Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes.
Ontology highlight
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]
Similar Datasets
Project description:Importance:Current surgical quality metrics might be insufficient to fully judge the quality of certain operations because they are not procedure specific. Hypocalcemia, recurrent laryngeal nerve (RLN) injury, and hematoma are considered to be the most relevant outcomes to measure after thyroidectomy. Whether these outcomes can be used as hospital quality metrics is unknown. Objectives:To evaluate whether thyroidectomy-specific outcomes vary among hospitals, whether the addition of thyroidectomy-specific variables affects risk adjustment, and whether differences in hospital performance are associated with thyroidectomy-specific care processes. Design, Setting, and Participants:In this retrospective cohort study, patients undergoing thyroidectomies from January 1, 2013, through December 31, 2015, at hospitals participating in the American College of Surgeons' National Surgical Quality Improvement Program were studied. Exposure:Thyroidectomy-related care. Main Outcomes and Measures:Clinically severe hypocalcemia, RLN injury, and clinically significant hematoma within 30 days of thyroid surgery and hospital-level performance variation, change in risk adjustment, and association with processes. Results:Overall, 14 540 patients (mean [SD] age, 52.1 [15.0] years; 11 499 [79.1%] female) underwent operations at 98 hospitals. Because operations missing thyroidectomy-specific outcomes were excluded, the numbers of operations and hospitals analyzed differed by outcome. Of 14 540 operations included, clinically severe hypocalcemia occurred in 450 patients (3.3% overall, 0.6% after partial, and 4.7% after subtotal or total thyroidectomy), RLN injury in 755 patients (5.7% overall, 4.2% after partial, and 6.6% after subtotal or total thyroidectomy), and hematoma in 175 patients (1.3%). Hospital performance varied for hypocalcemia and RLN injury but not for hematoma. Hospital performance rankings were largely unaffected by the inclusion of thyroidectomy-specific data in risk adjustment. With regard to processes, patients undergoing thyroidectomies at the best-performing vs worst-performing hospitals less frequently had their postoperative parathyroid hormone level measured (593 [19.9%] vs 457 [31.7%], P < .001) and more often were prescribed oral calcium, vitamin D, or both (2281 [76.6%] vs 962 [66.8%], P < .001). When profiled by RLN injury, use of energy devices (1517 [69.1%] vs 507 [55.2%], P < .001) and intraoperative nerve monitoring (1223 [55.7%] vs 346 [37.7%], P < .001) were more prevalent at the best- compared with the worst-performing hospitals. Conclusions and Relevance:Postoperative hypocalcemia and RLN injury, but not hematoma, potentially could be used as thyroidectomy-specific national hospital quality improvement metrics. Strategies aimed at reducing these complications after thyroidectomy may improve the care of these patients.
| S-EPMC5833609 | biostudies-literature
Project description:Adjustment for the differing risk profiles of patients is essential to the use of administrative hospital data for epidemiological research. Smoking is an important factor to include in such adjustments, but the accuracy of the diagnostic codes denoting smoking in hospital records is unknown. The aims of this study were to measure the validity of current smoking and ever smoked status identified from diagnoses in hospital records using a range of algorithms, relative to self-reported smoking status; and to examine whether the misclassification of smoking identified through hospital data is differential or non-differential with respect to common exposures and outcomes. Data from the baseline questionnaire of the 45 and Up Study, completed by 267,153 residents of New South Wales (NSW), Australia, aged 45 years and older, were linked to the NSW Admitted Patient Data Collection. Patients who had been admitted to hospital for an overnight stay between 1 July 2005 and the date of completion of the questionnaire (1 January 2006 to 2 March 2009) were included. Smokers were identified by applying a range of algorithms to hospital admission histories, and compared against self-reported smoking in the questionnaire ('gold standard'). Sensitivities for current smoking ranged from 59% to 84%, while specificities were 94% to 98%. Sensitivities for ever smoked ranged from 45% to 74% and specificities were 93% to 97%. For the majority of algorithms, sensitivities and/or specificities differed significantly according to principal diagnosis, number of comorbidities, socioeconomic status, residential remoteness, Indigenous status, 28 day readmission and 365 day mortality. The identification of smoking through diagnoses in hospital data results in differential misclassification. Risk adjustment based on smoking identified from these data will yield potentially misleading results. Systematic capture of information about smoking in hospital records using a mandatory item would increase the utility of administrative data for epidemiological research.
| S-EPMC3988140 | biostudies-literature
Project description:Sociodemographically disadvantaged patients have worse outcomes on some quality measures that inform Medicare Advantage plan ratings. Performance measurement that does not adjust for sociodemographic factors may penalize plans that disproportionately serve disadvantaged populations. We assessed the impact of adjusting for socioeconomic and demographic factors (sex, race/ethnicity, dual eligibility, disability, rurality, and neighborhood disadvantage) on Medicare Advantage plan rankings for blood pressure, diabetes, and cholesterol control. After adjustment, 20.3 percent, 19.5 percent, and 11.4 percent of Medicare Advantage plans improved by one or more quintiles in rank on the diabetes, cholesterol, and blood pressure measures, respectively. Plans that improved in ranking after adjustment enrolled higher proportions of disadvantaged enrollees. Adjusting quality measures for socioeconomic factors is important for equitable payment and quality reporting. Our study suggests that plans serving disadvantaged populations would have improved relative rankings for three important outcome measures if socioeconomic factors were included in risk-adjustment models.
| S-EPMC6063315 | biostudies-literature
Project description:BackgroundWorldwide, risk-equalization (RE) models in competitive health insurance markets have evolved from simple demographic models to sophisticated models containing diagnosis and pharmacy-based indicators of health. However, these models still have important imperfections; adding information on (diagnoses of) physiotherapy treatment may further improve RE-models. Therefore, a new risk-adjuster based on physiotherapy costs in the prior year was introduced in the Dutch RE-model of 2016.MethodsPhysiotherapy claims-data (2012) and administrative data on costs and risk-characteristics (2013) for 94% of the Dutch population (N = 15.8 million) are used to evaluate the current risk-adjuster based on physiotherapy costs and to assess the effects of replacing it by different modalities of a risk-adjuster based on physiotherapy diagnoses. Of the 89 diagnoses in the claims-data, 62 are dropped because they relate to temporary health problems. The 27 retained diagnoses are added to the Dutch model in 4 modalities: 27 separate risk-classes, 9 diagnosis-clusters based on main pathology category, 4 diagnosis-clusters based on residual costs, and the 4 clusters of modality 3 interacted with age.ResultsAlthough the cost-based risk-adjuster improves the model's predictive power and removes the average undercompensation (€919) for enrollees with physiotherapy costs in the prior year, it is outperformed by all 4 diagnosis-based modalities. Of these modalities, modality 3 is preferred based on its simplicity and comparable predictive power.ConclusionsAdding information on physiotherapy can further improve the performance of sophisticated RE-models. Regarding the Dutch model, a risk-adjuster containing 4 risk-classes for clustered diagnoses based on residual costs is the preferred modality.
| S-EPMC5813071 | biostudies-literature
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.
| S-EPMC3531241 | biostudies-literature
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.
| S-EPMC6545891 | biostudies-literature
Project description: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.ObjectivesTo 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 participantsUsing 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 measuresAll-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 relevanceHospitals 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.
| S-EPMC6248207 | biostudies-literature
Project description:BACKGROUND:Research 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. OBJECTIVE:We aimed to quantify the association of hospital characteristics with HAPI and their effect on residual hospital variation in HAPI risk. METHODS:We 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. RESULTS:After 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%. DISCUSSION:We 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.
| S-EPMC6023742 | biostudies-literature
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.
| S-EPMC6892499 | biostudies-literature
Project description:ObjectivesTo develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.DesignA nationally representative retrospective study.SettingRepresentative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.ParticipantsRepresentative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measuresIn-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).ResultsA total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as 'reference model', was 0.08% (10th and 90th percentiles: -1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.ConclusionsThe models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement.
| S-EPMC8043007 | biostudies-literature