:10.172216/23680

9. Bernstein E, Bade BC, Akgün KM, Rose MG, Cain HC. Barriers and facilitators to lung cancer screening and follow-up. Semin Oncol. 2022;S0093-7754(22)00058-6. doi:10.1053/j.seminoncol.2022.07.004

10. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(10):962-970. doi:10.1001/jama.2021.1117

11. Kinsinger LS, Atkins D, Provenzale D, Anderson C, Petzel R. Implementation of a new screening recommendation in health care: the Veterans Health Administration’s approach to lung cancer screening. Ann Intern Med. 2014;161(8):597-598. doi:10.7326/M14-1070

12. Lowenstein LM, Godoy MCB, Erasmus JJ, et al. Implementing decision coaching for lung cancer screening in the low-dose computed tomography setting. JCO Oncol Pract. 2020;16(8):e703-e725. doi:10.1200/JOP.19.00453

13. American College of Radiology Committee on Lung-RADS. Lung-RADS assessment categories 2022. Published November 2022. Accessed July 3, 2023. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung-RADS-2022.pdf

14. Lowenstein LM, Richards VF, Leal VB, et al. A brief measure of smokers’ knowledge of lung cancer screening with low-dose computed tomography. Prev Med Rep. 2016;4:351-356. doi:10.1016/j.pmedr.2016.07.008

15. Elwyn G, Barr PJ, Grande SW, Thompson R, Walsh T, Ozanne EM. Developing CollaboRATE: a fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Educ Couns. 2013;93(1):102-107. doi:10.1016/j.pec.2013.05.009

16. Barr PJ, Thompson R, Walsh T, Grande SW, Ozanne EM, Elwyn G. The psychometric properties of CollaboRATE: a fast and frugal patient-reported measure of the shared decision-making process. J Med Internet Res. 2014;16(1):e2. doi:10.2196/jmir.3085

17. Légaré F, Kearing S, Clay K, et al. Are you SURE?: Assessing patient decisional conflict with a 4-item screening test. Can Fam Physician. 2010;56(8):e308-e314.

18. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243. doi:10.1148/radiol.2017161659

19. Wiener RS, Koppelman E, Bolton R, et al. Patient and clinician perspectives on shared decision-making in early adopting lung cancer screening programs: a qualitative study. J Gen Intern Med. 2018;33(7):1035-1042. doi:10.1007/s11606-018-4350-9

20. Zeliadt SB, Heffner JL, Sayre G, et al. Attitudes and perceptions about smoking cessation in the context of lung cancer screening. JAMA Intern Med. 2015;175(9):1530-1537. doi:10.1001/jamainternmed.2015.3558

21. Mazzone PJ, White CS, Kazerooni EA, Smith RA, Thomson CC. Proposed quality metrics for lung cancer screening programs: a National Lung Cancer Roundtable Project. Chest. 2021;160(1):368-378. doi:10.1016/j.chest.2021.01.063

22. Mazzone PJ, Tenenbaum A, Seeley M, et al. Impact of a lung cancer screening counseling and shared decision-making visit. Chest. 2017;151(3):572-578. doi:10.1016/j.chest.2016.10.027

23. Reuland DS, Cubillos L, Brenner AT, Harris RP, Minish B, Pignone MP. A pre-post study testing a lung cancer screening decision aid in primary care. BMC Med Inform Decis Mak. 2018;18(1):5. doi:10.1186/s12911-018-0582-1

24. Dharod A, Bellinger C, Foley K, Case LD, Miller D. The reach and feasibility of an interactive lung cancer screening decision aid delivered by patient portal. Appl Clin Inform. 2019;10(1):19-27. doi:10.1055/s-0038-1676807

25. Stacey D, Légaré F, Lewis K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431. doi:10.1002/14651858.CD001431.pub5

26. Tanner NT, Banas E, Yeager D, Dai L, Hughes Halbert C, Silvestri GA. In-person and telephonic shared decision-making visits for people considering lung cancer screening: an assessment of decision quality. Chest. 2019;155(1):236-238. doi:10.1016/j.chest.2018.07.046

References

1. Zullig LL, Jackson GL, Dorn RA, et al. Cancer incidence among patients of the U.S. Veterans Affairs Health Care System. Mil Med. 2012;177(6):693-701. doi:10.7205/milmed-d-11-00434

2. Hoffman RM, Atallah RP, Struble RD, Badgett RG. Lung cancer screening with low-dose CT: a meta-analysis. J Gen Intern Med. 2020;35(10):3015-3025. doi:10.1007/s11606-020-05951-7

3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. doi:10.1056/NEJMoa1102873

4. Moyer VA, US Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330-338. doi:10.7326/M13-2771

5. Maurice NM, Tanner NT. Lung cancer screening at the VA: past, present and future. Semin Oncol. 2022;S0093-7754(22)00041-0. doi:10.1053/j.seminoncol.2022.06.001

6. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439N). Published 2015. Accessed July 10, 2023. http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo.aspx?NCAId=274

7. Centers for Medicare & Medicaid Services. Screening for lung cancer with low dose computed tomography (LDCT) (CAG-00439R). Published 2022. Accessed July 10, 2023. https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304

8. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; National Cancer Policy Forum. Implementation of Lung Cancer Screening: Proceedings of a Workshop. The National Academies Press; November 17, 2016. doi:10.172216/23680

9. Bernstein E, Bade BC, Akgün KM, Rose MG, Cain HC. Barriers and facilitators to lung cancer screening and follow-up. Semin Oncol. 2022;S0093-7754(22)00058-6. doi:10.1053/j.seminoncol.2022.07.004

10. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(10):962-970. doi:10.1001/jama.2021.1117

11. Kinsinger LS, Atkins D, Provenzale D, Anderson C, Petzel R. Implementation of a new screening recommendation in health care: the Veterans Health Administration’s approach to lung cancer screening. Ann Intern Med. 2014;161(8):597-598. doi:10.7326/M14-1070

12. Lowenstein LM, Godoy MCB, Erasmus JJ, et al. Implementing decision coaching for lung cancer screening in the low-dose computed tomography setting. JCO Oncol Pract. 2020;16(8):e703-e725. doi:10.1200/JOP.19.00453

13. American College of Radiology Committee on Lung-RADS. Lung-RADS assessment categories 2022. Published November 2022. Accessed July 3, 2023. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung-RADS-2022.pdf

14. Lowenstein LM, Richards VF, Leal VB, et al. A brief measure of smokers’ knowledge of lung cancer screening with low-dose computed tomography. Prev Med Rep. 2016;4:351-356. doi:10.1016/j.pmedr.2016.07.008

15. Elwyn G, Barr PJ, Grande SW, Thompson R, Walsh T, Ozanne EM. Developing CollaboRATE: a fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Educ Couns. 2013;93(1):102-107. doi:10.1016/j.pec.2013.05.009

16. Barr PJ, Thompson R, Walsh T, Grande SW, Ozanne EM, Elwyn G. The psychometric properties of CollaboRATE: a fast and frugal patient-reported measure of the shared decision-making process. J Med Internet Res. 2014;16(1):e2. doi:10.2196/jmir.3085

17. Légaré F, Kearing S, Clay K, et al. Are you SURE?: Assessing patient decisional conflict with a 4-item screening test. Can Fam Physician. 2010;56(8):e308-e314.

18. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243. doi:10.1148/radiol.2017161659

19. Wiener RS, Koppelman E, Bolton R, et al. Patient and clinician perspectives on shared decision-making in early adopting lung cancer screening programs: a qualitative study. J Gen Intern Med. 2018;33(7):1035-1042. doi:10.1007/s11606-018-4350-9

20. Zeliadt SB, Heffner JL, Sayre G, et al. Attitudes and perceptions about smoking cessation in the context of lung cancer screening. JAMA Intern Med. 2015;175(9):1530-1537. doi:10.1001/jamainternmed.2015.3558

21. Mazzone PJ, White CS, Kazerooni EA, Smith RA, Thomson CC. Proposed quality metrics for lung cancer screening programs: a National Lung Cancer Roundtable Project. Chest. 2021;160(1):368-378. doi:10.1016/j.chest.2021.01.063

22. Mazzone PJ, Tenenbaum A, Seeley M, et al. Impact of a lung cancer screening counseling and shared decision-making visit. Chest. 2017;151(3):572-578. doi:10.1016/j.chest.2016.10.027

23. Reuland DS, Cubillos L, Brenner AT, Harris RP, Minish B, Pignone MP. A pre-post study testing a lung cancer screening decision aid in primary care. BMC Med Inform Decis Mak. 2018;18(1):5. doi:10.1186/s12911-018-0582-1

24. Dharod A, Bellinger C, Foley K, Case LD, Miller D. The reach and feasibility of an interactive lung cancer screening decision aid delivered by patient portal. Appl Clin Inform. 2019;10(1):19-27. doi:10.1055/s-0038-1676807

25. Stacey D, Légaré F, Lewis K, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431. doi:10.1002/14651858.CD001431.pub5

26. Tanner NT, Banas E, Yeager D, Dai L, Hughes Halbert C, Silvestri GA. In-person and telephonic shared decision-making visits for people considering lung cancer screening: an assessment of decision quality. Chest. 2019;155(1):236-238. doi:10.1016/j.chest.2018.07.046

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