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Invited Commentary
Health Policy
October 19, 2018

Measuring and Improving the Value of Hospital Care

Author Affiliations
  • 1Department of Veterans Affairs’ Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania
  • 2Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
  • 3Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
  • 4Cardiovascular Outcomes, Quality, and Evaluative Research Center, University of Pennsylvania, Philadelphia
JAMA Netw Open. 2018;1(6):e183517. doi:10.1001/jamanetworkopen.2018.3517

While numerous comparisons of hospital quality performance and acute care outcomes are publicly available and used routinely by public and private payers,1 there is an increasing imperative to find reliable measures of the value of care that hospitals provide. In this context, the word value can be defined as the efficiency by which hospitals transform the dollars received as payments into optimal health outcomes for patients. Value is a core concept in multiple sectors of the US economy, and various empirical methods were developed to assess the efficiency of firms’ production of goods and services.2 Conversely in health care, while numerous studies have examined the value (ie, cost-effectiveness) of specific tests and treatments,3 there has been a paucity of studies measuring and comparing the value generated by health care firms (eg, hospitals). Desai et al4 make a promising start to addressing the problem of hospital value by analyzing hospitals’ Medicare payments and outcomes using sophisticated risk adjustment techniques.4 Their findings highlight both the challenges of assessing the value of hospital care, as well as the opportunities in unambiguously identifying which US hospitals are the most and least efficient in producing good health outcomes.

Measuring the value of a hospital’s care requires both a standardized measure of each hospital’s average health outcome, as well as a standardized measure of average cost, for comparable episodes of care across hospitals. Desai et al4 used the risk-standardized mortality rate, a well-validated statistical measure that is currently used by the Centers for Medicare & Medicaid Service’s Hospital Compare Program,5 to assess hospital outcomes for 3 common acute illnesses that require hospitalization: acute myocardial infarction, heart failure, and pneumonia. The authors used a similar statistic, the risk-standardized payment, to assess the amount paid to each hospital for an average case of each of these conditions. The juxtaposition of these 2 statistics provides insight into the value of each hospital’s care.

The authors’ findings raise several important questions. The first is why the lack of a strong-positive relationship between higher cost and better outcomes, which would be expected if most hospitals were delivering efficient care. While the lack of strong correlation might be caused in part by patients who were the sickest of the sick (and who also tend to be the costliest of the costly) being cared for disproportionately by a subset of hospitals, the authors did not find clear evidence indicating that large academic centers, which are more likely to receive such patients, had systematically higher costs and poorer outcomes. It therefore seems plausible that widespread inefficiency (eg, overuse of unnecessary services) was the primary culprit causing the lack of a strong cost to outcome correlation.

A second question is whether the many hospitals that had either higher than average mortality rates, higher than average costs, or both, could be incentivized to become more efficient producers of health outcomes without either undergoing impossible structural changes (eg, moving away from a high-cost location) or causing unforeseen adverse social consequences (eg, systematically avoiding patients potentially likely to have high costs and/or worse outcomes). Future research in hospital value should more fully elucidate and distinguish which factors influencing hospital value are modifiable and nonmodifiable, with an emphasis on identifying modifiable factors that would be potential targets for policymaking.

An additional critical issue to further explore is the relationship of socioeconomics, social support, neighborhood characteristics, and municipal infrastructure to a hospital’s cost of care. Particularly for episodes of care triggered by a hospitalization for severe illness, patients lacking sufficient personal, family, and/or community resources frequently require additional paid services provided by their hospitals in the postacute care period to avoid adverse outcomes such as clinical complications, hospital readmission, and death.6 Conversely, wealthier patients with more extensive resources might be expected, on average, to require fewer hospital provided services after hospital discharge. While the relationship between personal and community resources and hospital spending is undoubtedly complex (eg, some wealthier patients might demand more postacute hospital services than less wealthy patients), it is essential that hospital value calculations accurately account for this relationship. In the absence of such accounting there is a sizeable risk that value measurements would unfairly penalize hospitals for spending more on the patients who unavoidably require greater than average spending to achieve good clinical outcomes.

From a policy perspective, improving health care value has been a devilishly challenging concept to incentivize and implement. While almost all consumers are conceptually familiar with paying less for lower-quality goods and services (eg, a $100 stay in a 3-star hotel vs a $400 stay in a 5-star hotel), few patients would seek hospitalization at a lower-quality hospital solely because of its very low costs. Hence, rather than the actual consumers of hospital care, the public and private payers for hospital care have been the primary drivers of changes in the hospital marketplace so that value is incentivized.

Multiple policy initiatives designed to encourage this transformation are currently being implemented. For example, the narrowing of hospital referral networks by private payers seeks to incentivize patients to choose hospitals that deliver reasonable quality care without excessive costs.7 Unfortunately, the current evidence guiding the optimal design of these networks is limited, and payers’ designation of certain hospitals as preferred centers based on proprietary algorithms combining various measures of quality and costs often lacks both transparency and methodological rigor.8 A second policy initiative designed to improve hospital value are bundled payment programs, by which hospitals receive a fixed payment for an acute episode of care and are subsequently responsible for all inpatient and outpatient medical expenses during the episode.9 Unfortunately, when bundled payments lack rigorous adjustment for the wide array of health and socioeconomic differences across patients that influence hospital costs, they potentially incentivize the cherry picking of low-risk, low-cost patients and the systematic exclusion of sicker and/or poorer patients from hospital care.10

The shortcomings of current value enhancement policies such as narrow networks and bundled payments underscore the importance of a more detailed understanding of the complex mechanisms producing high-value hospital care. Only with better insight will future policies be more optimally designed and effective in improving hospital value. Ultimately, improving the value of hospital care is a critical societal goal, vitally important to achieving a sustainable growth rate in health care costs while simultaneously ensuring excellent health outcomes for hospitalized patients.

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Article Information

Published: October 19, 2018. doi:10.1001/jamanetworkopen.2018.3517

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2018 Groeneveld PW. JAMA Network Open.

Corresponding Author: Peter W. Groeneveld, MD, MS, Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 423 Service Dr, 1204 Blockley Hall, Philadelphia, PA 19104 (petergro@upenn.edu).

Conflict of Interest Disclosures: None reported.

Disclaimer: The opinions expressed in this commentary are the author’s own and do not represent the official views of the US Department of Veterans Affairs.

References
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Desai  NR, Ross  JS, Kwon  JY,  et al.  Association between hospital penalty status under the Hospital Readmission Reduction Program and readmission rates for target and nontarget conditions.  JAMA. 2016;316(24):2647-2656. doi:10.1001/jama.2016.18533PubMedGoogle ScholarCrossref
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Aigner  D, Lovell  CAK, Schmidt  P.  Formulation and estimation of stochastic frontier production models.  J Econom. 1997;6:21-37. doi:10.1016/0304-4076(77)90052-5Google ScholarCrossref
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Neumann  PJ, Thorat  T, Shi  J, Saret  CJ, Cohen  JT.  The changing face of the cost-utility literature, 1990-2012.  Value Health. 2015;18(2):271-277. doi:10.1016/j.jval.2014.12.002PubMedGoogle ScholarCrossref
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Desai  NR, Ott  LS, George  EJ,  et al.  Variation in and hospital characteristics associated with the value of care for Medicare beneficiaries with acute myocardial infarction, heart failure, and pneumonia.  JAMA Netw Open. 2018;1(6): e183519. doi:10.1001/jamanetworkopen.2018.3519Google Scholar
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Kangovi  S, Barg  FK, Carter  T,  et al.  Challenges faced by patients with low socioeconomic status during the post-hospital transition.  J Gen Intern Med. 2014;29(2):283-289. doi:10.1007/s11606-013-2571-5PubMedGoogle ScholarCrossref
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Polsky  D, Cidav  Z, Swanson  A.  Marketplace plans with narrow physician networks feature lower monthly premiums than plans with larger networks.  Health Aff (Millwood). 2016;35(10):1842-1848. doi:10.1377/hlthaff.2016.0693PubMedGoogle ScholarCrossref
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Austin  JM, Jha  AK, Romano  PS,  et al.  National hospital ratings systems share few common scores and may generate confusion instead of clarity.  Health Aff (Millwood). 2015;34(3):423-430. doi:10.1377/hlthaff.2014.0201PubMedGoogle ScholarCrossref
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Dummit  LA, Kahvecioglu  D, Marrufo  G,  et al.  Association between hospital participation in a Medicare Bundled Payment Initiative and payments and quality outcomes for lower extremity joint replacement episodes.  JAMA. 2016;316(12):1267-1278. doi:10.1001/jama.2016.12717PubMedGoogle ScholarCrossref
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Dranove  D, Kessler  D, McClellan  M, Satterthwaite  M.  Is more information better? the effects of “report cards” on health care providers.  J Polit Econ. 2003;111(3):555-588. doi:10.1086/374180Google ScholarCrossref
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