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. 2018 Nov 1;178(11):1498-1507.
doi: 10.1001/jamainternmed.2018.4481.

Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates: Implications for Pay for Performance

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Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates: Implications for Pay for Performance

Eric T Roberts et al. JAMA Intern Med. .
Free PMC article

Abstract

Importance: In several pay-for-performance programs, Medicare ties payments to readmission rates but accounts only for a limited set of patient characteristics-and no measures of social risk-when assessing performance of health care providers (clinicians, practices, hospitals, or other organizations). Debate continues over whether accounting for social risk would mitigate inappropriate penalties or would establish lower standards of care for disadvantaged patients if they are served by lower-quality providers.

Objectives: To assess changes in hospital performance on readmission rates after adjusting for additional clinical and social patient characteristics by using methods that distinguish the association between patient characteristics and readmission from between-hospital differences in quality.

Design, setting, and participants: Using Medicare claims for admissions in 2013 through 2014 and linked US Census data, we assessed several clinical and social characteristics of patients that are not currently used for risk adjustment in the Hospital Readmission Reduction Program. We compared hospital readmission rates with and without adjustment for these additional characteristics, using only the average within-hospital associations between patient characteristics and readmission as the basis for adjustment, thereby appropriately excluding hospitals' distinct contributions to readmission from the adjustment.

Main outcomes and measures: All-cause readmission within 30 days of discharge.

Results: The study sample consisted of 1 169 014 index admissions among 1 003 664 unique Medicare beneficiaries (41.5% men; mean [SD] age, 79.9 [8.3] years) in 2215 hospitals. Compared with adjustment for patient characteristics currently implemented by Medicare, adjustment for the additional characteristics reduced overall variation in hospital readmission rates by 9.6%, changed rates upward or downward by 0.37 to 0.72 percentage points for the 10% of hospitals most affected by the additional adjustments (±30.3% to ±58.9% of the hospital-level standard deviation), and would be expected to reduce penalties (in relative terms) by 52%, 46%, and 41% for hospitals with the largest 1%, 5%, and 10% of penalty reductions, respectively. The additional adjustments reduced the mean difference in readmission rates between hospitals in the top and bottom quintiles of high-risk patients by 0.53 percentage points (95% CI, 0.50-0.55; P < .001), or 54% of the difference estimated with CMS adjustments alone. Both clinical and social characteristics contributed to these reductions, and these reductions were considerably greater for conditions targeted by the Hospital Readmission Reduction Program. Adjustment for social characteristics resulted in greater changes in rates of readmission or death than in rates of readmission alone.

Conclusions and relevance: Hospitals serving higher-risk patients may be penalized substantially because of the patients they serve rather than their quality of care. Adjusting solely for within-hospital associations may allow adjustment for additional patient characteristics to mitigate unintended consequences of pay for performance without holding hospitals to different standards because of the patients they serve.

Conflict of interest statement

Conflict of Interest Disclosures: None reported.

Figures

Figure 1.
Figure 1.. Expected Changes in Readmission Rates After Adjusting for Additional Clinical and Social Characteristics of Patients
This graph shows the distribution of expected performance changes among hospitals after adjusting for the additional clinical and social risk factors found in the Box. Hospitals are grouped on the y axis by decile of readmission performance adjusted for age, sex, and recent comorbidities (ie, standard Centers for Medicare & Medicaid Services [CMS] adjustments). On the x axis, changes are reported in percentage points of the readmission rate. The plotted distribution reflects 5000 draws from the empirical covariances of hospitals’ unadjusted readmission rates with readmission rates predicted from the base CMS variables, as well as the additional clinical and social characteristics listed in the Box. After additional adjustments in our analysis, approximately 6.0% of hospitals originally above the median would move below the median (plotted in dark blue), while a similar proportion of hospitals below the median would be expected to move above the median (plotted in orange). See eFigure 2 in the Supplement, which plots the distribution of expected changes in the rate of 30-day readmission or mortality following additional adjustments.
Figure 2.
Figure 2.. Differences in Outcomes Between Hospitals Serving Higher-Risk Medicare Beneficiaries and Those Serving Lower-Risk Patients, Before and After Adjustment for Additional Patient Characteristics
These graphs display average risk-adjusted outcomes by quintile of hospitals before and after adjusting for clinical and social covariates found in the Box, in addition to base variables used by the Centers for Medicare & Medicaid Services (CMS) to adjust for risk of readmissions. The graphs display mean adjusted rates among all patients admitted to hospitals in each quintile. A and C, Hospitals are grouped into quintiles by patients’ clinical and social risk according to the additional characteristics examined. B and D, Hospitals are grouped into quintiles by the proportion of Medicare patients dually enrolled in Medicaid. The clinical and social risk of Medicare patients was assessed by constructing patient-level risk scores predicted by the average within-hospital associations between each outcome and the added clinical and social variables from the Box, controlling for base CMS variables and hospital fixed effects. Random effects models were used to estimate hospital-level averages of these risk scores or rates of dual enrollment in Medicaid, and the resulting estimates were used to group hospitals into quintiles (see eAppendix 3 in the Supplement for details).

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