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Comparison of a Potential Hospital Quality Metric With Existing Metrics for Surgical Quality-Associated Readmission.

ABSTRACT: Importance:The existing readmission quality metric does not meaningfully distinguish readmissions associated with surgical quality from those that are not associated with surgical quality and thus may not reflect the quality of surgical care. Objective:To compare a quality metric that classifies readmissions associated with surgical quality with the existing metric of any unplanned readmission in a surgical population. Design, Setting, and Participants:Cohort study using US nationwide administrative data collected on 4 high-volume surgical procedures performed at 103 Veterans Affairs hospitals from October 1, 2007, through September 30, 2014. Data analysis was conducted from October 1, 2017, to January 24, 2019. Main Outcomes and Measures:Hospital-level rates of unplanned readmission (existing metric) and surgical readmissions associated with surgical quality (new metric) in the 30 days following hospital discharge for an inpatient surgical procedure. Results:The study population included 109?258 patients who underwent surgery at 103 hospitals. Patients were majority male (94.1%) and white (78.2%) with a mean (SD) age of 64.0 (10.0) years at the time of surgery. After case-mix adjustment, 30-day surgical readmissions ranged from 4.6% (95% CI, 4.5%-4.8%) among knee arthroplasties to 11.1% (95% CI, 10.9%-11.3%) among colorectal resections. The new surgical readmission metric was significantly correlated with facility-level postdischarge complications for all procedures, with ? coefficients ranging from 0.33 (95% CI, 0.13-0.51) for cholecystectomy to 0.52 (95% CI, 0.38-0.68) for colorectal resection. Correlations between postdischarge complications and the new surgical readmission metric were higher than correlations between complications and the existing readmission metric for all procedures examined (knee arthroplasty: 0.50 vs 0.48; hip replacement: 0.44 vs 0.18; colorectal resection: 0.52 vs 0.42; and cholecystectomy: 0.33 vs 0.10). When compared with using the existing readmission metric, using the new surgical readmission metric could change hip replacement-associated payment penalty determinations in 28.4% of hospitals and knee arthroplasty-associated penalties in 26.0% of hospitals. Conclusions and Relevance:In this study, surgical quality-associated readmissions were more correlated with postdischarge complications at a higher rate than were unplanned readmissions. Thus, a metric based on such readmissions may be a better measure of surgical care quality. This work provides an important step in the development of future value-based payments and promotes evidence-based quality metrics targeting the quality of surgical care.

SUBMITTER: Graham LA

PROVIDER: S-EPMC6481441 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Comparison of a Potential Hospital Quality Metric With Existing Metrics for Surgical Quality-Associated Readmission.

Graham Laura A LA Mull Hillary J HJ Wagner Todd H TH Morris Melanie S MS Rosen Amy K AK Richman Joshua S JS Whittle Jeffery J Burns Edith E Copeland Laurel A LA Itani Kamal M F KMF Hawn Mary T MT

JAMA network open 20190405 4

<h4>Importance</h4>The existing readmission quality metric does not meaningfully distinguish readmissions associated with surgical quality from those that are not associated with surgical quality and thus may not reflect the quality of surgical care.<h4>Objective</h4>To compare a quality metric that classifies readmissions associated with surgical quality with the existing metric of any unplanned readmission in a surgical population.<h4>Design, setting, and participants</h4>Cohort study using US ...[more]

PMID: 31002316

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Project description:BackgroundAfter release of the Comprehensive Care for Joint Replacement bundle, there has been increased emphasis on reducing readmission rates for total knee arthroplasty (TKA). The potential for a separate, clinically-relevant metric, TKA revision rates within a year following surgery, has not been fully explored. Based on this, we compared rates and payments for TKA readmission and revision procedures as metrics for improving quality and cost.MethodsWe utilized the 2013 Nationwide Readmission Database (NRD) to examine national readmission and revision rates, the reasons for revision procedures, and associated costs for elective TKA procedures. As data are not linked across years, we examined revision rates for TKA completed in the month of January by capturing revision procedures in the subsequent following 11-month period to approximate a 1-year revision rate. Diagnosis and procedure codes for revision procedures were collected. Average readmission and revision procedure costs were then calculated, and the cost distributed across the entire TKA population.ResultsWe identified 20,851 patients having TKA surgery. The mean unadjusted 30- and 90-day TKA readmission rates were 3.4% and 5.8%, respectively. In contrast, the mean unadjusted 3-month and approximate 1-year reoperation rates were 1.0% and 1.6%, respectively. The most common cause for revision was periprosthetic joint infection, which accounting for 62% of all reported revision procedures. The mean payment for 90-day readmission was roughly half ($10,589±$11,084) of the mean inpatient payment for single reoperation procedure at 90 days ($20,222±$17,799). Importantly, nearly half (46%) of all 90-day readmissions were associated with a reoperation event within the first year.ConclusionsReadmission following TKA is associated with a 1-year reoperation in approximately half of patients. These reoperations represent a significant patient burden and have a higher per episode cost. Early reoperation may represent a more clinically relevant target for quality improvement and cost containment.

Project description:ImportanceMedical patients discharged from safety-net hospitals (SNHs) experience higher readmission rates compared with those discharged from non-SNHs. However, little is known about whether this association persists for surgical patients.ObjectivesTo examine differences in readmission rates between SNHs and non-SNHs among surgical patients after discharge and determine whether hospital characteristics might account for some of the variation.Design, setting, and participantsThis observational retrospective study linked the Healthcare Cost and Utilization Project State Inpatient Databases of the Agency for Healthcare Research and Quality from January 1, 2011, through December 31, 2014, for 4 states (New York, Florida, Iowa, and Washington) with data from the 2014 American Hospital Association annual survey. After identifying surgical discharges, SNHs were defined as those with the top quartile of inpatient stays paid by Medicaid or self-paid. Hospital-level risk-standardized readmission rates (RSRRs) for surgical discharges were calculated. The association between hospital RSRRs and hospital characteristics was evaluated with bivariate analyses. An estimated multivariable hierarchical linear regression model was used to examine variation in hospital RSRRs, adjusting for hospital characteristics, state, year, and SNH status. Data were analyzed from June 1, 2017, through March 1, 2018.ExposuresSurgical care at an SNH.Main outcomes and measuresReadmission after an index surgical admission.ResultsA total of 1 252 505 patients across all 4 years and states were included in the analysis (51.7% women; mean [SD] age, 52.7 [18.1] years). Bivariate analyses found that SNHs had higher mean (SD) surgical RSRRs compared with non-SNHs; significant differences were found for New York (9.6 [0.1] vs 10.9 [0.1]; P < .001) and Florida (11.6 [0.1] vs 12.1 [0.1]; P = .001). The SNHs also had higher RSRRs in these 2 states when stratified by hospital funding (nonfederal government SNHs in New York, 11.9 [0.2]; for-profit, private SNHs in Florida, 13.1 [0.2]; P < .001 for both); however, bed size was a significant factor for higher mean (SD) RSRRs only for New York (200 to 399 beds, 12.0 [0.4]; P = .006). Similar results were found for multivariable linear regression models; RSRRs were 1.02% higher for SNHs compared with non-SNHs (95% CI, 0.75%-1.29%; P < .001). Increased RSRRs were observed for hospitals in New York and Florida, teaching hospitals, and investor-owned hospitals. Factors associated with reduced RSRRs included presence of an ambulatory surgery center, cardiac catheterization capabilities, and high surgical volume.Conclusions and relevanceAccording to results of this study, surgical patients treated at SNHs experienced slightly higher RSRRs compared with those treated at non-SNHs. This association persisted after adjusting for year, state, and hospital factors, including teaching status, hospital bed size, and hospital volume.

Project description:ImportanceAlthough current federal quality improvement programs do not include cancer surgery, the Centers for Medicare & Medicaid Services and other payers are considering extending readmission reduction initiatives to include these and other common high-cost episodes.ObjectivesTo quantify between-hospital variation in quality-related outcomes and identify hospital characteristics associated with high and low performance.Design, setting, and participantsThis retrospective cohort study obtained data through linkage of the California Cancer Registry to hospital discharge claims databases maintained by the California Office of Statewide Health Planning and Development. All 351 acute care hospitals in California at which 1 or more adults underwent curative intent surgery between January 1, 2007, and December 31, 2011, with analyses finalized July 15, 2018, were included. A total of 138 799 adults undergoing surgery for colorectal, breast, lung, prostate, bladder, thyroid, kidney, endometrial, pancreatic, liver, or esophageal cancer within 6 months of diagnosis, with an American Joint Committee on Cancer stage of I to III at diagnosis, were included.Main outcomes and measuresMeasures included adjusted odds ratios and variance components from hierarchical mixed-effects logistic regression analyses of in-hospital mortality, 90-day readmission, and 90-day mortality, as well as hospital-specific risk-adjusted rates and risk-adjusted standardized rate ratios for hospitals with a mean annual surgical volume of 10 or more.ResultsAcross 138 799 patients at the 351 included hospitals, 8.9% were aged 18 to 44 years and 45.9% were aged 65 years or older, 57.4% were women, and 18.2% were nonwhite. Among these, 1240 patients (0.9%) died during the index admission. Among 137 559 patients discharged alive, 19 670 (14.3%) were readmitted and 1754 (1.3%) died within 90 days. After adjusting for patient case-mix differences, evidence of statistically significant variation in risk across hospitals was identified, as characterized by the variance of the random effects in the mixed model, for all 3 metrics (P < .001). In addition, substantial variation was observed in hospital performance profiles: across 260 hospitals with a mean annual surgical volume of 10 or more, 59 (22.7%) had lower-than-expected rates for all 3 metrics, 105 (40.4%) had higher-than-expected rates for 2 of the 3, and 19 (7.3%) had higher-than-expected rates for all 3 metrics.Conclusions and relevanceAccounting for patient case-mix differences, there appears to be substantial between-hospital variation in in-hospital mortality, 90-day readmission, and 90-day mortality after cancer surgical procedures. Recognizing the multifaceted nature of hospital performance through consideration of mortality and readmission simultaneously may help to prioritize strategies for improving surgical outcomes.

Project description:BACKGROUND:Whether types of hospitals with high readmission rates also have high overall postdischarge acute care utilization (including emergency department and observation care) is unknown. DESIGN:Cross-sectional analysis. SUBJECTS:Nonfederal United States acute care hospitals. MEASURES:Using methodology established by the Centers for Medicare & Medicaid Services, we calculated each hospital's "excess days in acute care" for fee-for-service (FFS) Medicare beneficiaries aged over 65 years discharged after hospitalization for acute myocardial infarction, heart failure (HF), or pneumonia, representing the mean difference between predicted and expected total days of acute care utilization in the 30 days following hospital discharge, per 100 discharges. We assessed the multivariable association of 8 hospital characteristics with excess days in acute care and the proportion of hospitals with each characteristic that were statistical outliers (95% credible interval estimate does not include 0). RESULTS:We included 2184 hospitals for acute myocardial infarction [228 (10.4%) better than expected, 549 (25.1%) worse than expected], 3720 hospitals for HF [484 (13.0%) better and 840 (22.6%) worse], and 4195 hospitals for pneumonia [673 (16.0%) better, 1005 (24.0%) worse]. Results for all conditions were similar. Worse than expected outliers for pneumonia included: 18.8% of safety net hospitals versus 26.1% of nonsafety net hospitals; 16.7% of public hospitals versus 33.1% of for-profit hospitals; 19.5% of nonteaching hospitals versus 52.2% of major teaching hospitals; 7.9% of rural hospitals versus 42.1% of large urban hospitals; 5.9% of hospitals with 24-<50 beds versus 58% of hospitals with >500 beds; and 29.0% of hospitals with nurse-to-bed ratios >1.0-1.5 versus 21.7% of hospitals with ratios >2.0. CONCLUSIONS:Including emergency department and observation stays in measures of postdischarge utilization produces similar results as measuring only readmissions in that major teaching, urban and for-profit hospitals still perform disproportionately poorly versus nonteaching or public hospitals. However, it enables identification of more outliers and a more granular assessment of the association of hospital factors and outcomes.

Project description:ImportanceThirty-day readmission rates have been suggested as a marker for quality of care. By investigating the factors associated with readmissions in all otolaryngology subspecialties we provide data relevant for the development of risk stratification systems to improve outcomes.ObjectiveTo establish the association of surgical and hospital volume and patient characteristics with 30-day readmission rates to guide the development of otolaryngology-specific risk stratification models.Design, setting, and participantsA retrospective cohort study including adult patients who underwent inpatient otolaryngology surgery in New York State between 1995 and 2015 was conducted using the Statewide Planning and Research Cooperative System (SPARCS). Regression techniques were used to describe relationships of patient-level factors, hospital, and surgeon volume to 30-day readmission rates in New York State.Main outcomes and measuresThe main outcome measures were patient-, surgeon-, and hospital-level risk factors for readmission. Secondary outcome measures were rate of readmissions by subspecialty procedure and by diagnosis on readmission.ResultsWe identified 254 257 cases of otolaryngology surgery (147 065 women [58%], mean [SD] age 50 [17] years). The 30-day readmission rate was 6%. In a multivariable model, odds ratios (ORs) identified Medicaid insurance (OR, 1.46; 99% CI, 1.36-1.57), Medicare insurance (OR, 1.32; 99% CI, 1.24-1.42), bottom quartile income (OR, 1.08; 99% CI, 1.01-1.15), patient comorbidities measured by the Charlson Comorbidity Index (CCI) (CCI >1; OR, 2.31; 99% CI, 2.16-2.47), length of stay (LOS) (LOS >10 days; OR, 2.29; 99% CI, 2.00-2.45), rhinology (OR, 1.37; 99% CI, 1.24-1.51), laryngology (OR, 1.98; 99% CI, 1.62-2.43), and head and neck cancer (OR, 1.27; 99% CI, 1.17-1.37) procedures as readmission predictors. High-volume surgeons were protective of 30-day readmission (OR, 0.67; 99% CI, 0.635-0.708) relative to low volume. Hospital volume was not significantly associated to readmissions. The most common causes of readmission included wound- (2682 patients, 18%), respiratory- (1776 patients, 12%), cardiovascular- (1210 patients, 8%), and volume- (1089 patients, 7%) related disorders.Conclusions and relevanceThis study evaluated the combined effects of patient-, surgeon-, and hospital-level factors on 30-day readmission after otolaryngology surgery. Socioeconomic factors, patient comorbidities, surgeon volumes, and procedure were significantly associated with 30-day readmission. Though the cause of 30-day readmission is multifactorial, a large portion is driven by socioeconomic factors. Addressing these disparities at the system level is necessary to address the described readmission disparities. The development of risk-stratification models based on patient-, procedure-, and surgeon-level factors may help facilitate resource distribution.

Dataset Information

Comparison of a Potential Hospital Quality Metric With Existing Metrics for Surgical Quality-Associated Readmission.

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Comparison of a Potential Hospital Quality Metric With Existing Metrics for Surgical Quality-Associated Readmission.

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