FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2116711783> ?p ?o ?g. }

• W2116711783 endingPage "352" @default.
• W2116711783 startingPage "350" @default.
• W2116711783 abstract "HomeCirculation: Cardiovascular Quality and OutcomesVol. 7, No. 3Measuring Quality and Enacting Policy Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBMeasuring Quality and Enacting PolicyReadmission Rates and Socioeconomic Factors Susannah M. Bernheim, MD, MHS Susannah M. BernheimSusannah M. Bernheim From the Yale-New Haven Hospital Center for Outcomes Research and Evaluation (CORE) and Yale University School of Medicine, Division of General Internal Medicine, New Haven, CT. Search for more papers by this author Originally published1 May 2014https://doi.org/10.1161/CIRCOUTCOMES.114.001037Circulation: Cardiovascular Quality and Outcomes. 2014;7:350–352Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: January 1, 2014: Previous Version 1 In this issue of Circulation Cardiovascular Quality and Outcomes, Blum et al1 ask how hospital quality profiling for the 48 hospitals in New York City would change if publicly reported heart failure readmission measures accounted for patients’ socioeconomic status. This article is timely—the debate about patients’ socioeconomic status and outcome quality measures is garnering national attention, including public statements by the American Hospital Association,2 the release of an expert report commissioned by the National Quality Forum,3 and proposed legislation on the same topic, which would modify the formula used to penalize hospitals for excess readmissions to account for the low-income patients served by the hospital.4Article see p 391Sparked by concerns about impact of pay-for-performance programs on safety-net providers5,6 and fueled by data suggesting that hospitals with higher disproportionate share hospital funding face payment penalties at a greater rate under the Hospital Readmission Reduction Program,7,8 increasingly stakeholders are calling for a change to the Center for Medicare and Medicaid Services (CMS) readmission measures to include risk adjustment for patients’ socioeconomic status. The article by Blum et al,1 however, demonstrates that for a diverse set of New York City hospitals such a change would have little impact on hospital profiling.Blum et al1 use a methodological approach that closely mirrors, but is not identical to, the methods used by CMS to calculate hospitals’ 30-day risk-standardized readmission risk for patients after heart failure hospitalizations. This is a strength of the article. As with CMS’s measures, the authors’ use hierarchical modeling to compare a hospital’s performance to what would be expected for an average hospital with a similar case mix. They then simulate the effect of adding socioeconomic status to the risk model. In their evaluation of hospital profiling, Blum et al1 determine whether the assessment of hospitals, as “no different than, worse than, or better than” the mean New York City readmission rate, changes if the same measure includes risk adjustment for patients’ socioeconomic status. They account for the uncertainty in measurement by estimating the confidence interval around each hospital’s risk-standardized readmission rate and categorize hospitals based on whether that interval crosses the New York average, similar to the use of the interval estimate in the national measures. Ultimately, the authors find that only 1 out of 48 hospitals would change category with the inclusion of socioeconomic status in the risk models.Critics will be quick to point out limitations of the socioeconomic status variable used by these authors. To account for patients’ socioeconomic status, the authors used a validated Agency for Healthcare Research and Quality index linked to patient zip code that incorporates 7 socioeconomic status variables into a single measure including area income, poverty, housing, and education levels. There is no perfect measure of patients’ socioeconomic status. Socioeconomic status is a multifaceted concept that includes education, wealth, income, and occupation, along with many other factors that have the potential to influence individuals’ clinical outcomes differently depending on context. There is a critical need to collect more reliable national data on socioeconomic factors and continued work to be done to fully explore the most important socioeconomic factors influencing patient outcomes. However, the metric used by the authors is arguably as good an index of socioeconomic status as could be feasibly incorporated into national measures in the near term. Given the dearth of nationally available individual socioeconomic variables, and the lack of a single accepted variable for assessing socioeconomic status, this study reasonably approximates a realistic change to current measures by using “CMS-like” measure methodology and a nationally available source of socioeconomic status.Although the generalizability of the article by Blum et al1 may be limited by its focus on only 48 hospitals within New York City, these hospitals have a broad range of socioeconomic case mix, which is reflected by the Agency for Healthcare Research and Quality socioeconomic status index. Moreover, the findings are supported by other reports showing that risk adjustment for dual-eligible status would not substantially change risk-standardized readmission rates nationally for heart failure, acute myocardial infarction, or pneumonia9 as well as a recent study showing that adjustment for patient-level socioeconomic status would have only a modest effect on the percentage of hospitals penalized under the Hospital Readmission Reduction Program (only 4% fewer disproportionate share hospitals would be affected by penalties for heart failure readmission).10 This article adds to growing evidence that patient-level risk adjustment for socioeconomic status may not have a meaningful impact on readmission measure results.The impetus for stakeholder calls to incorporate socioeconomic status into outcome measures, and readmission measures in particular is primarily concerns about the financial impact of pay-for-performance programs on providers caring for patients of low socioeconomic status. The findings of Blum et al1 do not undermine these concerns. The key to reconciling findings of Blum et al1 and legitimate concerns about the health of the safety net in the era of pay for performance is disentangling discussions about the quality measures themselves and the policies surrounding their use.Quality measures are meant to illuminate performance gaps and incentivize improvements in care. Outcome measures, in particular, are intended to provide broad targets that will catalyze innovation and transformations in care to improve outcomes that are meaningful to patients. Regardless of the results of this article, there are important conceptual and scientific reasons for being cautious about incorporating socioeconomic status into risk adjustment of outcome quality measures. Risk-standardized readmission measures are constructed to compare a hospital’s results to what would be expected based on an average hospital’s performance caring for a similar mix of patients. Risk adjustment thereby sets the standard by which a hospital is evaluated. By adding socioeconomic status to the readmission measures, the measures evaluate a hospital with reference to hospitals with a similar socioeconomic status mix—if low socioeconomic status patients have worse outcomes overall, then the expectation for hospitals with more low socioeconomic status patients will be to achieve worse outcome rates. Literally, we accept worse outcome rates as “expected” performance because a hospital cares for patients of low socioeconomic status, whereas we set higher standards for hospitals with fewer low socioeconomic status patients. CMS has, for obvious reasons, been reticent to set different standards for the outcomes of low socioeconomic status patients—particularly given evidence that disparities in outcomes for low socioeconomic status patients are at least partly the result of such patients’ exposure to worse quality of care.11Quality measures should not be constructed to treat disparities in patient outcomes as inevitable. Evidence shows that hospitals caring for low socioeconomic status patients can successfully diminish quality gaps through participation in incentive programs12 and safety-net providers can achieve similar outcomes to non–safety-net providers on readmission and mortality rates.13 Risk adjustment for patient socioeconomic status, by setting worse outcomes as the expectation, risks diminishing the incentives to improve care for vulnerable patients of low socioeconomic status by enshrining and accepting current outcomes disparities.On the other hand, within pay-for-performance programs, hospitals that care for low socioeconomic status patients may be at a distinct disadvantage. Safety-net hospitals often have lower operating margins and may be more vulnerable to payment penalties; this is what led to the establishment of disproportionate share hospital payments initially. Moreover, above and beyond the risk to financial margins and operations, it may take a greater investment of resources by hospitals and partner community organizations to achieve best outcomes for low socioeconomic status patients compared with other groups, and this is an investment that is not always funded. If we had empirical evidence that a pill helped low socioeconomic status patients avoid readmission, we might cover the cost for it, but in the case of readmission, that pill might actually be resource-intensive services such as social workers, extra time with translators, follow-up phone calls, or bus rides to the next appointment.Rather than adjusting outcome measures for socioeconomic status, several other possible approaches might better support the safety net to improve patient outcomes. These include incentives based on improvement rather than relative performance, slower phasing in of penalties for providers of low socioeconomic status patients to allow for investment in quality, or peer-group comparisons for the application of payment penalties as proposed by Medicare Payment Advisory Commission.14 Additionally, programs that directly fund quality efforts, such as Medicare’s Partnership for Patients, can be targeted to safety-net providers.Risk adjustment for socioeconomic status seems to have little effect on provider results on these outcome measures, and yet, it risks entrenching disparities in outcomes and decreasing incentives to improve care for vulnerable populations. We need policies and programs that hold high standards for improving outcomes and maintain a focus on improving disparities but recognize that safety-net providers may need greater resources to succeed.AcknowledgmentsDr Bernheim thanks Dr Joseph Ross, Dr Lisa Suter, and Dr Jeptha Curtis for comments on an early draft.DisclosuresDr Bernheim works under contract with the Centers for Medicare and Medicaid Services to develop and evaluate quality measures.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association, nor are they necessarily those of the Centers for Medicare and Medicaid Services.Correspondence to Susannah M. Bernheim, MD, MHS, Yale-New Haven Hospital Center for Outcomes Research and Evaluation (CORE) and Yale University School of Medicine, Division of General Internal Medicine, New Haven, CT 0652. E-mail [email protected]References1. Blum AB, Egorova NN, Sosunov EA, Gelijns AC, DuPree E, Moskowitz AJ, Federman AD, Ascheim DD, Keyhani S. Impact of socioeconomic status measures on hospital profiling in New York City.Circ Cardiovasc Qual Outcomes. 2014; 7:390–396.LinkGoogle Scholar2. American Hospital Association. AHA Comments on CMS’ Hospital IPPS Proposed Rule for FY 2014. http://www.aha.org/advocacy-issues/letter/2013/130620-cl-cms-1599p.pdf. Accessed June 20, 2013.Google Scholar3. National Quality Forum. Draft Report: Risk Adjustment for Socioeconomic Status or Other Sociodemographic Factors. http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=75398. Accessed March 18, 2014.Google Scholar4. Eaton S. Hospital readmission fines should reflect patient demographics, says bill by Rep. Jim Renacci.2014. Available at: http://www.Cleveland.com. Accessed May 10, 2014.Google Scholar5. Bhalla R, Kalkut G. Could Medicare readmission policy exacerbate health care system inequity?Ann Intern Med. 2010; 152:114–117.CrossrefMedlineGoogle Scholar6. Casalino LP, Elster A, Eisenberg A, Lewis E, Montgomery J, Ramos D. Will pay-for-performance and quality reporting affect health care disparities?Health Aff (Millwood). 2007; 26:w405–w414.CrossrefMedlineGoogle Scholar7. Joynt KE, Jha AK. Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program.JAMA. 2013; 309:342–343.CrossrefMedlineGoogle Scholar8. Berenson J, Shih AHigher Readmissions at Safety-Net Hospitals and Potential Policy Solutions. http://www.commonwealthfund.org/~/media/Files/Publications/Issue%20Brief/2012/Dec/1647_Berenson_higher_readmissions_at_safety_net_hosps_ib.pdf. Accessed December 10, 2012.Google Scholar9. Centers for Medicare & Medicaid Services. Medicare Hospital Quality Chartbook: Performance Report on Outcome Measures. Centers for Medicare & Medicaid Services; 2012.Google Scholar10. Gu Q, Koenig L, Faerberg J, Steinberg CR, Vaz C, Wheatley MP. The Medicare Hospital Readmissions Reduction Program: potential unintended consequences for hospitals serving vulnerable populations.Health Services Res. 2014.CrossrefMedlineGoogle Scholar11. Jha AK, Orav EJ, Epstein AM. Low-quality, high-cost hospitals, mainly in South, care for sharply higher shares of elderly black, Hispanic, and medicaid patients.Health Aff (Millwood). 2011; 30:1904–1911.CrossrefMedlineGoogle Scholar12. Jha AK, Orav EJ, Epstein AM. The effect of financial incentives on hospitals that serve poor patients.Ann Intern Med. 2010; 153:299–306.CrossrefMedlineGoogle Scholar13. Ross JS, Bernheim SM, Lin Z, Drye EE, Chen J, Normand SL, Krumholz HM. Based on key measures, care quality for Medicare enrollees at safety-net and non-safety-net hospitals was almost equal.Health Aff (Millwood). 2012; 31:1739–1748.CrossrefMedlineGoogle Scholar14. Medicare Payment Advisory Commission. Report to the Congress: Medicare and the Health Care Delivery System. Medicare Payment Advisory Commission; 2013.Google Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Buckman M, Grant A, Henson S, Ribeiro J, Roth K, Stranton D, Korvink M and Gunn L (2022) A review of socioeconomic factors associated with acute myocardial infarction-related mortality and hospital readmissions, Hospital Practice, 10.1080/21548331.2021.2022357, 50:1, (1-8), Online publication date: 1-Jan-2022. Talutis S, Chen Q, Wang N and Rosen A (2019) Comparison of Risk-Standardized Readmission Rates of Surgical Patients at Safety-Net and Non–Safety-Net Hospitals Using Agency for Healthcare Research and Quality and American Hospital Association Data, JAMA Surgery, 10.1001/jamasurg.2018.5242, 154:5, (391), Online publication date: 1-May-2019. Hu J, Schreiber M, Jordan J, George D and Nerenz D (2017) Associations Between Community Sociodemographics and Performance in HEDIS Quality Measures: A Study of 22 Medical Centers in a Primary Care Network, American Journal of Medical Quality, 10.1177/1062860617695456, 33:1, (5-13), Online publication date: 1-Jan-2018. Tabak Y, Sun X, Nunez C, Gupta V and Johannes R (2017) Predicting Readmission at Early Hospitalization Using Electronic Clinical Data, Medical Care, 10.1097/MLR.0000000000000654, 55:3, (267-275), Online publication date: 1-Mar-2017. Zdradzinski M, Phelan M and Mace S (2016) Impact of Frailty and Sociodemographic Factors on Hospital Admission From an Emergency Department Observation Unit, American Journal of Medical Quality, 10.1177/1062860616644779, 32:3, (299-306), Online publication date: 1-May-2017. Dharmarajan K and Krumholz H (2014) Strategies to Reduce 30-Day Readmissions in Older Patients Hospitalized with Heart Failure and Acute Myocardial Infarction, Current Geriatrics Reports, 10.1007/s13670-014-0103-8, 3:4, (306-315), Online publication date: 1-Dec-2014. Liao J and Chaiyachati K (2015) Beyond Adjustments for Socioeconomic Status in Hospital Readmissions Penalties, HPHR Journal, 10.54111/0001/h1:8 Daley S, Kajendrakumar B, Nandhakumar S, Personett C, Sholes M, Thapa S, Xue C, Korvink M and Gunn L (2021) County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S., Healthcare, 10.3390/healthcare9111424, 9:11, (1424) May 2014Vol 7, Issue 3 Advertisement Article InformationMetrics © 2014 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.114.001037PMID: 24823951 Originally publishedMay 1, 2014 KeywordsEditorialssocial classsocioeconomic statushealthcare quality, access and evaluationoutcomesquality measurespatient readmissionPDF download Advertisement SubjectsEthics and Policy" @default.
• W2116711783 created "2016-06-24" @default.
• W2116711783 creator A5079304800 @default.
• W2116711783 date "2014-05-01" @default.
• W2116711783 modified "2023-09-30" @default.
• W2116711783 title "Measuring Quality and Enacting Policy" @default.
• W2116711783 cites W2022388532 @default.
• W2116711783 cites W2067493161 @default.
• W2116711783 cites W2092861621 @default.
• W2116711783 cites W2107264379 @default.
• W2116711783 cites W2126480830 @default.
• W2116711783 cites W2137259690 @default.
• W2116711783 cites W2140391754 @default.
• W2116711783 cites W2142576266 @default.
• W2116711783 doi "https://doi.org/10.1161/circoutcomes.114.001037" @default.
• W2116711783 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24823951" @default.
• W2116711783 hasPublicationYear "2014" @default.
• W2116711783 type Work @default.
• W2116711783 sameAs 2116711783 @default.
• W2116711783 citedByCount "9" @default.
• W2116711783 countsByYear W21167117832014 @default.
• W2116711783 countsByYear W21167117832015 @default.
• W2116711783 countsByYear W21167117832016 @default.
• W2116711783 countsByYear W21167117832017 @default.
• W2116711783 countsByYear W21167117832019 @default.
• W2116711783 countsByYear W21167117832021 @default.
• W2116711783 countsByYear W21167117832022 @default.
• W2116711783 crossrefType "journal-article" @default.
• W2116711783 hasAuthorship W2116711783A5079304800 @default.
• W2116711783 hasBestOaLocation W21167117831 @default.
• W2116711783 hasConcept C111472728 @default.
• W2116711783 hasConcept C138885662 @default.
• W2116711783 hasConcept C17744445 @default.
• W2116711783 hasConcept C2779530757 @default.
• W2116711783 hasConceptScore W2116711783C111472728 @default.
• W2116711783 hasConceptScore W2116711783C138885662 @default.
• W2116711783 hasConceptScore W2116711783C17744445 @default.
• W2116711783 hasConceptScore W2116711783C2779530757 @default.
• W2116711783 hasIssue "3" @default.
• W2116711783 hasLocation W21167117831 @default.
• W2116711783 hasLocation W21167117832 @default.
• W2116711783 hasOpenAccess W2116711783 @default.
• W2116711783 hasPrimaryLocation W21167117831 @default.
• W2116711783 hasRelatedWork W2362665430 @default.
• W2116711783 hasRelatedWork W2373216116 @default.
• W2116711783 hasRelatedWork W2384667405 @default.
• W2116711783 hasRelatedWork W2562923059 @default.
• W2116711783 hasRelatedWork W2748952813 @default.
• W2116711783 hasRelatedWork W2899084033 @default.
• W2116711783 hasRelatedWork W2949263084 @default.
• W2116711783 hasRelatedWork W2955725829 @default.
• W2116711783 hasRelatedWork W3123396266 @default.
• W2116711783 hasRelatedWork W4323667137 @default.
• W2116711783 hasVolume "7" @default.
• W2116711783 isParatext "false" @default.
• W2116711783 isRetracted "false" @default.
• W2116711783 magId "2116711783" @default.
• W2116711783 workType "article" @default.