Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes.
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ABSTRACT:
SUBMITTER: DeCenso B
PROVIDER: S-EPMC5867142 | biostudies-literature | 2018 Apr
REPOSITORIES: biostudies-literature
Publications
Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes.
DeCenso Brendan B Duber Herbert C HC Flaxman Abraham D AD Murphy Shane M SM Hanlon Michael M
Health services research 20170313 2
<h4>Objective</h4>To assess the changes in patient outcome prediction and hospital performance ranking when incorporating diagnoses as risk adjusters rather than comorbidity indices.<h4>Data sources</h4>Healthcare Cost and Utilization Project State Inpatient Databases for New York State, 2005-2009.<h4>Study design</h4>Conducted tree-based classification for mortality and readmission by incorporating discrete patient diagnoses as predictors, comparing with traditional comorbidity indices such as ...[more]
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Project description:The increase in hospitalist-provided inpatient care may be accompanied by an expectation of improvement on patient outcomes. To date, the association between utilization of hospitalists and the publicly reported patient outcomes is unknown.Assess the relationship between hospitalist utilization and performance on 6 publicly reported patient outcomes.Cross-sectional study.Representatives of 598 hospitals in the United States with direct knowledge of inpatient service models.Survey of hospital personnel with knowledge of hospitalist use and hospitalist programs.Six publicly reported quality outcome measures across 3 medical conditions: acute myocardial infarction (AMI), congestive heart failure (HF), and pneumonia. Using multivariable regression models, we assessed the relationship between presence of hospitalists and performance on each outcome measure; we further assessed the relationship between the percentage of patients admitted by hospitalists and each outcome measure.Of 598 respondents, 429 (72%) reported the use of hospitalist services. In the comparison of hospitals with and without hospitalists, there was no statistically significant difference on any of the mortality or readmissions measures with the exception of the risk-stratified readmission rate for heart failure. For hospitals that used hospitalists, there was no significant change in any of the outcome measures with increasing percentage of patients admitted by hospitalists.The presence of hospitalists is not an independent predictor of performance on publicly reported mortality and readmissions measures for AMI, HF, or pneumonia. It is likely that broader system or organizational interventions are required to improve performance on patient outcomes.
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Project description:ImportanceSurvival rates after in-hospital cardiac arrest (IHCA) vary significantly among US centers; whether this variation is owing to differences in IHCA care quality is unknown.ObjectiveTo evaluate hospital-level variation to determine whether hospital process composite performance measures of IHCA care quality are associated with patient outcomes.Design, setting, and participantsUsing data from the American Heart Association's Get With the Guidelines-Resuscitation (GWTG-R) program, we analyzed 35 283 patients 18 years or older with IHCA treated at 261 US hospitals from January 1, 2010, through December 31, 2012. We calculated the hospital process composite performance score for IHCA using 5 guideline-recommended process measures. Opportunity-based scores were calculated for all patients, aggregated at the hospital level, divided into quartiles, and then associated with risk-standardized survival and neurologic status by a test for trend. The scores were then evaluated through hierarchical logistic regression and reported as odds ratios per 10% increment in hospital process composite performance.InterventionsAcute care treatments for IHCA.Main outcomes and measuresThe primary outcome was survival to discharge measured as risk standard survival rates, and the secondary outcome was favorable neurologic status at hospital discharge.ResultsOf the 35 283 adults included in this study, the median age was 67 years (interquartile range [IQR] 56-78 years), and 57.9% were male. The median IHCA hospital process composite performance was 89.7% (interquartile range, 85.4%-93.1%) and varied among hospital quartiles from 82.6% (lowest) to 94.8% (highest). The IHCA hospital process composite performance was linearly associated with risk-standardized hospital survival to discharge rates: 21.1%, 21.4%, 22.8%, and 23.4% from lowest to highest performance quartiles, respectively (P < .001). After adjustment, each 10% increase in a hospital's process composite performance was associated with a 22% higher odds of survival (adjusted odds ratio, 1.22; 95% CI, 1.08-1.37; P = .01). Hospital process composite quality performance was also associated with favorable neurologic status at discharge (P = .004).Conclusions and relevanceThe quality of guideline-based care for IHCA varies significantly among US hospitals and is associated with patient survival and neurologic outcomes.
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Project description:BackgroundResearch investigating risk factors for hospital-acquired pressure injury (HAPI) has primarily focused on the characteristics of patients and nursing staff. Limited data are available on the association of hospital characteristics with HAPI.ObjectiveWe aimed to quantify the association of hospital characteristics with HAPI and their effect on residual hospital variation in HAPI risk.MethodsWe employed a retrospective cohort study design with split validation using hierarchical survival analysis. This study extends the analysis "Hospital-Acquired Pressure Injury (HAPI): Risk Adjusted Comparisons in an Integrated Healthcare Delivery System" by Rondinelli et al. (2018) to include hospital-level factors. We analyzed 1,661 HAPI episodes among 728,266 adult hospitalization episodes across 35 California Kaiser Permanente hospitals, an integrated healthcare delivery system between January 1, 2013, and June 30, 2015.ResultsAfter adjusting for patient-level and hospital-level variables, 2 out of 12 candidate hospital variables were statistically significant predictors of HAPI. The hazard for HAPI decreased by 4.8% for every 0.1% increase in a hospital's mean mortality ([6.3%, 2.6%], p < .001), whereas every 1% increase in a hospital's proportion of patients with a history of diabetes increased HAPI hazard by 5% ([-0.04%, 10.0%], p = .072). Addition of these hierarchical variables decreased unexplained hospital variation of HAPI risk by 35%.DiscussionWe found hospitals with higher patient mortality had lower HAPI risk. Higher patient mortality may decrease the pool of patients who live to HAPI occurrence. Such hospitals may also provide more resources (specialty staff) to care for frail patient populations. Future research should aim to combine hospital data sets to overcome power limitations at the hospital level and should investigate additional measures of structure and process related to HAPI care.
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Project description:BACKGROUND:The conceptualization of hospital quality indicators usually includes some form of risk adjustment to account for hospital differences in case mix. For binary outcome variables like in-hospital mortality, frequently utilized risk adjusted measures include the standardized mortality ratio (SMR), the risk standardized mortality rate (RSMR), and excess risk (ER). All of these measures require the estimation of expected hospital mortality, which is often based on logistic regression models. In this context, an issue that is often neglected is correlation between hospital performance (e.g. care quality) and patient-specific risk factors. The objective of this study was to investigate the impact of such correlation on the adequacy of hospital rankings based on different measures and methods. METHODS:Using Monte Carlo simulation, the impact of correlation between hospital care quality and patient-specific risk factors on the adequacy of hospital rankings was assessed for SMR/RSMR, and ER based on logistic regression and random effects logistic regression. As an alternative method, fixed effects logistic regression with Firth correction was considered. The adequacies of the resulting hospital rankings were assessed by the shares of hospitals correctly classified into quintiles according to their true (unobserved) care qualities. RESULTS:The performance of risk adjustment approaches based on logistic regression and random effects logistic regression declined when correlation between care quality and a risk factor was induced. In contrast, fixed-effects-based estimations proved to be more robust. This was particularly true for fixed-effects-logistic-regression-based ER. In the absence of correlation between risk factors and care quality, all approaches showed similar performance. CONCLUSIONS:Correlation between risk factors and hospital performance may severely bias hospital rankings based on logistic regression and random effects logistic regression. ER based on fixed effects logistic regression with Firth correction should be considered as an alternative approach to assess hospital performance.
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Project description: Not available
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