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Hospital Mortality Rates: How Is Palliative Care Taken Into Account? J 914 Journal of Pain and Symptom Management Vol. 40 No. 6 December 2010 Special Article Hospital Mortality Rates: How Is Palliative Care Taken into Account? J. Brian Cassel, PhD, Amber B. Jones, MEd, Diane E. Meier, MD, Thomas J. Smith, MD, Lynn Hill Spragens, MBA, and David Weissman, MD Virginia Commonwealth University (J.B.C., T.J.S.), Richmond, Virginia; and Center to Advance Palliative Care (A.B.J., D.E.M., L.H.S., D.W.), New York, New York, USA Abstract Context. Using mortality rates to measure hospital quality presumes that hospital deaths are medical failures. To be a fair measure of hospital quality, hospital mortality measures must take patient-level factors, such as goals of care, into account. Objectives. To answer questions about how hospital mortality rates are computed and how the involvement of hospice or palliative care (PC) are recognized and handled. Methods. We analyzed the methods of four entities: Centers for Medicare & Medicaid Services ‘‘Hospital Compare;’’ U.S. News & World Report ‘‘Best Hospitals;’’ Thomson-Reuters ‘‘100 TopHospitals;’’ and HealthGrades. Results. All entities reviewed rely on Medicare data, compute risk-adjusted mortality rates, and use ‘‘all-cause’’ mortality. They vary considerably in their recognition and handling of cases that involved hospice care or PC. One entity excludes cases with prior hospice care and another excludes those discharged to hospice at the end of the index hospitalization. Two entities exclude some or all cases that were coded with the V66.7 ‘‘Palliative Care Encounter’’ International Classification of Disease, Ninth Revision, Clinical Modification diagnosis code. Conclusion. Proliferation of, and variability among, hospital mortality measures creates a challenge for hospital administrators. PC and hospice leaders need to educate themselves and their hospital administrators about the extent to which these mortality rates take end-of-life care into account. At the national level, PC and hospice leaders should take advantage of opportunities to engage these mortality raters in conversation about possible changes in their methods and to conduct further research on this topic. J Pain Symptom Manage 2010;40:914e925. Ó 2010 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Mortality, palliative care, hospice, hospital care, quality of care, measurement, coding Introduction Address correspondence to: J. Brian Cassel, PhD, Virginia Commonwealth University Massey Cancer Center, Commercial and government payers and the 401 College Street, P.O. Box 980037, Richmond, VA general public are demanding information 23298-0037, USA. E-mail: [email protected] about health care quality and costs. Hospital Accepted for publication: July 8, 2010. mortality rates are viewed as a key indicator Ó 2010 U.S. Cancer Pain Relief Committee 0885-3924/$ - see front matter Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jpainsymman.2010.07.005 Vol. 40 No. 6 December 2010 Hospital Mortality Rates and Palliative Care 915 of quality, and several sources now provide the at best and unethical at worst. As one recent re- ability for the general public to look at risk- port phrased it, ‘‘Holding facilities accountable adjusted mortality statistics and other measures for patient mortality rates is not just unless rele- of quality and outcomes for a given hospital.1,2 vant factors associated with the patient’s care Although not exhaustive, a list of prominent en- are taken into account.’’11(p. 25) That study found tities measuring and disseminating hospital that do-not-resuscitate (DNR) status and pallia- mortality rates includes government agencies, tive care (PC) involvement contributed signifi- such as the Centers for Medicare & Medicaid cantly to the explanation of hospital mortality, Services (CMS); commercial entities, such as above and beyond the standard risk-adjustment U.S. News & World Report (a general interest model: ‘‘These results indicate that DNR and magazine), HealthGrades (a health care ratings PC designations can identify mortality risk at organization), and Thomson-Reuters (a health the margin, controlling for other observed risk care analytics firm); and health care consortia, factors already in the standard model ..For such as Premier, Inc., the Leapfrog Group, example the PC designation identifies patients and University HealthSystem Consortium whose risk of dying is between 9% and 57% (UHC). Hospital mortality is also a metric greater than predicted by the model..’’11(p. 31) used by CMS/The Joint Commission in their The underlying premise of the hospice care ‘‘core measures’’ and, thus, may be incorpo- and PC field is that death, except in catastrophic rated into hospital reimbursement incentive events, is a natural life cycle event; it may be de- e programs.3 6 layed, but it can never be entirely prevented.7,8 The presupposition of using hospital mortal- It is also a fact that many Americans turn to ity rates as measures of hospital quality is that hospitals for their end-of-life care. In this hospital deaths represent medical failure; the context, and given the findings reported re- more risk-adjusted deaths a given hospital cently by Kroch et al.,11 the primary goal of has, the lower its quality of care. Implicit in our study was to determine the extent to this ‘‘death ¼ poor quality’’ equation is the mes- which publicly reported hospital mortality sage that hospitals with more risk-adjusted rates take PC or hospice involvement into ac- deaths than other hospitals could have and count, describe how they do so, and attempt should have prevented the marginal difference to understand the reasoning behind their in mortality. These assumptions have been eval- decisions. e uated in several recent articles.7 9 Secondary to that, we sought to consider the A methodological assumption in hospital implications of these mortality rate analyses for mortality rates is that patient- and hospital- our field, specifically for hospital-based PC level attributes that are not indicative of quality teams and their hospice partners. We know but are correlated with mortality (e.g., age, first hand from working with numerous PC acuity of illness when entering the hospital, programs that there is widespread misunder- or disproportionate share of critically ill pa- standing among hospital administrators about tients) are adjusted for or factored out of the the meaning of hospital mortality scores and equation. Whatever hospital-level variability re- the possible or putative impact of hospice mains unexplained by those factors is assumed care and PC on those scores.12 to be a valid and trustworthy measure of hospi- tal quality of care. This is the essence of risk adjustment and how it is used in equating mor- tality with hospital quality of care. Methods Others have examined whether these ap- We examined well-known national sources of proaches do an adequate job of factoring out hospital quality or performance data that in- hospital-level characteristics, such as tertiary clude mortality scores (based on hospital claims referral centers receiving large numbers of criti- data) as a critical part of their evaluations. We cally ill patients from other hospitals.10 In this identified more than 15 different entities that article, we focus on patient-level characteristics. calculate hospital mortality rates. We excluded If patient-level characteristics are not adequately benchmarking entities whose data are not gen- factored out, then attributing the remaining var- erally made available to the public (e.g., Premier iability to hospital quality of care is questionable Inc., UHC, and Thomson-Reuters Healthcare). 916 Cassel et al. Vol. 40 No. 6 December 2010 We excluded the Leapfrog Group, which limits The relative weight given to the mortality its mortality scores to several high-risk surgeries, rate, if a composite score for the hospital such as esophageal resection, pancreatic resec- is created. tion, and heart bypass surgery that rarely involve A brief description of risk adjustment is war- hospice care or PC. We excluded state-based ranted. Patient-level risk adjustment controls entities (e.g., Virginia Health Information). for how sick those patients were and other pa- We excluded entities that repackage existing tient characteristics, such as sex and age (to CMS ‘‘Hospital Compare’’ mortality data (e.g., our knowledge, none adjust for socioeconomic Commonwealth Fund). Four entities met our status at the patient level). Hospital-level risk- criteria for this review: CMS ‘‘Hospital Com- adjustment controls for whether the sickest pa- pare,’’ U.S. News & World Report ‘‘Best Hospi- tients tend to be seen in greater proportion at tals,’’ Thomson-Reuters ‘‘100 Top Hospitals’’ some hospitals rather than others. Determin- (a distinct entity from the Thomson-Reuters ing how sick the patients were is based on Healthcare benchmarking service), and the International Classification of Diseases, HealthGrades (see Table 1 for brief descriptions Ninth Revision, Clinical Modification (ICD- of these entities). 9-CM) diagnosis codes entered in the adminis- We accessed the methodological descrip- e trative or claims data for that hospitalization by tions from publicly available Web sites13 16 that hospital. for each of these four entities to ascertain How exactly these entities perform their risk the general approach to mortality rate analy- adjustment is a critical issue, but it is also a very ses. We coded the following key elements of complex one. The focus of this article
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