Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Hospital Readmission Rates Following Hip and Knee Arthroplasty

Martsolf, Grant R.; Barrett, Marguerite L; Weiss, Audrey J; Kandrack, Ryan; Washington, Raynard; Steiner, Claudia; Mehrotra, Ateev; SooHoo, Nelson F.; Coffey, Rosanna M.

doi:10.2106/jbjs.15.00884

Cited by 66 publications

(41 citation statements)

References 13 publications

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“…Prior studies of the HRRP that focused on overall changes in hospital performance rankings or penalty status may have missed salient financial consequences for an important minority of hospitals serving the highest-risk patients. [43][44][45] In programs like the HRRP, in which penalties increase proportionally for hospitals with higher readmission rates, risk adjustment can substantially reduce the penalties incurred by hospitals with high shares of high-risk patients without major changes in rankings or the proportion of hospitals receiving a penalty.…”

Section: Discussionmentioning

confidence: 99%

Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates

et al. 2018

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IMPORTANCEIn 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. RESULTSThe 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 adjustme...

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Section: Discussionmentioning

confidence: 99%

Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates

et al. 2018

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“…By exactly matching patients on each matching criterion, this technique was able to control for many potential confounding variables normally present in the retrospective cohort analysis. The matching criteria employed in the present study were selected based on prior work correlating these factors with patient outcomes 29‐31 . Although patients were matched on many key criteria, it is possible that using additional unknown/unstudied characteristics would have resulted in more perfect matching between quartiles.…”

Section: Discussionmentioning

confidence: 99%

“…Coarsened exact matching was performed to control for potential confounding variables previously found to be associated with patient outcomes (Figure 2A). 29‐31 Matches were sought between patients with different LACE+ score quartiles but otherwise identical characteristics (matching criteria). During this process, patients with the highest LACE+ scores (Q4) were matched to patients with lower LACE+ scores (Q3, Q2, Q1).…”

Section: Methodsmentioning

confidence: 99%

Composite score for prediction of 30‐day orthopedic surgery outcomes

Caplan

Winter

Glauser

et al. 2020

Journal Orthopaedic Research

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The LACE+ (Length of stay, Acuity of admission, Charlson Comorbidity Index score, and Emergency department visits in the past 6 months) risk‐prediction tool has never been tested in an orthopedic surgery population. LACE+ may help physicians more effectively identify and support high‐risk orthopedics patients after hospital discharge. LACE+ scores were retrospectively calculated for all consecutive orthopedic surgery patients (n = 18 893) at a multi‐center health system over 3 years (2016‐2018). Coarsened exact matching was employed to create “matched” study groups with different LACE+ score quartiles (Q1, Q2, Q3, Q4). Outcomes were compared between quartiles. In all, 1444 patients were matched between Q1 and Q4 (n = 2888); 2079 patients between Q2 and Q4 (n = 4158); 3032 patients between Q3 and Q4 (n = 6064). Higher LACE+ scores significantly predicted 30D readmission risk for Q4 vs Q1 and Q4 vs Q3 (P < .001). Larger LACE+ scores also significantly predicted 30D risk of ED visits for Q4 vs Q1, Q4 vs Q2, and Q4 vs Q3 (P < .001). Increased LACE+ score also significantly predicted 30D risk of reoperation for Q4 vs Q1 (P = .018), Q4 vs Q2 (P < .001), and Q4 vs Q3 (P < .001).

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“…3,11 In fact, when risk adjusted, differences in readmission rates narrow significantly between hospitals. 12 These concerns appear to be well-founded, as it was shown that the same hospitals that often struggle financially were disproportionately affected by the new policy. 13 An additional concern for hospitals under HRRP was whether it would be cost-effective to develop internal systems and procedures to help limit 30-day readmissions, as the development requires time, effort, and funding.…”

Section: Discussionmentioning

confidence: 99%

Use of a Mandatory Clinical Decision Unit Reduces Readmission Rates following Total Joint Arthroplasty

et al. 2020

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Readmission penalties have encouraged the implementation of protocols to reduce readmission rates. We hypothesized that by keeping postoperative patients, who return to the emergency department (ED) in a clinical decision unit (CDU) until being evaluated by the orthopaedic team, there would be a reduction in the readmission rate after total joint arthroplasty (TJA) at our institution. Our institution mandated the use of the CDU for all potential orthopaedic TJA readmissions. A retrospective review of prospectively collected data was performed on 365 patients who presented to the ED after either total hip arthroplasty (THA) or total knee arthroplasty (TKA). Patients presenting in the year prior to the implementation of the CDU program were compared with patients presenting in the year after implementation. Demographics, length of stay, comorbidities, and 30-day readmission rates were recorded. Additionally, a financial analysis was performed. Overall, for THA and TKA, there were a combined 141 ED visits prior to the implementation of the CDU program and 224 afterward; of these, 40 were readmitted before the CDU program and only 13 were readmitted afterward (p < 0.01). The financial analysis found that the overall 90-day cost for patients in the postoperative period was nearly $800 lower on average (p = 0.027) post-CDU implementation.During the first year of the CDU project at our institution, we significantly reduced the readmission rates following TJA and demonstrated significant cost saving. This is a Level III, prognostic study.

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Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Hospital Readmission Rates Following Hip and Knee Arthroplasty

Abstract: Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.

Cited by 66 publications

References 13 publications

Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates

Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates

Composite score for prediction of 30‐day orthopedic surgery outcomes

Use of a Mandatory Clinical Decision Unit Reduces Readmission Rates following Total Joint Arthroplasty

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