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Affordable Care Act Implementation
At the Centers for Medicare and Medicaid Services (CMS), we are charged with implementing many of the major provisions of the Affordable Care Act (ACA). Major policies and programs aimed at transforming the way care is delivered and paid for, testing and scaling innovative delivery system reforms, and expanding the number of Americans with health insurance will now move forward. The healthcare system is moving from paying for volume to paying for value. Hospitals and clinicians will need to be able to manage and be accountable for populations of patients and improving health outcomes. In this article, we highlight 4 broad provisions of the ACA that are either already implemented or under development for implementation in 2014, and are anticipated to have widespread impact on our health system. The potential impacts of each provision on hospitals and hospitalists are outlined in Table 1.
| Affordable Care Act Provision | Example of Potential Impacts on Hospitals and Hospitalists |
|---|---|
| |
| Expansion of insurance coverage | Care for fewer uninsured patients/fewer unreimbursed services |
| Patients have improved access to services after discharge | |
| Shorter lengths of stay due to better access to outpatient services and care | |
| Delivery system transformation | Financial incentives aligned between inpatient and outpatient providers to better coordinate care |
| Payment is at risk if performance rates do not meet benchmarks and if costs are not lowered | |
| Consolidation of hospitals and health systems within local markets | |
| Value‐based purchasing | Medicare FFS reimbursement increased or decreased based on quality and cost measure results |
| Opportunity to align incentives between hospitals and hospitalists | |
| Patient‐centered outcomes research | Emerging research on delivery system interventions relevant to hospitalists, such as care transitions |
| Funding for PCOR available for hospitalist researchers interested in delivery systems and outcomes research | |
EXPANSION OF INSURANCE COVERAGE
The central and perhaps most anticipated provision of the ACA is the expansion of insurance to the currently uninsured through the creation of state‐based health insurance exchanges. The exchanges are a competitive marketplace for purchasing private insurance products by individuals and small and large businesses. The individual mandate that accompanies the exchange provision requires that individuals purchase insurance. For those who cannot afford it, the government provides a subsidy. Any health plan that wishes to participate in an exchange marketplace must include at minimum a package of essential health benefits in each of their insurance products, which include benefits such as ambulatory care services, maternal and newborn services, and prescription drugs.[1] Importantly, health plans are required to implement quality improvement strategies and publicly report quality data. The ACA also requires the Secretary of Health and Human Services (HHS) to develop and administer a quality rating system and an enrollee satisfaction survey system, the results of which will be available to exchange consumers. All of these requirements will promote the delivery of high‐quality healthcare to millions of previously uninsured Americans.
Implementation of the exchanges in combination with the expansion of Medicaid is expected to provide insurance to approximately 30 million people who currently lack coverage. Prior to the Supreme Court ruling in June of 2012, states were required to expand Medicaid eligibility to a minimum of 133% of the federal poverty level. This expansion is subsidized 100% by the federal government through 2016, dropping to 90% by 2020. The Supreme Court ruled that the federal government could not require states to expand their Medicaid rolls, although it is expected that most states will do so given the generous federal subsidy and the significant cost to states, hospitals, and society to provide healthcare to the uninsured.
TRANSFORMATION OF HEALTHCARE DELIVERY
In addition to the expansion of insurance coverage, the ACA initiates a transformation in the way that healthcare will be delivered through the testing and implementation of innovative payment and care delivery models. The ACA authorized the creation of the Center for Medicare and Medicaid Innovation (CMMI, or The Innovation Center) within CMS. Payment and care delivery demonstrations or pilots that demonstrate a high quality of care at lower costs can be scaled up nationally at the discretion of the Secretary, rather than requiring authorization by Congress. The Innovation Center has already launched initiatives that test a variety of new models of care, all of which incentivize care coordination, provision of team‐based care, and use of data and quality metrics to drive systems‐based improvement. These programs include pilots that bundle payments to hospitals, physician group practices, and post‐acute care facilities for episodes of care across settings. This allows providers to innovate and redesign systems to deliver equivalent or higher quality of care at lower costs. Another CMMI model, called the comprehensive primary care initiative, involves CMS partnering with private insurers to provide payment to primary care practices for the delivery of chronic disease management and coordinated care to their entire population of patients, regardless of payer. Of great relevance to all hospitalists, CMMI and CMS, in partnership with other HHS agencies, launched the Partnership for Patients program in 2011. To date, approximately 4000 hospitals have signed on to the Partnership in a collective effort to significantly reduce hospital readmissions and hospital‐acquired conditions. Hospitalists are leading the charge related to Partnership for Patients in many hospitals. The Innovation Center is concurrently launching and rapidly evaluating current pilots, while considering what other new pilots might be needed to further test models aimed at the delivery of better healthcare and health outcomes at lower costs.
Perhaps the delivery system initiative that has received the most attention is the implementation of the Medicare Shared Savings Program (MSSP), or Accountable Care Organizations (ACO). Under the MSSP, ACOs are groups of providers (which may include hospitals) and suppliers of services who work together to coordinate care for the patients they serve. Participating ACOs must achieve performance benchmarks while lowering costs to share in the cost savings with CMS. Although this program is focused on Medicare fee‐for‐service (FFS) beneficiaries, it is expected that all patients will benefit from the infrastructure redesign and care coordination that is required under this program. The pioneer ACOs are large integrated health systems or other providers that have higher levels of shared risk in addition to shared savings. Hospitals that are a part of a participating ACO have greater financial incentives to work with their primary care and other outpatient providers to reduce readmissions and other adverse events and achieve quality benchmarks. With the degree of savings as well as financial risk that is on the table, it is possible that over time, hospitals and health systems may consolidate to capture a larger share of the market. Such a consequence could have a parallel effect on job opportunities and financial incentives and risk for hospitalists in local markets.
VALUE‐BASED PURCHASING
Improvement in the quality of care delivered to all patients is another central purpose of the Affordable Care Act. The law requires that the Secretary develop a National Quality Strategy that must be updated annually; the first version of this strategy was published in April of 2011.[2] The strategy identifies 3 aims for the nation: better healthcare for individuals, better health for populations and communities, and lower costs for all. One of the levers that CMS uses to achieve these 3 aims is value‐based purchasing (VBP). VBP is a way to link the National Quality Strategy with Medicare FFS payments on a national scale by adjusting payments based on performance. VBP rewards providers and health systems that deliver better outcomes in health and healthcare at lower cost to the beneficiaries and communities they serve, rather than rewarding them for the volume of services they provide. The ACA authorizes implementation of the Hospital Value‐Based Purchasing (HVBP) program as well as the Physician Value Modifier (PVM). The HVBP program began in 2011, and currently includes process, outcome, and patient experience quality metrics as well as a total cost metric, which includes 30 days postdischarge for beneficiaries admitted to the hospital. Hospitals are rewarded on either their improvement from baseline or achievement of a benchmark, whichever is higher.[3] The PVM program adjusts providers' Medicare FFS payments up or down beginning in 2015, based on quality metrics reported on care provided in 2013. In the first year of the program, groups of 100 or more physicians are eligible for the program, and are given a choice on metrics to report and whether to elect for quality tiering and the potential for payment adjustment[4]; by payment year 2017, all physicians must participate. To participate, physicians must report on quality metrics that they choose through the Physician Quality Reporting System (PQRS) or elect to have their quality assessed based on administrative claim measures. Measures currently in the PQRS program may not always be relevant for hospitalists; CMS is working to define and include metrics that would be most meaningful to hospitalists' scope of practice and is seeking comment on whether to allow hospital‐based physicians to align with and accept hospital quality measures to count as their performance metrics.
PATIENT‐CENTERED OUTCOMES RESEARCH
Building on the down payment on Comparative Effectiveness Research (CER) funded under the American Recovery and Reinvestment Act of 2009, the ACA authorized the creation of the Patient‐Centered Outcomes Research Institute (PCORI) and allocated funding for CER over 10 years. Rebranded as Patient‐Centered Outcomes Research (PCOR), CER has the potential to improve quality and reduce costs by identifying what works for different populations of patients (eg, children, elderly, patients with multiple chronic conditions, racial and ethnic minorities) in varied settings (eg, ambulatory, hospital, nursing home) under real‐world conditions. The PCORI governance board was created in 2010, and as required by law, developed a national agenda for patient‐centered outcomes research, which includes assessment of prevention, diagnosis, and treatment options; improving healthcare systems; communicating and disseminating research; addressing healthcare disparities; and accelerating PCOR and methodological research. The amount of funding available for research and PCOR infrastructure will ramp up over the next several years, eventually reaching approximately $500 million annually, with increasing funding opportunities for comparative research questions related to clinical and delivery system interventions using pragmatic, randomized, controlled trials; implementation science; and other novel research methodologies. Hospitalists have many roles within this realm, whether as researchers comparing delivery system or clinical interventions, as educators of students or healthcare professionals on the results of PCOR and their implications for practice, or as hospital leaders responsible for implementation of evidence‐based practices.[5]
CONCLUSION
The Affordable Care Act is a transformative piece of legislation, and our healthcare system is changing rapidly. Many of the ACA's provisions will change how care is delivered in the United States and will have a direct effect on practicing physicians, hospitals, and patients. Although CMS plays a major role in the implementation of the law, the government cannot be, and should not be, the primary force in transforming health care in this country. Through the provisions highlighted here as well as others, CMS can create a supportive environment, be a catalyst, and provide incentives for change; however, true transformation must occur on the front lines. For hospitalists, this means partnering with the hospital administration and other hospital personnel, local providers, and community organizations to drive systems‐based improvements that will ultimately achieve higher‐quality care at lower costs for all. It also calls for hospitalists to lead change in their local systems focused on better care, better health, and lower costs through improvement.
Disclosure
The views expressed in this manuscript represent the authors and not necessarily the policy or opinions of the Centers for Medicare and Medicaid Services.
- Department of Health and Human Services. Essential Health Benefits: HHS Informational Bulletin. Available at: http://www.healthcare.gov/news/factsheets/2011/12/essential‐health‐benefits12162011a.html. Accessed December 13, 2012.
- Department of Health and Human Services. Report to Congress: National Strategy for Quality Improvement in Healthcare. March 2011. Available at: http://www.healthcare.gov/law/resources/reports/quality03212011a.html. Accessed December 13, 2012.
- Centers for Medicare and Medicaid Services. FY 2013 IPPS Final Rule Home Page. August 2012. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/AcuteInpatientPPS/FY‐2013‐IPPS‐Final‐Rule‐Home‐Page.html. Accessed December 13, 2012.
- Centers for Medicare and Medicaid Services. Physician Fee Schedule. November 2012. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/PhysicianFeeSched/index.html. Accessed December 13, 2012.
- , . Comparative effectiveness research: implications for hospitalists. J Hosp Medicine. 2010;5(5):257–260.
At the Centers for Medicare and Medicaid Services (CMS), we are charged with implementing many of the major provisions of the Affordable Care Act (ACA). Major policies and programs aimed at transforming the way care is delivered and paid for, testing and scaling innovative delivery system reforms, and expanding the number of Americans with health insurance will now move forward. The healthcare system is moving from paying for volume to paying for value. Hospitals and clinicians will need to be able to manage and be accountable for populations of patients and improving health outcomes. In this article, we highlight 4 broad provisions of the ACA that are either already implemented or under development for implementation in 2014, and are anticipated to have widespread impact on our health system. The potential impacts of each provision on hospitals and hospitalists are outlined in Table 1.
| Affordable Care Act Provision | Example of Potential Impacts on Hospitals and Hospitalists |
|---|---|
| |
| Expansion of insurance coverage | Care for fewer uninsured patients/fewer unreimbursed services |
| Patients have improved access to services after discharge | |
| Shorter lengths of stay due to better access to outpatient services and care | |
| Delivery system transformation | Financial incentives aligned between inpatient and outpatient providers to better coordinate care |
| Payment is at risk if performance rates do not meet benchmarks and if costs are not lowered | |
| Consolidation of hospitals and health systems within local markets | |
| Value‐based purchasing | Medicare FFS reimbursement increased or decreased based on quality and cost measure results |
| Opportunity to align incentives between hospitals and hospitalists | |
| Patient‐centered outcomes research | Emerging research on delivery system interventions relevant to hospitalists, such as care transitions |
| Funding for PCOR available for hospitalist researchers interested in delivery systems and outcomes research | |
EXPANSION OF INSURANCE COVERAGE
The central and perhaps most anticipated provision of the ACA is the expansion of insurance to the currently uninsured through the creation of state‐based health insurance exchanges. The exchanges are a competitive marketplace for purchasing private insurance products by individuals and small and large businesses. The individual mandate that accompanies the exchange provision requires that individuals purchase insurance. For those who cannot afford it, the government provides a subsidy. Any health plan that wishes to participate in an exchange marketplace must include at minimum a package of essential health benefits in each of their insurance products, which include benefits such as ambulatory care services, maternal and newborn services, and prescription drugs.[1] Importantly, health plans are required to implement quality improvement strategies and publicly report quality data. The ACA also requires the Secretary of Health and Human Services (HHS) to develop and administer a quality rating system and an enrollee satisfaction survey system, the results of which will be available to exchange consumers. All of these requirements will promote the delivery of high‐quality healthcare to millions of previously uninsured Americans.
Implementation of the exchanges in combination with the expansion of Medicaid is expected to provide insurance to approximately 30 million people who currently lack coverage. Prior to the Supreme Court ruling in June of 2012, states were required to expand Medicaid eligibility to a minimum of 133% of the federal poverty level. This expansion is subsidized 100% by the federal government through 2016, dropping to 90% by 2020. The Supreme Court ruled that the federal government could not require states to expand their Medicaid rolls, although it is expected that most states will do so given the generous federal subsidy and the significant cost to states, hospitals, and society to provide healthcare to the uninsured.
TRANSFORMATION OF HEALTHCARE DELIVERY
In addition to the expansion of insurance coverage, the ACA initiates a transformation in the way that healthcare will be delivered through the testing and implementation of innovative payment and care delivery models. The ACA authorized the creation of the Center for Medicare and Medicaid Innovation (CMMI, or The Innovation Center) within CMS. Payment and care delivery demonstrations or pilots that demonstrate a high quality of care at lower costs can be scaled up nationally at the discretion of the Secretary, rather than requiring authorization by Congress. The Innovation Center has already launched initiatives that test a variety of new models of care, all of which incentivize care coordination, provision of team‐based care, and use of data and quality metrics to drive systems‐based improvement. These programs include pilots that bundle payments to hospitals, physician group practices, and post‐acute care facilities for episodes of care across settings. This allows providers to innovate and redesign systems to deliver equivalent or higher quality of care at lower costs. Another CMMI model, called the comprehensive primary care initiative, involves CMS partnering with private insurers to provide payment to primary care practices for the delivery of chronic disease management and coordinated care to their entire population of patients, regardless of payer. Of great relevance to all hospitalists, CMMI and CMS, in partnership with other HHS agencies, launched the Partnership for Patients program in 2011. To date, approximately 4000 hospitals have signed on to the Partnership in a collective effort to significantly reduce hospital readmissions and hospital‐acquired conditions. Hospitalists are leading the charge related to Partnership for Patients in many hospitals. The Innovation Center is concurrently launching and rapidly evaluating current pilots, while considering what other new pilots might be needed to further test models aimed at the delivery of better healthcare and health outcomes at lower costs.
Perhaps the delivery system initiative that has received the most attention is the implementation of the Medicare Shared Savings Program (MSSP), or Accountable Care Organizations (ACO). Under the MSSP, ACOs are groups of providers (which may include hospitals) and suppliers of services who work together to coordinate care for the patients they serve. Participating ACOs must achieve performance benchmarks while lowering costs to share in the cost savings with CMS. Although this program is focused on Medicare fee‐for‐service (FFS) beneficiaries, it is expected that all patients will benefit from the infrastructure redesign and care coordination that is required under this program. The pioneer ACOs are large integrated health systems or other providers that have higher levels of shared risk in addition to shared savings. Hospitals that are a part of a participating ACO have greater financial incentives to work with their primary care and other outpatient providers to reduce readmissions and other adverse events and achieve quality benchmarks. With the degree of savings as well as financial risk that is on the table, it is possible that over time, hospitals and health systems may consolidate to capture a larger share of the market. Such a consequence could have a parallel effect on job opportunities and financial incentives and risk for hospitalists in local markets.
VALUE‐BASED PURCHASING
Improvement in the quality of care delivered to all patients is another central purpose of the Affordable Care Act. The law requires that the Secretary develop a National Quality Strategy that must be updated annually; the first version of this strategy was published in April of 2011.[2] The strategy identifies 3 aims for the nation: better healthcare for individuals, better health for populations and communities, and lower costs for all. One of the levers that CMS uses to achieve these 3 aims is value‐based purchasing (VBP). VBP is a way to link the National Quality Strategy with Medicare FFS payments on a national scale by adjusting payments based on performance. VBP rewards providers and health systems that deliver better outcomes in health and healthcare at lower cost to the beneficiaries and communities they serve, rather than rewarding them for the volume of services they provide. The ACA authorizes implementation of the Hospital Value‐Based Purchasing (HVBP) program as well as the Physician Value Modifier (PVM). The HVBP program began in 2011, and currently includes process, outcome, and patient experience quality metrics as well as a total cost metric, which includes 30 days postdischarge for beneficiaries admitted to the hospital. Hospitals are rewarded on either their improvement from baseline or achievement of a benchmark, whichever is higher.[3] The PVM program adjusts providers' Medicare FFS payments up or down beginning in 2015, based on quality metrics reported on care provided in 2013. In the first year of the program, groups of 100 or more physicians are eligible for the program, and are given a choice on metrics to report and whether to elect for quality tiering and the potential for payment adjustment[4]; by payment year 2017, all physicians must participate. To participate, physicians must report on quality metrics that they choose through the Physician Quality Reporting System (PQRS) or elect to have their quality assessed based on administrative claim measures. Measures currently in the PQRS program may not always be relevant for hospitalists; CMS is working to define and include metrics that would be most meaningful to hospitalists' scope of practice and is seeking comment on whether to allow hospital‐based physicians to align with and accept hospital quality measures to count as their performance metrics.
PATIENT‐CENTERED OUTCOMES RESEARCH
Building on the down payment on Comparative Effectiveness Research (CER) funded under the American Recovery and Reinvestment Act of 2009, the ACA authorized the creation of the Patient‐Centered Outcomes Research Institute (PCORI) and allocated funding for CER over 10 years. Rebranded as Patient‐Centered Outcomes Research (PCOR), CER has the potential to improve quality and reduce costs by identifying what works for different populations of patients (eg, children, elderly, patients with multiple chronic conditions, racial and ethnic minorities) in varied settings (eg, ambulatory, hospital, nursing home) under real‐world conditions. The PCORI governance board was created in 2010, and as required by law, developed a national agenda for patient‐centered outcomes research, which includes assessment of prevention, diagnosis, and treatment options; improving healthcare systems; communicating and disseminating research; addressing healthcare disparities; and accelerating PCOR and methodological research. The amount of funding available for research and PCOR infrastructure will ramp up over the next several years, eventually reaching approximately $500 million annually, with increasing funding opportunities for comparative research questions related to clinical and delivery system interventions using pragmatic, randomized, controlled trials; implementation science; and other novel research methodologies. Hospitalists have many roles within this realm, whether as researchers comparing delivery system or clinical interventions, as educators of students or healthcare professionals on the results of PCOR and their implications for practice, or as hospital leaders responsible for implementation of evidence‐based practices.[5]
CONCLUSION
The Affordable Care Act is a transformative piece of legislation, and our healthcare system is changing rapidly. Many of the ACA's provisions will change how care is delivered in the United States and will have a direct effect on practicing physicians, hospitals, and patients. Although CMS plays a major role in the implementation of the law, the government cannot be, and should not be, the primary force in transforming health care in this country. Through the provisions highlighted here as well as others, CMS can create a supportive environment, be a catalyst, and provide incentives for change; however, true transformation must occur on the front lines. For hospitalists, this means partnering with the hospital administration and other hospital personnel, local providers, and community organizations to drive systems‐based improvements that will ultimately achieve higher‐quality care at lower costs for all. It also calls for hospitalists to lead change in their local systems focused on better care, better health, and lower costs through improvement.
Disclosure
The views expressed in this manuscript represent the authors and not necessarily the policy or opinions of the Centers for Medicare and Medicaid Services.
At the Centers for Medicare and Medicaid Services (CMS), we are charged with implementing many of the major provisions of the Affordable Care Act (ACA). Major policies and programs aimed at transforming the way care is delivered and paid for, testing and scaling innovative delivery system reforms, and expanding the number of Americans with health insurance will now move forward. The healthcare system is moving from paying for volume to paying for value. Hospitals and clinicians will need to be able to manage and be accountable for populations of patients and improving health outcomes. In this article, we highlight 4 broad provisions of the ACA that are either already implemented or under development for implementation in 2014, and are anticipated to have widespread impact on our health system. The potential impacts of each provision on hospitals and hospitalists are outlined in Table 1.
| Affordable Care Act Provision | Example of Potential Impacts on Hospitals and Hospitalists |
|---|---|
| |
| Expansion of insurance coverage | Care for fewer uninsured patients/fewer unreimbursed services |
| Patients have improved access to services after discharge | |
| Shorter lengths of stay due to better access to outpatient services and care | |
| Delivery system transformation | Financial incentives aligned between inpatient and outpatient providers to better coordinate care |
| Payment is at risk if performance rates do not meet benchmarks and if costs are not lowered | |
| Consolidation of hospitals and health systems within local markets | |
| Value‐based purchasing | Medicare FFS reimbursement increased or decreased based on quality and cost measure results |
| Opportunity to align incentives between hospitals and hospitalists | |
| Patient‐centered outcomes research | Emerging research on delivery system interventions relevant to hospitalists, such as care transitions |
| Funding for PCOR available for hospitalist researchers interested in delivery systems and outcomes research | |
EXPANSION OF INSURANCE COVERAGE
The central and perhaps most anticipated provision of the ACA is the expansion of insurance to the currently uninsured through the creation of state‐based health insurance exchanges. The exchanges are a competitive marketplace for purchasing private insurance products by individuals and small and large businesses. The individual mandate that accompanies the exchange provision requires that individuals purchase insurance. For those who cannot afford it, the government provides a subsidy. Any health plan that wishes to participate in an exchange marketplace must include at minimum a package of essential health benefits in each of their insurance products, which include benefits such as ambulatory care services, maternal and newborn services, and prescription drugs.[1] Importantly, health plans are required to implement quality improvement strategies and publicly report quality data. The ACA also requires the Secretary of Health and Human Services (HHS) to develop and administer a quality rating system and an enrollee satisfaction survey system, the results of which will be available to exchange consumers. All of these requirements will promote the delivery of high‐quality healthcare to millions of previously uninsured Americans.
Implementation of the exchanges in combination with the expansion of Medicaid is expected to provide insurance to approximately 30 million people who currently lack coverage. Prior to the Supreme Court ruling in June of 2012, states were required to expand Medicaid eligibility to a minimum of 133% of the federal poverty level. This expansion is subsidized 100% by the federal government through 2016, dropping to 90% by 2020. The Supreme Court ruled that the federal government could not require states to expand their Medicaid rolls, although it is expected that most states will do so given the generous federal subsidy and the significant cost to states, hospitals, and society to provide healthcare to the uninsured.
TRANSFORMATION OF HEALTHCARE DELIVERY
In addition to the expansion of insurance coverage, the ACA initiates a transformation in the way that healthcare will be delivered through the testing and implementation of innovative payment and care delivery models. The ACA authorized the creation of the Center for Medicare and Medicaid Innovation (CMMI, or The Innovation Center) within CMS. Payment and care delivery demonstrations or pilots that demonstrate a high quality of care at lower costs can be scaled up nationally at the discretion of the Secretary, rather than requiring authorization by Congress. The Innovation Center has already launched initiatives that test a variety of new models of care, all of which incentivize care coordination, provision of team‐based care, and use of data and quality metrics to drive systems‐based improvement. These programs include pilots that bundle payments to hospitals, physician group practices, and post‐acute care facilities for episodes of care across settings. This allows providers to innovate and redesign systems to deliver equivalent or higher quality of care at lower costs. Another CMMI model, called the comprehensive primary care initiative, involves CMS partnering with private insurers to provide payment to primary care practices for the delivery of chronic disease management and coordinated care to their entire population of patients, regardless of payer. Of great relevance to all hospitalists, CMMI and CMS, in partnership with other HHS agencies, launched the Partnership for Patients program in 2011. To date, approximately 4000 hospitals have signed on to the Partnership in a collective effort to significantly reduce hospital readmissions and hospital‐acquired conditions. Hospitalists are leading the charge related to Partnership for Patients in many hospitals. The Innovation Center is concurrently launching and rapidly evaluating current pilots, while considering what other new pilots might be needed to further test models aimed at the delivery of better healthcare and health outcomes at lower costs.
Perhaps the delivery system initiative that has received the most attention is the implementation of the Medicare Shared Savings Program (MSSP), or Accountable Care Organizations (ACO). Under the MSSP, ACOs are groups of providers (which may include hospitals) and suppliers of services who work together to coordinate care for the patients they serve. Participating ACOs must achieve performance benchmarks while lowering costs to share in the cost savings with CMS. Although this program is focused on Medicare fee‐for‐service (FFS) beneficiaries, it is expected that all patients will benefit from the infrastructure redesign and care coordination that is required under this program. The pioneer ACOs are large integrated health systems or other providers that have higher levels of shared risk in addition to shared savings. Hospitals that are a part of a participating ACO have greater financial incentives to work with their primary care and other outpatient providers to reduce readmissions and other adverse events and achieve quality benchmarks. With the degree of savings as well as financial risk that is on the table, it is possible that over time, hospitals and health systems may consolidate to capture a larger share of the market. Such a consequence could have a parallel effect on job opportunities and financial incentives and risk for hospitalists in local markets.
VALUE‐BASED PURCHASING
Improvement in the quality of care delivered to all patients is another central purpose of the Affordable Care Act. The law requires that the Secretary develop a National Quality Strategy that must be updated annually; the first version of this strategy was published in April of 2011.[2] The strategy identifies 3 aims for the nation: better healthcare for individuals, better health for populations and communities, and lower costs for all. One of the levers that CMS uses to achieve these 3 aims is value‐based purchasing (VBP). VBP is a way to link the National Quality Strategy with Medicare FFS payments on a national scale by adjusting payments based on performance. VBP rewards providers and health systems that deliver better outcomes in health and healthcare at lower cost to the beneficiaries and communities they serve, rather than rewarding them for the volume of services they provide. The ACA authorizes implementation of the Hospital Value‐Based Purchasing (HVBP) program as well as the Physician Value Modifier (PVM). The HVBP program began in 2011, and currently includes process, outcome, and patient experience quality metrics as well as a total cost metric, which includes 30 days postdischarge for beneficiaries admitted to the hospital. Hospitals are rewarded on either their improvement from baseline or achievement of a benchmark, whichever is higher.[3] The PVM program adjusts providers' Medicare FFS payments up or down beginning in 2015, based on quality metrics reported on care provided in 2013. In the first year of the program, groups of 100 or more physicians are eligible for the program, and are given a choice on metrics to report and whether to elect for quality tiering and the potential for payment adjustment[4]; by payment year 2017, all physicians must participate. To participate, physicians must report on quality metrics that they choose through the Physician Quality Reporting System (PQRS) or elect to have their quality assessed based on administrative claim measures. Measures currently in the PQRS program may not always be relevant for hospitalists; CMS is working to define and include metrics that would be most meaningful to hospitalists' scope of practice and is seeking comment on whether to allow hospital‐based physicians to align with and accept hospital quality measures to count as their performance metrics.
PATIENT‐CENTERED OUTCOMES RESEARCH
Building on the down payment on Comparative Effectiveness Research (CER) funded under the American Recovery and Reinvestment Act of 2009, the ACA authorized the creation of the Patient‐Centered Outcomes Research Institute (PCORI) and allocated funding for CER over 10 years. Rebranded as Patient‐Centered Outcomes Research (PCOR), CER has the potential to improve quality and reduce costs by identifying what works for different populations of patients (eg, children, elderly, patients with multiple chronic conditions, racial and ethnic minorities) in varied settings (eg, ambulatory, hospital, nursing home) under real‐world conditions. The PCORI governance board was created in 2010, and as required by law, developed a national agenda for patient‐centered outcomes research, which includes assessment of prevention, diagnosis, and treatment options; improving healthcare systems; communicating and disseminating research; addressing healthcare disparities; and accelerating PCOR and methodological research. The amount of funding available for research and PCOR infrastructure will ramp up over the next several years, eventually reaching approximately $500 million annually, with increasing funding opportunities for comparative research questions related to clinical and delivery system interventions using pragmatic, randomized, controlled trials; implementation science; and other novel research methodologies. Hospitalists have many roles within this realm, whether as researchers comparing delivery system or clinical interventions, as educators of students or healthcare professionals on the results of PCOR and their implications for practice, or as hospital leaders responsible for implementation of evidence‐based practices.[5]
CONCLUSION
The Affordable Care Act is a transformative piece of legislation, and our healthcare system is changing rapidly. Many of the ACA's provisions will change how care is delivered in the United States and will have a direct effect on practicing physicians, hospitals, and patients. Although CMS plays a major role in the implementation of the law, the government cannot be, and should not be, the primary force in transforming health care in this country. Through the provisions highlighted here as well as others, CMS can create a supportive environment, be a catalyst, and provide incentives for change; however, true transformation must occur on the front lines. For hospitalists, this means partnering with the hospital administration and other hospital personnel, local providers, and community organizations to drive systems‐based improvements that will ultimately achieve higher‐quality care at lower costs for all. It also calls for hospitalists to lead change in their local systems focused on better care, better health, and lower costs through improvement.
Disclosure
The views expressed in this manuscript represent the authors and not necessarily the policy or opinions of the Centers for Medicare and Medicaid Services.
- Department of Health and Human Services. Essential Health Benefits: HHS Informational Bulletin. Available at: http://www.healthcare.gov/news/factsheets/2011/12/essential‐health‐benefits12162011a.html. Accessed December 13, 2012.
- Department of Health and Human Services. Report to Congress: National Strategy for Quality Improvement in Healthcare. March 2011. Available at: http://www.healthcare.gov/law/resources/reports/quality03212011a.html. Accessed December 13, 2012.
- Centers for Medicare and Medicaid Services. FY 2013 IPPS Final Rule Home Page. August 2012. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/AcuteInpatientPPS/FY‐2013‐IPPS‐Final‐Rule‐Home‐Page.html. Accessed December 13, 2012.
- Centers for Medicare and Medicaid Services. Physician Fee Schedule. November 2012. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/PhysicianFeeSched/index.html. Accessed December 13, 2012.
- , . Comparative effectiveness research: implications for hospitalists. J Hosp Medicine. 2010;5(5):257–260.
- Department of Health and Human Services. Essential Health Benefits: HHS Informational Bulletin. Available at: http://www.healthcare.gov/news/factsheets/2011/12/essential‐health‐benefits12162011a.html. Accessed December 13, 2012.
- Department of Health and Human Services. Report to Congress: National Strategy for Quality Improvement in Healthcare. March 2011. Available at: http://www.healthcare.gov/law/resources/reports/quality03212011a.html. Accessed December 13, 2012.
- Centers for Medicare and Medicaid Services. FY 2013 IPPS Final Rule Home Page. August 2012. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/AcuteInpatientPPS/FY‐2013‐IPPS‐Final‐Rule‐Home‐Page.html. Accessed December 13, 2012.
- Centers for Medicare and Medicaid Services. Physician Fee Schedule. November 2012. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/PhysicianFeeSched/index.html. Accessed December 13, 2012.
- , . Comparative effectiveness research: implications for hospitalists. J Hosp Medicine. 2010;5(5):257–260.
Hospitalist Utilization and Performance
The past several years have seen a dramatic increase in the percentage of patients cared for by hospitalists, yet an emerging body of literature examining the association between care given by hospitalists and performance on a number of process measures has shown mixed results. Hospitalists do not appear to provide higher quality of care for pneumonia,1, 2 while results in heart failure are mixed.35 Each of these studies was conducted at a single site, and examined patient‐level effects. More recently, Vasilevskis et al6 assessed the association between the intensity of hospitalist use (measured as the percentage of patients admitted by hospitalists) and performance on process measures. In a cohort of 208 California hospitals, they found a significant improvement in performance on process measures in patients with acute myocardial infarction, heart failure, and pneumonia with increasing percentages of patients admitted by hospitalists.6
To date, no study has examined the association between the use of hospitalists and the publicly reported 30‐day mortality and readmission measures. Specifically, the Centers for Medicare and Medicaid Services (CMS) have developed and now publicly report risk‐standardized 30‐day mortality (RSMR) and readmission rates (RSRR) for Medicare patients hospitalized for 3 common and costly conditionsacute myocardial infarction (AMI), heart failure (HF), and pneumonia.7 Performance on these hospital‐based quality measures varies widely, and vary by hospital volume, ownership status, teaching status, and nurse staffing levels.813 However, even accounting for these characteristics leaves much of the variation in outcomes unexplained. We hypothesized that the presence of hospitalists within a hospital would be associated with higher performance on 30‐day mortality and 30‐day readmission measures for AMI, HF, and pneumonia. We further hypothesized that for hospitals using hospitalists, there would be a positive correlation between increasing percentage of patients admitted by hospitalists and performance on outcome measures. To test these hypotheses, we conducted a national survey of hospitalist leaders, linking data from survey responses to data on publicly reported outcome measures for AMI, HF, and pneumonia.
MATERIALS AND METHODS
Study Sites
Of the 4289 hospitals in operation in 2008, 1945 had 25 or more AMI discharges. We identified hospitals using American Hospital Association (AHA) data, calling hospitals up to 6 times each until we reached our target sample size of 600. Using this methodology, we contacted 1558 hospitals of a possible 1920 with AHA data; of the 1558 called, 598 provided survey results.
Survey Data
Our survey was adapted from the survey developed by Vasilevskis et al.6 The entire survey can be found in the Appendix (see Supporting Information in the online version of this article). Our key questions were: 1) Does your hospital have at least 1 hospitalist program or group? 2) Approximately what percentage of all medical patients in your hospital are admitted by hospitalists? The latter question was intended as an approximation of the intensity of hospitalist use, and has been used in prior studies.6, 14 A more direct measure was not feasible given the complexity of obtaining admission data for such a large and diverse set of hospitals. Respondents were also asked about hospitalist care of AMI, HF, and pneumonia patients. Given the low likelihood of precise estimation of hospitalist participation in care for specific conditions, the response choices were divided into percentage quartiles: 025, 2650, 5175, and 76100. Finally, participants were asked a number of questions regarding hospitalist organizational and clinical characteristics.
Survey Process
We obtained data regarding presence or absence of hospitalists and characteristics of the hospitalist services via phone‐ and fax‐administered survey (see Supporting Information, Appendix, in the online version of this article). Telephone and faxed surveys were administered between February 2010 and January 2011. Hospital telephone numbers were obtained from the 2008 AHA survey database and from a review of each hospital's website. Up to 6 attempts were made to obtain a completed survey from nonrespondents unless participation was specifically refused. Potential respondents were contacted in the following order: hospital medicine department leaders, hospital medicine clinical managers, vice president for medical affairs, chief medical officers, and other hospital executives with knowledge of the hospital medicine services. All respondents agreed with a question asking whether they had direct working knowledge of their hospital medicine services; contacts who said they did not have working knowledge of their hospital medicine services were asked to refer our surveyor to the appropriate person at their site. Absence of a hospitalist program was confirmed by contacting the Medical Staff Office.
Hospital Organizational and Patient‐Mix Characteristics
Hospital‐level organizational characteristics (eg, bed size, teaching status) and patient‐mix characteristics (eg, Medicare and Medicaid inpatient days) were obtained from the 2008 AHA survey database.
Outcome Performance Measures
The 30‐day risk‐standardized mortality and readmission rates (RSMR and RSRR) for 2008 for AMI, HF, and pneumonia were calculated for all admissions for people age 65 and over with traditional fee‐for‐service Medicare. Beneficiaries had to be enrolled for 12 months prior to their hospitalization for any of the 3 conditions, and had to have complete claims data available for that 12‐month period.7 These 6 outcome measures were constructed using hierarchical generalized linear models.1520 Using the RSMR for AMI as an example, for each hospital, the measure is estimated by dividing the predicted number of deaths within 30 days of admission for AMI by the expected number of deaths within 30 days of admission for AMI. This ratio is then divided by the national unadjusted 30‐day mortality rate for AMI, which is obtained using data on deaths from the Medicare beneficiary denominator file. Each measure is adjusted for patient characteristics such as age, gender, and comorbidities. All 6 measures are endorsed by the National Quality Forum (NQF) and are reported publicly by CMS on the Hospital Compare web site.
Statistical Analysis
Comparison of hospital‐ and patient‐level characteristics between hospitals with and without hospitalists was performed using chi‐square tests and Student t tests.
The primary outcome variables are the RSMRs and RSRRs for AMI, HF, and pneumonia. Multivariable linear regression models were used to assess the relationship between hospitals with at least 1 hospitalist group and each dependent variable. Models were adjusted for variables previously reported to be associated with quality of care. Hospital‐level characteristics included core‐based statistical area, teaching status, number of beds, region, safety‐net status, nursing staff ratio (number of registered nurse FTEs/number of hospital FTEs), and presence or absence of cardiac catheterization and coronary bypass capability. Patient‐level characteristics included Medicare and Medicaid inpatient days as a percentage of total inpatient days and percentage of admissions by race (black vs non‐black). The presence of hospitalists was correlated with each of the hospital and patient‐level characteristics. Further analyses of the subset of hospitals that use hospitalists included construction of multivariable linear regression models to assess the relationship between the percentage of patients admitted by hospitalists and the dependent variables. Models were adjusted for the same patient‐ and hospital‐level characteristics.
The institutional review boards at Yale University and University of California, San Francisco approved the study. All analyses were performed using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc, Cary, NC).
RESULTS
Characteristics of Participating Hospitals
Telephone, fax, and e‐mail surveys were attempted with 1558 hospitals; we received 598 completed surveys for a response rate of 40%. There was no difference between responders and nonresponders on any of the 6 outcome variables, the number of Medicare or Medicaid inpatient days, and the percentage of admissions by race. Responders and nonresponders were also similar in size, ownership, safety‐net and teaching status, nursing staff ratio, presence of cardiac catheterization and coronary bypass capability, and core‐based statistical area. They differed only on region of the country, where hospitals in the northwest Central and Pacific regions of the country had larger overall proportions of respondents. All hospitals provided information about the presence or absence of hospitalist programs. The majority of respondents were hospitalist clinical or administrative managers (n = 220) followed by hospitalist leaders (n = 106), other executives (n = 58), vice presidents for medical affairs (n = 39), and chief medical officers (n = 15). Each respondent indicated a working knowledge of their site's hospitalist utilization and practice characteristics. Absence of hospitalist utilization was confirmed by contact with the Medical Staff Office.
Comparisons of Sites With Hospitalists and Those Without Hospitalists
Hospitals with and without hospitalists differed by a number of organizational characteristics (Table 1). Sites with hospitalists were more likely to be larger, nonprofit teaching hospitals, located in metropolitan regions, and have cardiac surgical services. There was no difference in the hospitals' safety‐net status or RN staffing ratio. Hospitals with hospitalists admitted lower percentages of black patients.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| N (%) | N (%) | P Value | |
| |||
| Core‐based statistical area | <0.0001 | ||
| Division | 94 (21.9%) | 53 (31.4%) | |
| Metro | 275 (64.1%) | 72 (42.6%) | |
| Micro | 52 (12.1%) | 38 (22.5%) | |
| Rural | 8 (1.9%) | 6 (3.6%) | |
| Owner | 0.0003 | ||
| Public | 47 (11.0%) | 20 (11.8%) | |
| Nonprofit | 333 (77.6%) | 108 (63.9%) | |
| Private | 49 (11.4%) | 41 (24.3%) | |
| Teaching status | <0.0001 | ||
| COTH | 54 (12.6%) | 7 (4.1%) | |
| Teaching | 110 (25.6%) | 26 (15.4%) | |
| Other | 265 (61.8%) | 136 (80.5%) | |
| Cardiac type | 0.0003 | ||
| CABG | 286 (66.7%) | 86 (50.9%) | |
| CATH | 79 (18.4%) | 36 (21.3%) | |
| Other | 64 (14.9%) | 47 (27.8%) | |
| Region | 0.007 | ||
| New England | 35 (8.2%) | 3 (1.8%) | |
| Middle Atlantic | 60 (14.0%) | 29 (17.2%) | |
| South Atlantic | 78 (18.2%) | 23 (13.6%) | |
| NE Central | 60 (14.0%) | 35 (20.7%) | |
| SE Central | 31 (7.2%) | 10 (5.9%) | |
| NW Central | 38 (8.9%) | 23 (13.6%) | |
| SW Central | 41 (9.6%) | 21 (12.4%) | |
| Mountain | 22 (5.1%) | 3 (1.8%) | |
| Pacific | 64 (14.9%) | 22 (13.0%) | |
| Safety‐net | 0.53 | ||
| Yes | 72 (16.8%) | 32 (18.9%) | |
| No | 357 (83.2%) | 137 (81.1%) | |
| Mean (SD) | Mean (SD) | P value | |
| RN staffing ratio (n = 455) | 27.3 (17.0) | 26.1 (7.6) | 0.28 |
| Total beds | 315.0 (216.6) | 214.8 (136.0) | <0.0001 |
| % Medicare inpatient days | 47.2 (42) | 49.7 (41) | 0.19 |
| % Medicaid inpatient days | 18.5 (28) | 21.4 (46) | 0.16 |
| % Black | 7.6 (9.6) | 10.6 (17.4) | 0.03 |
Characteristics of Hospitalist Programs and Responsibilities
Of the 429 sites reporting use of hospitalists, the median percentage of patients admitted by hospitalists was 60%, with an interquartile range (IQR) of 35% to 80%. The median number of full‐time equivalent hospitalists per hospital was 8 with an IQR of 5 to 14. The IQR reflects the middle 50% of the distribution of responses, and is not affected by outliers or extreme values. Additional characteristics of hospitalist programs can be found in Table 2. The estimated percentage of patients with AMI, HF, and pneumonia cared for by hospitalists varied considerably, with fewer patients with AMI and more patients with pneumonia under hospitalist care. Overall, a majority of hospitalist groups provided the following services: care of critical care patients, emergency department admission screening, observation unit coverage, coverage for cardiac arrests and rapid response teams, quality improvement or utilization review activities, development of hospital practice guidelines, and participation in implementation of major hospital system projects (such as implementation of an electronic health record system).
| N (%) | |
|---|---|
| |
| Date program established | |
| 19871994 | 9 (2.2%) |
| 19952002 | 130 (32.1%) |
| 20032011 | 266 (65.7%) |
| Missing date | 24 |
| No. of hospitalist FTEs | |
| Median (IQR) | 8 (5, 14) |
| Percent of medical patients admitted by hospitalists | |
| Median (IQR) | 60% (35, 80) |
| No. of hospitalists groups | |
| 1 | 333 (77.6%) |
| 2 | 54 (12.6%) |
| 3 | 36 (8.4%) |
| Don't know | 6 (1.4%) |
| Employment of hospitalists (not mutually exclusive) | |
| Hospital system | 98 (22.8%) |
| Hospital | 185 (43.1%) |
| Local physician practice group | 62 (14.5%) |
| Hospitalist physician practice group (local) | 83 (19.3%) |
| Hospitalist physician practice group (national/regional) | 36 (8.4%) |
| Other/unknown | 36 (8.4%) |
| Any 24‐hr in‐house coverage by hospitalists | |
| Yes | 329 (76.7%) |
| No | 98 (22.8%) |
| 3 | 1 (0.2%) |
| Unknown | 1 (0.2%) |
| No. of hospitalist international medical graduates | |
| Median (IQR) | 3 (1, 6) |
| No. of hospitalists that are <1 yr out of residency | |
| Median (IQR) | 1 (0, 2) |
| Percent of patients with AMI cared for by hospitalists | |
| 0%25% | 148 (34.5%) |
| 26%50% | 67 (15.6%) |
| 51%75% | 50 (11.7%) |
| 76%100% | 54 (12.6%) |
| Don't know | 110 (25.6%) |
| Percent of patients with heart failure cared for by hospitalists | |
| 0%25% | 79 (18.4%) |
| 26%50% | 78 (18.2%) |
| 51%75% | 75 (17.5%) |
| 76%100% | 84 (19.6%) |
| Don't know | 113 (26.3%) |
| Percent of patients with pneumonia cared for by hospitalists | |
| 0%25% | 47 (11.0%) |
| 26%50% | 61 (14.3%) |
| 51%75% | 74 (17.3%) |
| 76%100% | 141 (32.9%) |
| Don't know | 105 (24.5%) |
| Hospitalist provision of services | |
| Care of critical care patients | |
| Hospitalists provide service | 346 (80.7%) |
| Hospitalists do not provide service | 80 (18.7%) |
| Don't know | 3 (0.7%) |
| Emergency department admission screening | |
| Hospitalists provide service | 281 (65.5%) |
| Hospitalists do not provide service | 143 (33.3%) |
| Don't know | 5 (1.2%) |
| Observation unit coverage | |
| Hospitalists provide service | 359 (83.7%) |
| Hospitalists do not provide service | 64 (14.9%) |
| Don't know | 6 (1.4%) |
| Emergency department coverage | |
| Hospitalists provide service | 145 (33.8%) |
| Hospitalists do not provide service | 280 (65.3%) |
| Don't know | 4 (0.9%) |
| Coverage for cardiac arrests | |
| Hospitalists provide service | 283 (66.0%) |
| Hospitalists do not provide service | 135 (31.5%) |
| Don't know | 11 (2.6%) |
| Rapid response team coverage | |
| Hospitalists provide service | 240 (55.9%) |
| Hospitalists do not provide service | 168 (39.2%) |
| Don't know | 21 (4.9%) |
| Quality improvement or utilization review | |
| Hospitalists provide service | 376 (87.7%) |
| Hospitalists do not provide service | 37 (8.6%) |
| Don't know | 16 (3.7%) |
| Hospital practice guideline development | |
| Hospitalists provide service | 339 (79.0%) |
| Hospitalists do not provide service | 55 (12.8%) |
| Don't know | 35 (8.2%) |
| Implementation of major hospital system projects | |
| Hospitalists provide service | 309 (72.0%) |
| Hospitalists do not provide service | 96 (22.4%) |
| Don't know | 24 (5.6%) |
Relationship Between Hospitalist Utilization and Outcomes
Tables 3 and 4 show the comparisons between hospitals with and without hospitalists on each of the 6 outcome measures. In the bivariate analysis (Table 3), there was no statistically significant difference between groups on any of the outcome measures with the exception of the risk‐stratified readmission rate for heart failure. Sites with hospitalists had a lower RSRR for HF than sites without hospitalists (24.7% vs 25.4%, P < 0.0001). These results were similar in the multivariable models as seen in Table 4, in which the beta estimate (slope) was not significantly different for hospitals utilizing hospitalists compared to those that did not, on all measures except the RSRR for HF. For the subset of hospitals that used hospitalists, there was no statistically significant change in any of the 6 outcome measures, with increasing percentage of patients admitted by hospitalists. Table 5 demonstrates that for each RSMR and RSRR, the slope did not consistently increase or decrease with incrementally higher percentages of patients admitted by hospitalists, and the confidence intervals for all estimates crossed zero.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| Outcome Measure | Mean % (SD) | Mean (SD) | P Value |
| |||
| MI RSMR | 16.0 (1.6) | 16.1 (1.5) | 0.56 |
| MI RSRR | 19.9 (0.88) | 20.0 (0.86) | 0.16 |
| HF RSMR | 11.3 (1.4) | 11.3 (1.4) | 0.77 |
| HF RSRR | 24.7 (1.6) | 25.4 (1.8) | <0.0001 |
| Pneumonia RSMR | 11.7 (1.7) | 12.0 (1.7) | 0.08 |
| Pneumonia RSRR | 18.2 (1.2) | 18.3 (1.1) | 0.28 |
| Adjusted beta estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Hospitalist | 0.001 (0.002, 004) |
| MI RSRR | |
| Hospitalist | 0.001 (0.002, 0.001) |
| HF RSMR | |
| Hospitalist | 0.0004 (0.002, 0.003) |
| HF RSRR | |
| Hospitalist | 0.006 (0.009, 0.003) |
| Pneumonia RSMR | |
| Hospitalist | 0.002 (0.005, 0.001) |
| Pneumonia RSRR | |
| Hospitalist | 0.00001 (0.002, 0.002) |
| Adjusted Beta Estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Percent admit | |
| 0%30% | 0.003 (0.007, 0.002) |
| 32%48% | 0.001 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.004 (0.001, 0.009) |
| 85% | 0.004 (0.009, 0.001) |
| MI RSRR | |
| Percent admit | |
| 0%30% | 0.001 (0.002, 0.004) |
| 32%48% | 0.001 (0.004, 0.004) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.002, 0.004) |
| 85% | 0.001 (0.002, 0.004) |
| HF RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.005, 0.003) |
| 32%48% | 0.002 (0.007, 0.003) |
| 50%66% | Ref |
| 70%80% | 0.002 (0.006, 0.002) |
| 85% | 0.001 (0.004, 0.005) |
| HF RSRR | |
| Percent admit | |
| 0%30% | 0.002 (0.004, 0.007) |
| 32%48% | 0.0003 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.005, 0.004) |
| 85% | 0.002 (0.007, 0.003) |
| Pneumonia RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.004, 0.006) |
| 32%48% | 0.00001 (0.006, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.004, 0.006) |
| 85% | 0.001 (0.006, 0.005) |
| Pneumonia RSRR | |
| Percent admit | |
| 0%30% | 0.0002 (0.004, 0.003) |
| 32%48% | 0.004 (0.0003, 0.008) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.003, 0.004) |
| 85% | 0.002 (0.002, 0.006) |
DISCUSSION
In this national survey of hospitals, we did not find a significant association between the use of hospitalists and hospitals' performance on 30‐day mortality or readmissions measures for AMI, HF, or pneumonia. While there was a statistically lower 30‐day risk‐standardized readmission rate measure for the heart failure measure among hospitals that use hospitalists, the effect size was small. The survey response rate of 40% is comparable to other surveys of physicians and other healthcare personnel, however, there were no significant differences between responders and nonresponders, so the potential for response bias, while present, is small.
Contrary to the findings of a recent study,21 we did not find a higher readmission rate for any of the 3 conditions in hospitals with hospitalist programs. One advantage of our study is the use of more robust risk‐adjustment methods. Our study used NQF‐endorsed risk‐standardized measures of readmission, which capture readmissions to any hospital for common, high priority conditions where the impact of care coordination and discontinuity of care are paramount. The models use administrative claims data, but have been validated by medical record data. Another advantage is that our study focused on a time period when hospital readmissions were a standard quality benchmark and increasing priority for hospitals, hospitalists, and community‐based care delivery systems. While our study is not able to discern whether patients had primary care physicians or the reason for admission to a hospitalist's care, our data do suggest that hospitalists continue to care for a large percentage of hospitalized patients. Moreover, increasing the proportion of patients being admitted to hospitalists did not affect the risk for readmission, providing perhaps reassuring evidence (or lack of proof) for a direct association between use of hospitalist systems and higher risk for readmission.
While hospitals with hospitalists clearly did not have better mortality or readmission rates, an alternate viewpoint might hold that, despite concerns that hospitalists negatively impact care continuity, our data do not demonstrate an association between readmission rates and use of hospitalist services. It is possible that hospitals that have hospitalists may have more ability to invest in hospital‐based systems of care,22 an association which may incorporate any hospitalist effect, but our results were robust even after testing whether adjustment for hospital factors (such as profit status, size) affected our results.
It is also possible that secular trends in hospitals or hospitalist systems affected our results. A handful of single‐site studies carried out soon after the hospitalist model's earliest descriptions found a reduction in mortality and readmission rates with the implementation of a hospitalist program.2325 Alternatively, it may be that there has been a dilution of the effect of hospitalists as often occurs when any new innovation is spread from early adopter sites to routine practice. Consistent with other multicenter studies from recent eras,21, 26 our article's findings do not demonstrate an association between hospitalists and improved outcomes. Unlike other multicenter studies, we had access to disease‐specific risk‐adjustment methodologies, which may partially account for referral biases related to patient‐specific measures of acute or chronic illness severity.
Changes in the hospitalist effect over time have a number of explanations, some of which are relevant to our study. Recent evidence suggests that complex organizational characteristics, such as organizational values and goals, may contribute to performance on 30‐day mortality for AMI rather than specific processes and protocols27; intense focus on AMI as a quality improvement target is emblematic of a number of national initiatives that may have affected our results. Interestingly, hospitalist systems have changed over time as well. Early in the hospitalist movement, hospitalist systems were implemented largely at the behest of hospitals trying to reduce costs. In recent years, however, hospitalist systems are at least as frequently being implemented because outpatient‐based physicians or surgeons request hospitalists; hospitalists have been focused on care of uncoveredpatients, since the model's earliest description. In addition, some hospitals invest in hospitalist programs based on perceived ability of hospitalists to improve quality and achieve better patient outcomes in an era of payment increasingly being linked to quality of care metrics.
Our study has several limitations, six of which are noted here. First, while the hospitalist model has been widely embraced in the adult medicine field, in the absence of board certification, there is no gold standard definition of a hospitalist. It is therefore possible that some respondents may have represented groups that were identified incorrectly as hospitalists. Second, the data for the primary independent variable of interest was based upon self‐report and, therefore, subject to recall bias and potential misclassification of results. Respondents were not aware of our hypothesis, so the bias should not have been in one particular direction. Third, the data for the outcome variables are from 2008. They may, therefore, not reflect organizational enhancements related to use of hospitalists that are in process, and take years to yield downstream improvements on performance metrics. In addition, of the 429 hospitals that have hospitalist programs, 46 programs were initiated after 2008. While national performance on the 6 outcome variables has been relatively static over time,7 any significant change in hospital performance on these metrics since 2008 could suggest an overestimation or underestimation of the effect of hospitalist programs on patient outcomes. Fourth, we were not able to adjust for additional hospital or health system level characteristics that may be associated with hospitalist use or patient outcomes. Fifth, our regression models had significant collinearity, in that the presence of hospitalists was correlated with each of the covariates. However, this finding would indicate that our estimates may be overly conservative and could have contributed to our nonsignificant findings. Finally, outcomes for 2 of the 3 clinical conditions measured are ones for which hospitalists may less frequently provide care: acute myocardial infarction and heart failure. Outcome measures more relevant for hospitalists may be all‐condition, all‐cause, 30‐day mortality and readmission.
This work adds to the growing body of literature examining the impact of hospitalists on quality of care. To our knowledge, it is the first study to assess the association between hospitalist use and performance on outcome metrics at a national level. While our findings suggest that use of hospitalists alone may not lead to improved performance on outcome measures, a parallel body of research is emerging implicating broader system and organizational factors as key to high performance on outcome measures. It is likely that multiple factors contribute to performance on outcome measures, including type and mix of hospital personnel, patient care processes and workflow, and system level attributes. Comparative effectiveness and implementation research that assess the contextual factors and interventions that lead to successful system improvement and better performance is increasingly needed. It is unlikely that a single factor, such as hospitalist use, will significantly impact 30‐day mortality or readmission and, therefore, multifactorial interventions are likely required. In addition, hospitalist use is a complex intervention as the structure, processes, training, experience, role in the hospital system, and other factors (including quality of hospitalists or the hospitalist program) vary across programs. Rather than focusing on the volume of care delivered by hospitalists, hospitals will likely need to support hospital medicine programs that have the time and expertise to devote to improving the quality and value of care delivered across the hospital system. This study highlights that interventions leading to improvement on core outcome measures are more complex than simply having a hospital medicine program.
Acknowledgements
The authors acknowledge Judy Maselli, MPH, Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, for her assistance with statistical analyses and preparation of tables.
Disclosures: Work on this project was supported by the Robert Wood Johnson Clinical Scholars Program (K.G.); California Healthcare Foundation grant 15763 (A.D.A.); and a grant from the National Heart, Lung, and Blood Institute (NHLBI), study 1U01HL105270‐02 (H.M.K.). Dr Krumholz is the chair of the Cardiac Scientific Advisory Board for United Health and has a research grant with Medtronic through Yale University; Dr Auerbach has a grant through the National Heart, Lung, and Blood Institute (NHLBI). The authors have no other disclosures to report.
- ,,,.Comparison of hospitalists and nonhospitalists regarding core measures of pneumonia care.Am J Manag Care.2007;13:129–132.
- ,,,.Comparison of processes and outcomes of pneumonia care between hospitalists and community‐based primary care physicians.Mayo Clin Proc.2002;77(10):1053–1058.
- ,,,,.Quality of care for patients hospitalized with heart failure: assessing the impact of hospitalists.Arch Intern Med.2002;162(11):1251–1256.
- ,,, et al.Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):1399–1406.
- ,,.Comparison of practice patterns of hospitalists and community physicians in the care of patients with congestive heart failure.J Hosp Med.2008;3(1):35–41.
- ,,,,.Cross‐sectional analysis of hospitalist prevalence and quality of care in California.J Hosp Med.2010;5(4):200–207.
- Hospital Compare. Department of Health and Human Services. Available at: http://www.hospitalcompare.hhs.gov. Accessed September 3,2011.
- ,.Teaching hospitals and quality of care: a review of the literature.Milbank Q.2002;80(3):569–593.
- ,,, et al.A systematic review and meta‐analysis of studies comparing mortality rates of private for‐profit and private not‐for‐profit hospitals.Can Med Assoc J.2002;166(11):1399–1406.
- ,,,,.Patient and hospital characteristics associated with recommended processes of care for elderly patients hospitalized with pneumonia: results from the Medicare Quality Indicator System Pneumonia Module.Arch Intern Med.2002;162(7):827–833.
- ,,,.Care in U.S. hospitals—The Hospital Quality Alliance Program.N Engl J Med.2005;353(3):265–274.
- ,,, et al.Hospital characteristics and quality of care.JAMA.1992;268(13):1709–1714.
- ,,,,.Nurse‐staffing levels and the quality of care in hospitals.N Engl J Med.2002;346(22):1715–1722.
- ,,,.Health care market trends and the evolution of hospitalist use and roles.J Gen Intern Med.2005;20:101–107.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with an acute myocardial infarction.Circulation.2006;113:1683–1692.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance based on 30‐day all‐cause readmission rates among patients with acute myocardial infarction.Circulation.2011;4:243–252.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance on the basis of 30‐day all‐cause readmission rates among patients with heart failure.Circ Cardiovasc Qual Outcomes.2008;1:29–37.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with heart failure.Circulation.2006;113:1693–1701.
- ,,, et al.An administrative claims model for profiling hospital 30‐day mortality rates for pneumonia patients.PLoS ONE.2011;6(4):e17401.
- ,,, et al.Development, validation and results of a measure of 30‐day readmission following hospitalization for pneumonia.J Hosp Med.2011;6:142–150.
- ,.Association of hospitalist care with medical utilization after discharge: evidence of cost shift from a cohort study.Ann Intern Med.2011;155:152–159.
- ,,,.California hospital leaders' views of hospitalists: meeting needs of the present and future.J Hosp Med.2009;4:528–534.
- ,,, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866–874.
- ,,,,,.Implementation of a voluntary hospitalist service at a community teaching hospital: improved clinical efficiency and patients outcomes.Ann Intern Med.2002;137:859–865.
- ,,,.A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service.South Med J.2009;102:145–149.
- ,,,,,.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357:2589–2600.
- ,,, et al.What distinguishes top‐performing hospitals in acute myocardial infarction mortality rates?Ann Intern Med.2011;154:384–390.
The past several years have seen a dramatic increase in the percentage of patients cared for by hospitalists, yet an emerging body of literature examining the association between care given by hospitalists and performance on a number of process measures has shown mixed results. Hospitalists do not appear to provide higher quality of care for pneumonia,1, 2 while results in heart failure are mixed.35 Each of these studies was conducted at a single site, and examined patient‐level effects. More recently, Vasilevskis et al6 assessed the association between the intensity of hospitalist use (measured as the percentage of patients admitted by hospitalists) and performance on process measures. In a cohort of 208 California hospitals, they found a significant improvement in performance on process measures in patients with acute myocardial infarction, heart failure, and pneumonia with increasing percentages of patients admitted by hospitalists.6
To date, no study has examined the association between the use of hospitalists and the publicly reported 30‐day mortality and readmission measures. Specifically, the Centers for Medicare and Medicaid Services (CMS) have developed and now publicly report risk‐standardized 30‐day mortality (RSMR) and readmission rates (RSRR) for Medicare patients hospitalized for 3 common and costly conditionsacute myocardial infarction (AMI), heart failure (HF), and pneumonia.7 Performance on these hospital‐based quality measures varies widely, and vary by hospital volume, ownership status, teaching status, and nurse staffing levels.813 However, even accounting for these characteristics leaves much of the variation in outcomes unexplained. We hypothesized that the presence of hospitalists within a hospital would be associated with higher performance on 30‐day mortality and 30‐day readmission measures for AMI, HF, and pneumonia. We further hypothesized that for hospitals using hospitalists, there would be a positive correlation between increasing percentage of patients admitted by hospitalists and performance on outcome measures. To test these hypotheses, we conducted a national survey of hospitalist leaders, linking data from survey responses to data on publicly reported outcome measures for AMI, HF, and pneumonia.
MATERIALS AND METHODS
Study Sites
Of the 4289 hospitals in operation in 2008, 1945 had 25 or more AMI discharges. We identified hospitals using American Hospital Association (AHA) data, calling hospitals up to 6 times each until we reached our target sample size of 600. Using this methodology, we contacted 1558 hospitals of a possible 1920 with AHA data; of the 1558 called, 598 provided survey results.
Survey Data
Our survey was adapted from the survey developed by Vasilevskis et al.6 The entire survey can be found in the Appendix (see Supporting Information in the online version of this article). Our key questions were: 1) Does your hospital have at least 1 hospitalist program or group? 2) Approximately what percentage of all medical patients in your hospital are admitted by hospitalists? The latter question was intended as an approximation of the intensity of hospitalist use, and has been used in prior studies.6, 14 A more direct measure was not feasible given the complexity of obtaining admission data for such a large and diverse set of hospitals. Respondents were also asked about hospitalist care of AMI, HF, and pneumonia patients. Given the low likelihood of precise estimation of hospitalist participation in care for specific conditions, the response choices were divided into percentage quartiles: 025, 2650, 5175, and 76100. Finally, participants were asked a number of questions regarding hospitalist organizational and clinical characteristics.
Survey Process
We obtained data regarding presence or absence of hospitalists and characteristics of the hospitalist services via phone‐ and fax‐administered survey (see Supporting Information, Appendix, in the online version of this article). Telephone and faxed surveys were administered between February 2010 and January 2011. Hospital telephone numbers were obtained from the 2008 AHA survey database and from a review of each hospital's website. Up to 6 attempts were made to obtain a completed survey from nonrespondents unless participation was specifically refused. Potential respondents were contacted in the following order: hospital medicine department leaders, hospital medicine clinical managers, vice president for medical affairs, chief medical officers, and other hospital executives with knowledge of the hospital medicine services. All respondents agreed with a question asking whether they had direct working knowledge of their hospital medicine services; contacts who said they did not have working knowledge of their hospital medicine services were asked to refer our surveyor to the appropriate person at their site. Absence of a hospitalist program was confirmed by contacting the Medical Staff Office.
Hospital Organizational and Patient‐Mix Characteristics
Hospital‐level organizational characteristics (eg, bed size, teaching status) and patient‐mix characteristics (eg, Medicare and Medicaid inpatient days) were obtained from the 2008 AHA survey database.
Outcome Performance Measures
The 30‐day risk‐standardized mortality and readmission rates (RSMR and RSRR) for 2008 for AMI, HF, and pneumonia were calculated for all admissions for people age 65 and over with traditional fee‐for‐service Medicare. Beneficiaries had to be enrolled for 12 months prior to their hospitalization for any of the 3 conditions, and had to have complete claims data available for that 12‐month period.7 These 6 outcome measures were constructed using hierarchical generalized linear models.1520 Using the RSMR for AMI as an example, for each hospital, the measure is estimated by dividing the predicted number of deaths within 30 days of admission for AMI by the expected number of deaths within 30 days of admission for AMI. This ratio is then divided by the national unadjusted 30‐day mortality rate for AMI, which is obtained using data on deaths from the Medicare beneficiary denominator file. Each measure is adjusted for patient characteristics such as age, gender, and comorbidities. All 6 measures are endorsed by the National Quality Forum (NQF) and are reported publicly by CMS on the Hospital Compare web site.
Statistical Analysis
Comparison of hospital‐ and patient‐level characteristics between hospitals with and without hospitalists was performed using chi‐square tests and Student t tests.
The primary outcome variables are the RSMRs and RSRRs for AMI, HF, and pneumonia. Multivariable linear regression models were used to assess the relationship between hospitals with at least 1 hospitalist group and each dependent variable. Models were adjusted for variables previously reported to be associated with quality of care. Hospital‐level characteristics included core‐based statistical area, teaching status, number of beds, region, safety‐net status, nursing staff ratio (number of registered nurse FTEs/number of hospital FTEs), and presence or absence of cardiac catheterization and coronary bypass capability. Patient‐level characteristics included Medicare and Medicaid inpatient days as a percentage of total inpatient days and percentage of admissions by race (black vs non‐black). The presence of hospitalists was correlated with each of the hospital and patient‐level characteristics. Further analyses of the subset of hospitals that use hospitalists included construction of multivariable linear regression models to assess the relationship between the percentage of patients admitted by hospitalists and the dependent variables. Models were adjusted for the same patient‐ and hospital‐level characteristics.
The institutional review boards at Yale University and University of California, San Francisco approved the study. All analyses were performed using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc, Cary, NC).
RESULTS
Characteristics of Participating Hospitals
Telephone, fax, and e‐mail surveys were attempted with 1558 hospitals; we received 598 completed surveys for a response rate of 40%. There was no difference between responders and nonresponders on any of the 6 outcome variables, the number of Medicare or Medicaid inpatient days, and the percentage of admissions by race. Responders and nonresponders were also similar in size, ownership, safety‐net and teaching status, nursing staff ratio, presence of cardiac catheterization and coronary bypass capability, and core‐based statistical area. They differed only on region of the country, where hospitals in the northwest Central and Pacific regions of the country had larger overall proportions of respondents. All hospitals provided information about the presence or absence of hospitalist programs. The majority of respondents were hospitalist clinical or administrative managers (n = 220) followed by hospitalist leaders (n = 106), other executives (n = 58), vice presidents for medical affairs (n = 39), and chief medical officers (n = 15). Each respondent indicated a working knowledge of their site's hospitalist utilization and practice characteristics. Absence of hospitalist utilization was confirmed by contact with the Medical Staff Office.
Comparisons of Sites With Hospitalists and Those Without Hospitalists
Hospitals with and without hospitalists differed by a number of organizational characteristics (Table 1). Sites with hospitalists were more likely to be larger, nonprofit teaching hospitals, located in metropolitan regions, and have cardiac surgical services. There was no difference in the hospitals' safety‐net status or RN staffing ratio. Hospitals with hospitalists admitted lower percentages of black patients.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| N (%) | N (%) | P Value | |
| |||
| Core‐based statistical area | <0.0001 | ||
| Division | 94 (21.9%) | 53 (31.4%) | |
| Metro | 275 (64.1%) | 72 (42.6%) | |
| Micro | 52 (12.1%) | 38 (22.5%) | |
| Rural | 8 (1.9%) | 6 (3.6%) | |
| Owner | 0.0003 | ||
| Public | 47 (11.0%) | 20 (11.8%) | |
| Nonprofit | 333 (77.6%) | 108 (63.9%) | |
| Private | 49 (11.4%) | 41 (24.3%) | |
| Teaching status | <0.0001 | ||
| COTH | 54 (12.6%) | 7 (4.1%) | |
| Teaching | 110 (25.6%) | 26 (15.4%) | |
| Other | 265 (61.8%) | 136 (80.5%) | |
| Cardiac type | 0.0003 | ||
| CABG | 286 (66.7%) | 86 (50.9%) | |
| CATH | 79 (18.4%) | 36 (21.3%) | |
| Other | 64 (14.9%) | 47 (27.8%) | |
| Region | 0.007 | ||
| New England | 35 (8.2%) | 3 (1.8%) | |
| Middle Atlantic | 60 (14.0%) | 29 (17.2%) | |
| South Atlantic | 78 (18.2%) | 23 (13.6%) | |
| NE Central | 60 (14.0%) | 35 (20.7%) | |
| SE Central | 31 (7.2%) | 10 (5.9%) | |
| NW Central | 38 (8.9%) | 23 (13.6%) | |
| SW Central | 41 (9.6%) | 21 (12.4%) | |
| Mountain | 22 (5.1%) | 3 (1.8%) | |
| Pacific | 64 (14.9%) | 22 (13.0%) | |
| Safety‐net | 0.53 | ||
| Yes | 72 (16.8%) | 32 (18.9%) | |
| No | 357 (83.2%) | 137 (81.1%) | |
| Mean (SD) | Mean (SD) | P value | |
| RN staffing ratio (n = 455) | 27.3 (17.0) | 26.1 (7.6) | 0.28 |
| Total beds | 315.0 (216.6) | 214.8 (136.0) | <0.0001 |
| % Medicare inpatient days | 47.2 (42) | 49.7 (41) | 0.19 |
| % Medicaid inpatient days | 18.5 (28) | 21.4 (46) | 0.16 |
| % Black | 7.6 (9.6) | 10.6 (17.4) | 0.03 |
Characteristics of Hospitalist Programs and Responsibilities
Of the 429 sites reporting use of hospitalists, the median percentage of patients admitted by hospitalists was 60%, with an interquartile range (IQR) of 35% to 80%. The median number of full‐time equivalent hospitalists per hospital was 8 with an IQR of 5 to 14. The IQR reflects the middle 50% of the distribution of responses, and is not affected by outliers or extreme values. Additional characteristics of hospitalist programs can be found in Table 2. The estimated percentage of patients with AMI, HF, and pneumonia cared for by hospitalists varied considerably, with fewer patients with AMI and more patients with pneumonia under hospitalist care. Overall, a majority of hospitalist groups provided the following services: care of critical care patients, emergency department admission screening, observation unit coverage, coverage for cardiac arrests and rapid response teams, quality improvement or utilization review activities, development of hospital practice guidelines, and participation in implementation of major hospital system projects (such as implementation of an electronic health record system).
| N (%) | |
|---|---|
| |
| Date program established | |
| 19871994 | 9 (2.2%) |
| 19952002 | 130 (32.1%) |
| 20032011 | 266 (65.7%) |
| Missing date | 24 |
| No. of hospitalist FTEs | |
| Median (IQR) | 8 (5, 14) |
| Percent of medical patients admitted by hospitalists | |
| Median (IQR) | 60% (35, 80) |
| No. of hospitalists groups | |
| 1 | 333 (77.6%) |
| 2 | 54 (12.6%) |
| 3 | 36 (8.4%) |
| Don't know | 6 (1.4%) |
| Employment of hospitalists (not mutually exclusive) | |
| Hospital system | 98 (22.8%) |
| Hospital | 185 (43.1%) |
| Local physician practice group | 62 (14.5%) |
| Hospitalist physician practice group (local) | 83 (19.3%) |
| Hospitalist physician practice group (national/regional) | 36 (8.4%) |
| Other/unknown | 36 (8.4%) |
| Any 24‐hr in‐house coverage by hospitalists | |
| Yes | 329 (76.7%) |
| No | 98 (22.8%) |
| 3 | 1 (0.2%) |
| Unknown | 1 (0.2%) |
| No. of hospitalist international medical graduates | |
| Median (IQR) | 3 (1, 6) |
| No. of hospitalists that are <1 yr out of residency | |
| Median (IQR) | 1 (0, 2) |
| Percent of patients with AMI cared for by hospitalists | |
| 0%25% | 148 (34.5%) |
| 26%50% | 67 (15.6%) |
| 51%75% | 50 (11.7%) |
| 76%100% | 54 (12.6%) |
| Don't know | 110 (25.6%) |
| Percent of patients with heart failure cared for by hospitalists | |
| 0%25% | 79 (18.4%) |
| 26%50% | 78 (18.2%) |
| 51%75% | 75 (17.5%) |
| 76%100% | 84 (19.6%) |
| Don't know | 113 (26.3%) |
| Percent of patients with pneumonia cared for by hospitalists | |
| 0%25% | 47 (11.0%) |
| 26%50% | 61 (14.3%) |
| 51%75% | 74 (17.3%) |
| 76%100% | 141 (32.9%) |
| Don't know | 105 (24.5%) |
| Hospitalist provision of services | |
| Care of critical care patients | |
| Hospitalists provide service | 346 (80.7%) |
| Hospitalists do not provide service | 80 (18.7%) |
| Don't know | 3 (0.7%) |
| Emergency department admission screening | |
| Hospitalists provide service | 281 (65.5%) |
| Hospitalists do not provide service | 143 (33.3%) |
| Don't know | 5 (1.2%) |
| Observation unit coverage | |
| Hospitalists provide service | 359 (83.7%) |
| Hospitalists do not provide service | 64 (14.9%) |
| Don't know | 6 (1.4%) |
| Emergency department coverage | |
| Hospitalists provide service | 145 (33.8%) |
| Hospitalists do not provide service | 280 (65.3%) |
| Don't know | 4 (0.9%) |
| Coverage for cardiac arrests | |
| Hospitalists provide service | 283 (66.0%) |
| Hospitalists do not provide service | 135 (31.5%) |
| Don't know | 11 (2.6%) |
| Rapid response team coverage | |
| Hospitalists provide service | 240 (55.9%) |
| Hospitalists do not provide service | 168 (39.2%) |
| Don't know | 21 (4.9%) |
| Quality improvement or utilization review | |
| Hospitalists provide service | 376 (87.7%) |
| Hospitalists do not provide service | 37 (8.6%) |
| Don't know | 16 (3.7%) |
| Hospital practice guideline development | |
| Hospitalists provide service | 339 (79.0%) |
| Hospitalists do not provide service | 55 (12.8%) |
| Don't know | 35 (8.2%) |
| Implementation of major hospital system projects | |
| Hospitalists provide service | 309 (72.0%) |
| Hospitalists do not provide service | 96 (22.4%) |
| Don't know | 24 (5.6%) |
Relationship Between Hospitalist Utilization and Outcomes
Tables 3 and 4 show the comparisons between hospitals with and without hospitalists on each of the 6 outcome measures. In the bivariate analysis (Table 3), there was no statistically significant difference between groups on any of the outcome measures with the exception of the risk‐stratified readmission rate for heart failure. Sites with hospitalists had a lower RSRR for HF than sites without hospitalists (24.7% vs 25.4%, P < 0.0001). These results were similar in the multivariable models as seen in Table 4, in which the beta estimate (slope) was not significantly different for hospitals utilizing hospitalists compared to those that did not, on all measures except the RSRR for HF. For the subset of hospitals that used hospitalists, there was no statistically significant change in any of the 6 outcome measures, with increasing percentage of patients admitted by hospitalists. Table 5 demonstrates that for each RSMR and RSRR, the slope did not consistently increase or decrease with incrementally higher percentages of patients admitted by hospitalists, and the confidence intervals for all estimates crossed zero.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| Outcome Measure | Mean % (SD) | Mean (SD) | P Value |
| |||
| MI RSMR | 16.0 (1.6) | 16.1 (1.5) | 0.56 |
| MI RSRR | 19.9 (0.88) | 20.0 (0.86) | 0.16 |
| HF RSMR | 11.3 (1.4) | 11.3 (1.4) | 0.77 |
| HF RSRR | 24.7 (1.6) | 25.4 (1.8) | <0.0001 |
| Pneumonia RSMR | 11.7 (1.7) | 12.0 (1.7) | 0.08 |
| Pneumonia RSRR | 18.2 (1.2) | 18.3 (1.1) | 0.28 |
| Adjusted beta estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Hospitalist | 0.001 (0.002, 004) |
| MI RSRR | |
| Hospitalist | 0.001 (0.002, 0.001) |
| HF RSMR | |
| Hospitalist | 0.0004 (0.002, 0.003) |
| HF RSRR | |
| Hospitalist | 0.006 (0.009, 0.003) |
| Pneumonia RSMR | |
| Hospitalist | 0.002 (0.005, 0.001) |
| Pneumonia RSRR | |
| Hospitalist | 0.00001 (0.002, 0.002) |
| Adjusted Beta Estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Percent admit | |
| 0%30% | 0.003 (0.007, 0.002) |
| 32%48% | 0.001 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.004 (0.001, 0.009) |
| 85% | 0.004 (0.009, 0.001) |
| MI RSRR | |
| Percent admit | |
| 0%30% | 0.001 (0.002, 0.004) |
| 32%48% | 0.001 (0.004, 0.004) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.002, 0.004) |
| 85% | 0.001 (0.002, 0.004) |
| HF RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.005, 0.003) |
| 32%48% | 0.002 (0.007, 0.003) |
| 50%66% | Ref |
| 70%80% | 0.002 (0.006, 0.002) |
| 85% | 0.001 (0.004, 0.005) |
| HF RSRR | |
| Percent admit | |
| 0%30% | 0.002 (0.004, 0.007) |
| 32%48% | 0.0003 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.005, 0.004) |
| 85% | 0.002 (0.007, 0.003) |
| Pneumonia RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.004, 0.006) |
| 32%48% | 0.00001 (0.006, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.004, 0.006) |
| 85% | 0.001 (0.006, 0.005) |
| Pneumonia RSRR | |
| Percent admit | |
| 0%30% | 0.0002 (0.004, 0.003) |
| 32%48% | 0.004 (0.0003, 0.008) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.003, 0.004) |
| 85% | 0.002 (0.002, 0.006) |
DISCUSSION
In this national survey of hospitals, we did not find a significant association between the use of hospitalists and hospitals' performance on 30‐day mortality or readmissions measures for AMI, HF, or pneumonia. While there was a statistically lower 30‐day risk‐standardized readmission rate measure for the heart failure measure among hospitals that use hospitalists, the effect size was small. The survey response rate of 40% is comparable to other surveys of physicians and other healthcare personnel, however, there were no significant differences between responders and nonresponders, so the potential for response bias, while present, is small.
Contrary to the findings of a recent study,21 we did not find a higher readmission rate for any of the 3 conditions in hospitals with hospitalist programs. One advantage of our study is the use of more robust risk‐adjustment methods. Our study used NQF‐endorsed risk‐standardized measures of readmission, which capture readmissions to any hospital for common, high priority conditions where the impact of care coordination and discontinuity of care are paramount. The models use administrative claims data, but have been validated by medical record data. Another advantage is that our study focused on a time period when hospital readmissions were a standard quality benchmark and increasing priority for hospitals, hospitalists, and community‐based care delivery systems. While our study is not able to discern whether patients had primary care physicians or the reason for admission to a hospitalist's care, our data do suggest that hospitalists continue to care for a large percentage of hospitalized patients. Moreover, increasing the proportion of patients being admitted to hospitalists did not affect the risk for readmission, providing perhaps reassuring evidence (or lack of proof) for a direct association between use of hospitalist systems and higher risk for readmission.
While hospitals with hospitalists clearly did not have better mortality or readmission rates, an alternate viewpoint might hold that, despite concerns that hospitalists negatively impact care continuity, our data do not demonstrate an association between readmission rates and use of hospitalist services. It is possible that hospitals that have hospitalists may have more ability to invest in hospital‐based systems of care,22 an association which may incorporate any hospitalist effect, but our results were robust even after testing whether adjustment for hospital factors (such as profit status, size) affected our results.
It is also possible that secular trends in hospitals or hospitalist systems affected our results. A handful of single‐site studies carried out soon after the hospitalist model's earliest descriptions found a reduction in mortality and readmission rates with the implementation of a hospitalist program.2325 Alternatively, it may be that there has been a dilution of the effect of hospitalists as often occurs when any new innovation is spread from early adopter sites to routine practice. Consistent with other multicenter studies from recent eras,21, 26 our article's findings do not demonstrate an association between hospitalists and improved outcomes. Unlike other multicenter studies, we had access to disease‐specific risk‐adjustment methodologies, which may partially account for referral biases related to patient‐specific measures of acute or chronic illness severity.
Changes in the hospitalist effect over time have a number of explanations, some of which are relevant to our study. Recent evidence suggests that complex organizational characteristics, such as organizational values and goals, may contribute to performance on 30‐day mortality for AMI rather than specific processes and protocols27; intense focus on AMI as a quality improvement target is emblematic of a number of national initiatives that may have affected our results. Interestingly, hospitalist systems have changed over time as well. Early in the hospitalist movement, hospitalist systems were implemented largely at the behest of hospitals trying to reduce costs. In recent years, however, hospitalist systems are at least as frequently being implemented because outpatient‐based physicians or surgeons request hospitalists; hospitalists have been focused on care of uncoveredpatients, since the model's earliest description. In addition, some hospitals invest in hospitalist programs based on perceived ability of hospitalists to improve quality and achieve better patient outcomes in an era of payment increasingly being linked to quality of care metrics.
Our study has several limitations, six of which are noted here. First, while the hospitalist model has been widely embraced in the adult medicine field, in the absence of board certification, there is no gold standard definition of a hospitalist. It is therefore possible that some respondents may have represented groups that were identified incorrectly as hospitalists. Second, the data for the primary independent variable of interest was based upon self‐report and, therefore, subject to recall bias and potential misclassification of results. Respondents were not aware of our hypothesis, so the bias should not have been in one particular direction. Third, the data for the outcome variables are from 2008. They may, therefore, not reflect organizational enhancements related to use of hospitalists that are in process, and take years to yield downstream improvements on performance metrics. In addition, of the 429 hospitals that have hospitalist programs, 46 programs were initiated after 2008. While national performance on the 6 outcome variables has been relatively static over time,7 any significant change in hospital performance on these metrics since 2008 could suggest an overestimation or underestimation of the effect of hospitalist programs on patient outcomes. Fourth, we were not able to adjust for additional hospital or health system level characteristics that may be associated with hospitalist use or patient outcomes. Fifth, our regression models had significant collinearity, in that the presence of hospitalists was correlated with each of the covariates. However, this finding would indicate that our estimates may be overly conservative and could have contributed to our nonsignificant findings. Finally, outcomes for 2 of the 3 clinical conditions measured are ones for which hospitalists may less frequently provide care: acute myocardial infarction and heart failure. Outcome measures more relevant for hospitalists may be all‐condition, all‐cause, 30‐day mortality and readmission.
This work adds to the growing body of literature examining the impact of hospitalists on quality of care. To our knowledge, it is the first study to assess the association between hospitalist use and performance on outcome metrics at a national level. While our findings suggest that use of hospitalists alone may not lead to improved performance on outcome measures, a parallel body of research is emerging implicating broader system and organizational factors as key to high performance on outcome measures. It is likely that multiple factors contribute to performance on outcome measures, including type and mix of hospital personnel, patient care processes and workflow, and system level attributes. Comparative effectiveness and implementation research that assess the contextual factors and interventions that lead to successful system improvement and better performance is increasingly needed. It is unlikely that a single factor, such as hospitalist use, will significantly impact 30‐day mortality or readmission and, therefore, multifactorial interventions are likely required. In addition, hospitalist use is a complex intervention as the structure, processes, training, experience, role in the hospital system, and other factors (including quality of hospitalists or the hospitalist program) vary across programs. Rather than focusing on the volume of care delivered by hospitalists, hospitals will likely need to support hospital medicine programs that have the time and expertise to devote to improving the quality and value of care delivered across the hospital system. This study highlights that interventions leading to improvement on core outcome measures are more complex than simply having a hospital medicine program.
Acknowledgements
The authors acknowledge Judy Maselli, MPH, Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, for her assistance with statistical analyses and preparation of tables.
Disclosures: Work on this project was supported by the Robert Wood Johnson Clinical Scholars Program (K.G.); California Healthcare Foundation grant 15763 (A.D.A.); and a grant from the National Heart, Lung, and Blood Institute (NHLBI), study 1U01HL105270‐02 (H.M.K.). Dr Krumholz is the chair of the Cardiac Scientific Advisory Board for United Health and has a research grant with Medtronic through Yale University; Dr Auerbach has a grant through the National Heart, Lung, and Blood Institute (NHLBI). The authors have no other disclosures to report.
The past several years have seen a dramatic increase in the percentage of patients cared for by hospitalists, yet an emerging body of literature examining the association between care given by hospitalists and performance on a number of process measures has shown mixed results. Hospitalists do not appear to provide higher quality of care for pneumonia,1, 2 while results in heart failure are mixed.35 Each of these studies was conducted at a single site, and examined patient‐level effects. More recently, Vasilevskis et al6 assessed the association between the intensity of hospitalist use (measured as the percentage of patients admitted by hospitalists) and performance on process measures. In a cohort of 208 California hospitals, they found a significant improvement in performance on process measures in patients with acute myocardial infarction, heart failure, and pneumonia with increasing percentages of patients admitted by hospitalists.6
To date, no study has examined the association between the use of hospitalists and the publicly reported 30‐day mortality and readmission measures. Specifically, the Centers for Medicare and Medicaid Services (CMS) have developed and now publicly report risk‐standardized 30‐day mortality (RSMR) and readmission rates (RSRR) for Medicare patients hospitalized for 3 common and costly conditionsacute myocardial infarction (AMI), heart failure (HF), and pneumonia.7 Performance on these hospital‐based quality measures varies widely, and vary by hospital volume, ownership status, teaching status, and nurse staffing levels.813 However, even accounting for these characteristics leaves much of the variation in outcomes unexplained. We hypothesized that the presence of hospitalists within a hospital would be associated with higher performance on 30‐day mortality and 30‐day readmission measures for AMI, HF, and pneumonia. We further hypothesized that for hospitals using hospitalists, there would be a positive correlation between increasing percentage of patients admitted by hospitalists and performance on outcome measures. To test these hypotheses, we conducted a national survey of hospitalist leaders, linking data from survey responses to data on publicly reported outcome measures for AMI, HF, and pneumonia.
MATERIALS AND METHODS
Study Sites
Of the 4289 hospitals in operation in 2008, 1945 had 25 or more AMI discharges. We identified hospitals using American Hospital Association (AHA) data, calling hospitals up to 6 times each until we reached our target sample size of 600. Using this methodology, we contacted 1558 hospitals of a possible 1920 with AHA data; of the 1558 called, 598 provided survey results.
Survey Data
Our survey was adapted from the survey developed by Vasilevskis et al.6 The entire survey can be found in the Appendix (see Supporting Information in the online version of this article). Our key questions were: 1) Does your hospital have at least 1 hospitalist program or group? 2) Approximately what percentage of all medical patients in your hospital are admitted by hospitalists? The latter question was intended as an approximation of the intensity of hospitalist use, and has been used in prior studies.6, 14 A more direct measure was not feasible given the complexity of obtaining admission data for such a large and diverse set of hospitals. Respondents were also asked about hospitalist care of AMI, HF, and pneumonia patients. Given the low likelihood of precise estimation of hospitalist participation in care for specific conditions, the response choices were divided into percentage quartiles: 025, 2650, 5175, and 76100. Finally, participants were asked a number of questions regarding hospitalist organizational and clinical characteristics.
Survey Process
We obtained data regarding presence or absence of hospitalists and characteristics of the hospitalist services via phone‐ and fax‐administered survey (see Supporting Information, Appendix, in the online version of this article). Telephone and faxed surveys were administered between February 2010 and January 2011. Hospital telephone numbers were obtained from the 2008 AHA survey database and from a review of each hospital's website. Up to 6 attempts were made to obtain a completed survey from nonrespondents unless participation was specifically refused. Potential respondents were contacted in the following order: hospital medicine department leaders, hospital medicine clinical managers, vice president for medical affairs, chief medical officers, and other hospital executives with knowledge of the hospital medicine services. All respondents agreed with a question asking whether they had direct working knowledge of their hospital medicine services; contacts who said they did not have working knowledge of their hospital medicine services were asked to refer our surveyor to the appropriate person at their site. Absence of a hospitalist program was confirmed by contacting the Medical Staff Office.
Hospital Organizational and Patient‐Mix Characteristics
Hospital‐level organizational characteristics (eg, bed size, teaching status) and patient‐mix characteristics (eg, Medicare and Medicaid inpatient days) were obtained from the 2008 AHA survey database.
Outcome Performance Measures
The 30‐day risk‐standardized mortality and readmission rates (RSMR and RSRR) for 2008 for AMI, HF, and pneumonia were calculated for all admissions for people age 65 and over with traditional fee‐for‐service Medicare. Beneficiaries had to be enrolled for 12 months prior to their hospitalization for any of the 3 conditions, and had to have complete claims data available for that 12‐month period.7 These 6 outcome measures were constructed using hierarchical generalized linear models.1520 Using the RSMR for AMI as an example, for each hospital, the measure is estimated by dividing the predicted number of deaths within 30 days of admission for AMI by the expected number of deaths within 30 days of admission for AMI. This ratio is then divided by the national unadjusted 30‐day mortality rate for AMI, which is obtained using data on deaths from the Medicare beneficiary denominator file. Each measure is adjusted for patient characteristics such as age, gender, and comorbidities. All 6 measures are endorsed by the National Quality Forum (NQF) and are reported publicly by CMS on the Hospital Compare web site.
Statistical Analysis
Comparison of hospital‐ and patient‐level characteristics between hospitals with and without hospitalists was performed using chi‐square tests and Student t tests.
The primary outcome variables are the RSMRs and RSRRs for AMI, HF, and pneumonia. Multivariable linear regression models were used to assess the relationship between hospitals with at least 1 hospitalist group and each dependent variable. Models were adjusted for variables previously reported to be associated with quality of care. Hospital‐level characteristics included core‐based statistical area, teaching status, number of beds, region, safety‐net status, nursing staff ratio (number of registered nurse FTEs/number of hospital FTEs), and presence or absence of cardiac catheterization and coronary bypass capability. Patient‐level characteristics included Medicare and Medicaid inpatient days as a percentage of total inpatient days and percentage of admissions by race (black vs non‐black). The presence of hospitalists was correlated with each of the hospital and patient‐level characteristics. Further analyses of the subset of hospitals that use hospitalists included construction of multivariable linear regression models to assess the relationship between the percentage of patients admitted by hospitalists and the dependent variables. Models were adjusted for the same patient‐ and hospital‐level characteristics.
The institutional review boards at Yale University and University of California, San Francisco approved the study. All analyses were performed using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc, Cary, NC).
RESULTS
Characteristics of Participating Hospitals
Telephone, fax, and e‐mail surveys were attempted with 1558 hospitals; we received 598 completed surveys for a response rate of 40%. There was no difference between responders and nonresponders on any of the 6 outcome variables, the number of Medicare or Medicaid inpatient days, and the percentage of admissions by race. Responders and nonresponders were also similar in size, ownership, safety‐net and teaching status, nursing staff ratio, presence of cardiac catheterization and coronary bypass capability, and core‐based statistical area. They differed only on region of the country, where hospitals in the northwest Central and Pacific regions of the country had larger overall proportions of respondents. All hospitals provided information about the presence or absence of hospitalist programs. The majority of respondents were hospitalist clinical or administrative managers (n = 220) followed by hospitalist leaders (n = 106), other executives (n = 58), vice presidents for medical affairs (n = 39), and chief medical officers (n = 15). Each respondent indicated a working knowledge of their site's hospitalist utilization and practice characteristics. Absence of hospitalist utilization was confirmed by contact with the Medical Staff Office.
Comparisons of Sites With Hospitalists and Those Without Hospitalists
Hospitals with and without hospitalists differed by a number of organizational characteristics (Table 1). Sites with hospitalists were more likely to be larger, nonprofit teaching hospitals, located in metropolitan regions, and have cardiac surgical services. There was no difference in the hospitals' safety‐net status or RN staffing ratio. Hospitals with hospitalists admitted lower percentages of black patients.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| N (%) | N (%) | P Value | |
| |||
| Core‐based statistical area | <0.0001 | ||
| Division | 94 (21.9%) | 53 (31.4%) | |
| Metro | 275 (64.1%) | 72 (42.6%) | |
| Micro | 52 (12.1%) | 38 (22.5%) | |
| Rural | 8 (1.9%) | 6 (3.6%) | |
| Owner | 0.0003 | ||
| Public | 47 (11.0%) | 20 (11.8%) | |
| Nonprofit | 333 (77.6%) | 108 (63.9%) | |
| Private | 49 (11.4%) | 41 (24.3%) | |
| Teaching status | <0.0001 | ||
| COTH | 54 (12.6%) | 7 (4.1%) | |
| Teaching | 110 (25.6%) | 26 (15.4%) | |
| Other | 265 (61.8%) | 136 (80.5%) | |
| Cardiac type | 0.0003 | ||
| CABG | 286 (66.7%) | 86 (50.9%) | |
| CATH | 79 (18.4%) | 36 (21.3%) | |
| Other | 64 (14.9%) | 47 (27.8%) | |
| Region | 0.007 | ||
| New England | 35 (8.2%) | 3 (1.8%) | |
| Middle Atlantic | 60 (14.0%) | 29 (17.2%) | |
| South Atlantic | 78 (18.2%) | 23 (13.6%) | |
| NE Central | 60 (14.0%) | 35 (20.7%) | |
| SE Central | 31 (7.2%) | 10 (5.9%) | |
| NW Central | 38 (8.9%) | 23 (13.6%) | |
| SW Central | 41 (9.6%) | 21 (12.4%) | |
| Mountain | 22 (5.1%) | 3 (1.8%) | |
| Pacific | 64 (14.9%) | 22 (13.0%) | |
| Safety‐net | 0.53 | ||
| Yes | 72 (16.8%) | 32 (18.9%) | |
| No | 357 (83.2%) | 137 (81.1%) | |
| Mean (SD) | Mean (SD) | P value | |
| RN staffing ratio (n = 455) | 27.3 (17.0) | 26.1 (7.6) | 0.28 |
| Total beds | 315.0 (216.6) | 214.8 (136.0) | <0.0001 |
| % Medicare inpatient days | 47.2 (42) | 49.7 (41) | 0.19 |
| % Medicaid inpatient days | 18.5 (28) | 21.4 (46) | 0.16 |
| % Black | 7.6 (9.6) | 10.6 (17.4) | 0.03 |
Characteristics of Hospitalist Programs and Responsibilities
Of the 429 sites reporting use of hospitalists, the median percentage of patients admitted by hospitalists was 60%, with an interquartile range (IQR) of 35% to 80%. The median number of full‐time equivalent hospitalists per hospital was 8 with an IQR of 5 to 14. The IQR reflects the middle 50% of the distribution of responses, and is not affected by outliers or extreme values. Additional characteristics of hospitalist programs can be found in Table 2. The estimated percentage of patients with AMI, HF, and pneumonia cared for by hospitalists varied considerably, with fewer patients with AMI and more patients with pneumonia under hospitalist care. Overall, a majority of hospitalist groups provided the following services: care of critical care patients, emergency department admission screening, observation unit coverage, coverage for cardiac arrests and rapid response teams, quality improvement or utilization review activities, development of hospital practice guidelines, and participation in implementation of major hospital system projects (such as implementation of an electronic health record system).
| N (%) | |
|---|---|
| |
| Date program established | |
| 19871994 | 9 (2.2%) |
| 19952002 | 130 (32.1%) |
| 20032011 | 266 (65.7%) |
| Missing date | 24 |
| No. of hospitalist FTEs | |
| Median (IQR) | 8 (5, 14) |
| Percent of medical patients admitted by hospitalists | |
| Median (IQR) | 60% (35, 80) |
| No. of hospitalists groups | |
| 1 | 333 (77.6%) |
| 2 | 54 (12.6%) |
| 3 | 36 (8.4%) |
| Don't know | 6 (1.4%) |
| Employment of hospitalists (not mutually exclusive) | |
| Hospital system | 98 (22.8%) |
| Hospital | 185 (43.1%) |
| Local physician practice group | 62 (14.5%) |
| Hospitalist physician practice group (local) | 83 (19.3%) |
| Hospitalist physician practice group (national/regional) | 36 (8.4%) |
| Other/unknown | 36 (8.4%) |
| Any 24‐hr in‐house coverage by hospitalists | |
| Yes | 329 (76.7%) |
| No | 98 (22.8%) |
| 3 | 1 (0.2%) |
| Unknown | 1 (0.2%) |
| No. of hospitalist international medical graduates | |
| Median (IQR) | 3 (1, 6) |
| No. of hospitalists that are <1 yr out of residency | |
| Median (IQR) | 1 (0, 2) |
| Percent of patients with AMI cared for by hospitalists | |
| 0%25% | 148 (34.5%) |
| 26%50% | 67 (15.6%) |
| 51%75% | 50 (11.7%) |
| 76%100% | 54 (12.6%) |
| Don't know | 110 (25.6%) |
| Percent of patients with heart failure cared for by hospitalists | |
| 0%25% | 79 (18.4%) |
| 26%50% | 78 (18.2%) |
| 51%75% | 75 (17.5%) |
| 76%100% | 84 (19.6%) |
| Don't know | 113 (26.3%) |
| Percent of patients with pneumonia cared for by hospitalists | |
| 0%25% | 47 (11.0%) |
| 26%50% | 61 (14.3%) |
| 51%75% | 74 (17.3%) |
| 76%100% | 141 (32.9%) |
| Don't know | 105 (24.5%) |
| Hospitalist provision of services | |
| Care of critical care patients | |
| Hospitalists provide service | 346 (80.7%) |
| Hospitalists do not provide service | 80 (18.7%) |
| Don't know | 3 (0.7%) |
| Emergency department admission screening | |
| Hospitalists provide service | 281 (65.5%) |
| Hospitalists do not provide service | 143 (33.3%) |
| Don't know | 5 (1.2%) |
| Observation unit coverage | |
| Hospitalists provide service | 359 (83.7%) |
| Hospitalists do not provide service | 64 (14.9%) |
| Don't know | 6 (1.4%) |
| Emergency department coverage | |
| Hospitalists provide service | 145 (33.8%) |
| Hospitalists do not provide service | 280 (65.3%) |
| Don't know | 4 (0.9%) |
| Coverage for cardiac arrests | |
| Hospitalists provide service | 283 (66.0%) |
| Hospitalists do not provide service | 135 (31.5%) |
| Don't know | 11 (2.6%) |
| Rapid response team coverage | |
| Hospitalists provide service | 240 (55.9%) |
| Hospitalists do not provide service | 168 (39.2%) |
| Don't know | 21 (4.9%) |
| Quality improvement or utilization review | |
| Hospitalists provide service | 376 (87.7%) |
| Hospitalists do not provide service | 37 (8.6%) |
| Don't know | 16 (3.7%) |
| Hospital practice guideline development | |
| Hospitalists provide service | 339 (79.0%) |
| Hospitalists do not provide service | 55 (12.8%) |
| Don't know | 35 (8.2%) |
| Implementation of major hospital system projects | |
| Hospitalists provide service | 309 (72.0%) |
| Hospitalists do not provide service | 96 (22.4%) |
| Don't know | 24 (5.6%) |
Relationship Between Hospitalist Utilization and Outcomes
Tables 3 and 4 show the comparisons between hospitals with and without hospitalists on each of the 6 outcome measures. In the bivariate analysis (Table 3), there was no statistically significant difference between groups on any of the outcome measures with the exception of the risk‐stratified readmission rate for heart failure. Sites with hospitalists had a lower RSRR for HF than sites without hospitalists (24.7% vs 25.4%, P < 0.0001). These results were similar in the multivariable models as seen in Table 4, in which the beta estimate (slope) was not significantly different for hospitals utilizing hospitalists compared to those that did not, on all measures except the RSRR for HF. For the subset of hospitals that used hospitalists, there was no statistically significant change in any of the 6 outcome measures, with increasing percentage of patients admitted by hospitalists. Table 5 demonstrates that for each RSMR and RSRR, the slope did not consistently increase or decrease with incrementally higher percentages of patients admitted by hospitalists, and the confidence intervals for all estimates crossed zero.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| Outcome Measure | Mean % (SD) | Mean (SD) | P Value |
| |||
| MI RSMR | 16.0 (1.6) | 16.1 (1.5) | 0.56 |
| MI RSRR | 19.9 (0.88) | 20.0 (0.86) | 0.16 |
| HF RSMR | 11.3 (1.4) | 11.3 (1.4) | 0.77 |
| HF RSRR | 24.7 (1.6) | 25.4 (1.8) | <0.0001 |
| Pneumonia RSMR | 11.7 (1.7) | 12.0 (1.7) | 0.08 |
| Pneumonia RSRR | 18.2 (1.2) | 18.3 (1.1) | 0.28 |
| Adjusted beta estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Hospitalist | 0.001 (0.002, 004) |
| MI RSRR | |
| Hospitalist | 0.001 (0.002, 0.001) |
| HF RSMR | |
| Hospitalist | 0.0004 (0.002, 0.003) |
| HF RSRR | |
| Hospitalist | 0.006 (0.009, 0.003) |
| Pneumonia RSMR | |
| Hospitalist | 0.002 (0.005, 0.001) |
| Pneumonia RSRR | |
| Hospitalist | 0.00001 (0.002, 0.002) |
| Adjusted Beta Estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Percent admit | |
| 0%30% | 0.003 (0.007, 0.002) |
| 32%48% | 0.001 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.004 (0.001, 0.009) |
| 85% | 0.004 (0.009, 0.001) |
| MI RSRR | |
| Percent admit | |
| 0%30% | 0.001 (0.002, 0.004) |
| 32%48% | 0.001 (0.004, 0.004) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.002, 0.004) |
| 85% | 0.001 (0.002, 0.004) |
| HF RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.005, 0.003) |
| 32%48% | 0.002 (0.007, 0.003) |
| 50%66% | Ref |
| 70%80% | 0.002 (0.006, 0.002) |
| 85% | 0.001 (0.004, 0.005) |
| HF RSRR | |
| Percent admit | |
| 0%30% | 0.002 (0.004, 0.007) |
| 32%48% | 0.0003 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.005, 0.004) |
| 85% | 0.002 (0.007, 0.003) |
| Pneumonia RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.004, 0.006) |
| 32%48% | 0.00001 (0.006, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.004, 0.006) |
| 85% | 0.001 (0.006, 0.005) |
| Pneumonia RSRR | |
| Percent admit | |
| 0%30% | 0.0002 (0.004, 0.003) |
| 32%48% | 0.004 (0.0003, 0.008) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.003, 0.004) |
| 85% | 0.002 (0.002, 0.006) |
DISCUSSION
In this national survey of hospitals, we did not find a significant association between the use of hospitalists and hospitals' performance on 30‐day mortality or readmissions measures for AMI, HF, or pneumonia. While there was a statistically lower 30‐day risk‐standardized readmission rate measure for the heart failure measure among hospitals that use hospitalists, the effect size was small. The survey response rate of 40% is comparable to other surveys of physicians and other healthcare personnel, however, there were no significant differences between responders and nonresponders, so the potential for response bias, while present, is small.
Contrary to the findings of a recent study,21 we did not find a higher readmission rate for any of the 3 conditions in hospitals with hospitalist programs. One advantage of our study is the use of more robust risk‐adjustment methods. Our study used NQF‐endorsed risk‐standardized measures of readmission, which capture readmissions to any hospital for common, high priority conditions where the impact of care coordination and discontinuity of care are paramount. The models use administrative claims data, but have been validated by medical record data. Another advantage is that our study focused on a time period when hospital readmissions were a standard quality benchmark and increasing priority for hospitals, hospitalists, and community‐based care delivery systems. While our study is not able to discern whether patients had primary care physicians or the reason for admission to a hospitalist's care, our data do suggest that hospitalists continue to care for a large percentage of hospitalized patients. Moreover, increasing the proportion of patients being admitted to hospitalists did not affect the risk for readmission, providing perhaps reassuring evidence (or lack of proof) for a direct association between use of hospitalist systems and higher risk for readmission.
While hospitals with hospitalists clearly did not have better mortality or readmission rates, an alternate viewpoint might hold that, despite concerns that hospitalists negatively impact care continuity, our data do not demonstrate an association between readmission rates and use of hospitalist services. It is possible that hospitals that have hospitalists may have more ability to invest in hospital‐based systems of care,22 an association which may incorporate any hospitalist effect, but our results were robust even after testing whether adjustment for hospital factors (such as profit status, size) affected our results.
It is also possible that secular trends in hospitals or hospitalist systems affected our results. A handful of single‐site studies carried out soon after the hospitalist model's earliest descriptions found a reduction in mortality and readmission rates with the implementation of a hospitalist program.2325 Alternatively, it may be that there has been a dilution of the effect of hospitalists as often occurs when any new innovation is spread from early adopter sites to routine practice. Consistent with other multicenter studies from recent eras,21, 26 our article's findings do not demonstrate an association between hospitalists and improved outcomes. Unlike other multicenter studies, we had access to disease‐specific risk‐adjustment methodologies, which may partially account for referral biases related to patient‐specific measures of acute or chronic illness severity.
Changes in the hospitalist effect over time have a number of explanations, some of which are relevant to our study. Recent evidence suggests that complex organizational characteristics, such as organizational values and goals, may contribute to performance on 30‐day mortality for AMI rather than specific processes and protocols27; intense focus on AMI as a quality improvement target is emblematic of a number of national initiatives that may have affected our results. Interestingly, hospitalist systems have changed over time as well. Early in the hospitalist movement, hospitalist systems were implemented largely at the behest of hospitals trying to reduce costs. In recent years, however, hospitalist systems are at least as frequently being implemented because outpatient‐based physicians or surgeons request hospitalists; hospitalists have been focused on care of uncoveredpatients, since the model's earliest description. In addition, some hospitals invest in hospitalist programs based on perceived ability of hospitalists to improve quality and achieve better patient outcomes in an era of payment increasingly being linked to quality of care metrics.
Our study has several limitations, six of which are noted here. First, while the hospitalist model has been widely embraced in the adult medicine field, in the absence of board certification, there is no gold standard definition of a hospitalist. It is therefore possible that some respondents may have represented groups that were identified incorrectly as hospitalists. Second, the data for the primary independent variable of interest was based upon self‐report and, therefore, subject to recall bias and potential misclassification of results. Respondents were not aware of our hypothesis, so the bias should not have been in one particular direction. Third, the data for the outcome variables are from 2008. They may, therefore, not reflect organizational enhancements related to use of hospitalists that are in process, and take years to yield downstream improvements on performance metrics. In addition, of the 429 hospitals that have hospitalist programs, 46 programs were initiated after 2008. While national performance on the 6 outcome variables has been relatively static over time,7 any significant change in hospital performance on these metrics since 2008 could suggest an overestimation or underestimation of the effect of hospitalist programs on patient outcomes. Fourth, we were not able to adjust for additional hospital or health system level characteristics that may be associated with hospitalist use or patient outcomes. Fifth, our regression models had significant collinearity, in that the presence of hospitalists was correlated with each of the covariates. However, this finding would indicate that our estimates may be overly conservative and could have contributed to our nonsignificant findings. Finally, outcomes for 2 of the 3 clinical conditions measured are ones for which hospitalists may less frequently provide care: acute myocardial infarction and heart failure. Outcome measures more relevant for hospitalists may be all‐condition, all‐cause, 30‐day mortality and readmission.
This work adds to the growing body of literature examining the impact of hospitalists on quality of care. To our knowledge, it is the first study to assess the association between hospitalist use and performance on outcome metrics at a national level. While our findings suggest that use of hospitalists alone may not lead to improved performance on outcome measures, a parallel body of research is emerging implicating broader system and organizational factors as key to high performance on outcome measures. It is likely that multiple factors contribute to performance on outcome measures, including type and mix of hospital personnel, patient care processes and workflow, and system level attributes. Comparative effectiveness and implementation research that assess the contextual factors and interventions that lead to successful system improvement and better performance is increasingly needed. It is unlikely that a single factor, such as hospitalist use, will significantly impact 30‐day mortality or readmission and, therefore, multifactorial interventions are likely required. In addition, hospitalist use is a complex intervention as the structure, processes, training, experience, role in the hospital system, and other factors (including quality of hospitalists or the hospitalist program) vary across programs. Rather than focusing on the volume of care delivered by hospitalists, hospitals will likely need to support hospital medicine programs that have the time and expertise to devote to improving the quality and value of care delivered across the hospital system. This study highlights that interventions leading to improvement on core outcome measures are more complex than simply having a hospital medicine program.
Acknowledgements
The authors acknowledge Judy Maselli, MPH, Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, for her assistance with statistical analyses and preparation of tables.
Disclosures: Work on this project was supported by the Robert Wood Johnson Clinical Scholars Program (K.G.); California Healthcare Foundation grant 15763 (A.D.A.); and a grant from the National Heart, Lung, and Blood Institute (NHLBI), study 1U01HL105270‐02 (H.M.K.). Dr Krumholz is the chair of the Cardiac Scientific Advisory Board for United Health and has a research grant with Medtronic through Yale University; Dr Auerbach has a grant through the National Heart, Lung, and Blood Institute (NHLBI). The authors have no other disclosures to report.
- ,,,.Comparison of hospitalists and nonhospitalists regarding core measures of pneumonia care.Am J Manag Care.2007;13:129–132.
- ,,,.Comparison of processes and outcomes of pneumonia care between hospitalists and community‐based primary care physicians.Mayo Clin Proc.2002;77(10):1053–1058.
- ,,,,.Quality of care for patients hospitalized with heart failure: assessing the impact of hospitalists.Arch Intern Med.2002;162(11):1251–1256.
- ,,, et al.Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):1399–1406.
- ,,.Comparison of practice patterns of hospitalists and community physicians in the care of patients with congestive heart failure.J Hosp Med.2008;3(1):35–41.
- ,,,,.Cross‐sectional analysis of hospitalist prevalence and quality of care in California.J Hosp Med.2010;5(4):200–207.
- Hospital Compare. Department of Health and Human Services. Available at: http://www.hospitalcompare.hhs.gov. Accessed September 3,2011.
- ,.Teaching hospitals and quality of care: a review of the literature.Milbank Q.2002;80(3):569–593.
- ,,, et al.A systematic review and meta‐analysis of studies comparing mortality rates of private for‐profit and private not‐for‐profit hospitals.Can Med Assoc J.2002;166(11):1399–1406.
- ,,,,.Patient and hospital characteristics associated with recommended processes of care for elderly patients hospitalized with pneumonia: results from the Medicare Quality Indicator System Pneumonia Module.Arch Intern Med.2002;162(7):827–833.
- ,,,.Care in U.S. hospitals—The Hospital Quality Alliance Program.N Engl J Med.2005;353(3):265–274.
- ,,, et al.Hospital characteristics and quality of care.JAMA.1992;268(13):1709–1714.
- ,,,,.Nurse‐staffing levels and the quality of care in hospitals.N Engl J Med.2002;346(22):1715–1722.
- ,,,.Health care market trends and the evolution of hospitalist use and roles.J Gen Intern Med.2005;20:101–107.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with an acute myocardial infarction.Circulation.2006;113:1683–1692.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance based on 30‐day all‐cause readmission rates among patients with acute myocardial infarction.Circulation.2011;4:243–252.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance on the basis of 30‐day all‐cause readmission rates among patients with heart failure.Circ Cardiovasc Qual Outcomes.2008;1:29–37.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with heart failure.Circulation.2006;113:1693–1701.
- ,,, et al.An administrative claims model for profiling hospital 30‐day mortality rates for pneumonia patients.PLoS ONE.2011;6(4):e17401.
- ,,, et al.Development, validation and results of a measure of 30‐day readmission following hospitalization for pneumonia.J Hosp Med.2011;6:142–150.
- ,.Association of hospitalist care with medical utilization after discharge: evidence of cost shift from a cohort study.Ann Intern Med.2011;155:152–159.
- ,,,.California hospital leaders' views of hospitalists: meeting needs of the present and future.J Hosp Med.2009;4:528–534.
- ,,, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866–874.
- ,,,,,.Implementation of a voluntary hospitalist service at a community teaching hospital: improved clinical efficiency and patients outcomes.Ann Intern Med.2002;137:859–865.
- ,,,.A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service.South Med J.2009;102:145–149.
- ,,,,,.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357:2589–2600.
- ,,, et al.What distinguishes top‐performing hospitals in acute myocardial infarction mortality rates?Ann Intern Med.2011;154:384–390.
- ,,,.Comparison of hospitalists and nonhospitalists regarding core measures of pneumonia care.Am J Manag Care.2007;13:129–132.
- ,,,.Comparison of processes and outcomes of pneumonia care between hospitalists and community‐based primary care physicians.Mayo Clin Proc.2002;77(10):1053–1058.
- ,,,,.Quality of care for patients hospitalized with heart failure: assessing the impact of hospitalists.Arch Intern Med.2002;162(11):1251–1256.
- ,,, et al.Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):1399–1406.
- ,,.Comparison of practice patterns of hospitalists and community physicians in the care of patients with congestive heart failure.J Hosp Med.2008;3(1):35–41.
- ,,,,.Cross‐sectional analysis of hospitalist prevalence and quality of care in California.J Hosp Med.2010;5(4):200–207.
- Hospital Compare. Department of Health and Human Services. Available at: http://www.hospitalcompare.hhs.gov. Accessed September 3,2011.
- ,.Teaching hospitals and quality of care: a review of the literature.Milbank Q.2002;80(3):569–593.
- ,,, et al.A systematic review and meta‐analysis of studies comparing mortality rates of private for‐profit and private not‐for‐profit hospitals.Can Med Assoc J.2002;166(11):1399–1406.
- ,,,,.Patient and hospital characteristics associated with recommended processes of care for elderly patients hospitalized with pneumonia: results from the Medicare Quality Indicator System Pneumonia Module.Arch Intern Med.2002;162(7):827–833.
- ,,,.Care in U.S. hospitals—The Hospital Quality Alliance Program.N Engl J Med.2005;353(3):265–274.
- ,,, et al.Hospital characteristics and quality of care.JAMA.1992;268(13):1709–1714.
- ,,,,.Nurse‐staffing levels and the quality of care in hospitals.N Engl J Med.2002;346(22):1715–1722.
- ,,,.Health care market trends and the evolution of hospitalist use and roles.J Gen Intern Med.2005;20:101–107.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with an acute myocardial infarction.Circulation.2006;113:1683–1692.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance based on 30‐day all‐cause readmission rates among patients with acute myocardial infarction.Circulation.2011;4:243–252.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance on the basis of 30‐day all‐cause readmission rates among patients with heart failure.Circ Cardiovasc Qual Outcomes.2008;1:29–37.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with heart failure.Circulation.2006;113:1693–1701.
- ,,, et al.An administrative claims model for profiling hospital 30‐day mortality rates for pneumonia patients.PLoS ONE.2011;6(4):e17401.
- ,,, et al.Development, validation and results of a measure of 30‐day readmission following hospitalization for pneumonia.J Hosp Med.2011;6:142–150.
- ,.Association of hospitalist care with medical utilization after discharge: evidence of cost shift from a cohort study.Ann Intern Med.2011;155:152–159.
- ,,,.California hospital leaders' views of hospitalists: meeting needs of the present and future.J Hosp Med.2009;4:528–534.
- ,,, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866–874.
- ,,,,,.Implementation of a voluntary hospitalist service at a community teaching hospital: improved clinical efficiency and patients outcomes.Ann Intern Med.2002;137:859–865.
- ,,,.A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service.South Med J.2009;102:145–149.
- ,,,,,.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357:2589–2600.
- ,,, et al.What distinguishes top‐performing hospitals in acute myocardial infarction mortality rates?Ann Intern Med.2011;154:384–390.
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