Policymakers and payers in the US are now focusing intensively on healthcare value, commonly defined as quality achieved per dollar spent, as a means of simultaneously improving quality of care and reducing or stabilizing costs. In 2017, 34% of payments across all payers were through value-based models, while only 41% were through traditional fee-for-service models. Inpatient care accounts for one third of all US health expenditures and has had the most long-standing value-based models. To date, however, value-based programs for inpatient care have had mixed or discouraging results. Without a clear understanding of what factors help hospitals and communities provide high value care, the value-based payment movement may not succeed. This renewal proposal extends work successfully done in the first funded R01, which explored hospital and community factors associated with readmission (one specific example of quality and cost). During that grant, which has already generated 14 publications in journals such as The New England Journal of Medicine and JAMA and 174 citations, we developed a robust data infrastructure that links 6.8 million hospitalizations to over 70 hospital and community factors. In this study, we will build upon that data infrastructure to explore practitioner, hospital and community factors associated with the overall value of inpatient healthcare.
In Aim 1, we will use the Centers for Medicare & Medicaid Services (CMS) Star Ratings measure developed by our team as our main quality outcome (which aggregates performance on 57 measures of mortality, readmission, safety, experience, effectiveness, and use of imaging), and the CMS Medicare Spending per Beneficiary measure as our main cost outcome.
In Aim 2 we will use other measures of quality, such as performance on mortality, or overall quality for specific conditions, and other measures of cost, such as the CMS condition-specific risk-standardized payment measures developed by our team. In both aims, we will explore the influence of provider, hospital and community factors on outcomes to determine the degree to which they contribute to healthcare value and mediate patient factors.
In Aims 1 -2 we will use measures already available in a number of datasets from CMS, the National Institutes of Health, the Census Bureau, the American Hospital Association, the City Health Dashboard, the County Health Rankings and others.
In Aim 3, we will identify new predictors by directly surveying high and low value hospitals about specific practices that are not available in existing datasets: for instance, aspects of quality infrastructure, Board and staff engagement, electronic health record capabilities, data infrastructure, community coordination and others. We will assess the association of those new predictors with the outcomes used in Aims 1 and 2. By the end of the grant period, we expect to have developed a nuanced understanding of healthcare value that will enable clinicians and policymakers to improve healthcare delivery for all patients.
This grant will explore provider, hospital and community factors affecting the value (quality and cost) of inpatient care. A better understanding of what drives high value care will help hospitals across the country to more rapidly transform to a value-based healthcare system, improving healthcare quality and cost for all Americans.
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|Horwitz, Leora I; Wang, Yongfei; Altaf, Faseeha K et al. (2018) Hospital Characteristics Associated With Postdischarge Hospital Readmission, Observation, and Emergency Department Utilization. Med Care 56:281-289|
|Angraal, Suveen; Khera, Rohan; Zhou, Shengfan et al. (2018) Trends in 30-Day Readmission Rates for Medicare and Non-Medicare Patients in the Era of the Affordable Care Act. Am J Med 131:1324-1331.e14|
|Blecker, Saul; Herrin, Jeph; Kwon, Ji Young et al. (2018) Effect of Hospital Readmission Reduction on Patients at Low, Medium, and High Risk of Readmission in the Medicare Population. J Hosp Med 13:537-543|
|Blecker, Saul; Kwon, Ji Young; Herrin, Jeph et al. (2018) Seasonal Variation in Readmission Risk for Patients Hospitalized with Cardiopulmonary Conditions. J Gen Intern Med 33:599-601|
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|Elysee, Gerald; Herrin, Jeph; Horwitz, Leora I (2017) An observational study of the relationship between meaningful use-based electronic health information exchange, interoperability, and medication reconciliation capabilities. Medicine (Baltimore) 96:e8274|
|Salerno, Amy M; Horwitz, Leora I; Kwon, Ji Young et al. (2017) Trends in readmission rates for safety net hospitals and non-safety net hospitals in the era of the US Hospital Readmission Reduction Program: a retrospective time series analysis using Medicare administrative claims data from 2008 to 2015. BMJ Open 7:e016149|
|Dharmarajan, Kumar; Wang, Yongfei; Lin, Zhenqiu et al. (2017) Association of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge. JAMA 318:270-278|
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