Reporting comparative results from hospital patient surveys.
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Externally-reported assessments of hospital quality are in increasing demand, as consumers, purchasers, providers, and public policy makers express growing interest in public disclosure of performance information. This article presents an analysis of a groundbreaking program in Massachusetts to measure and disseminate comparative quality information about patients' hospital experiences. The article emphasizes the reporting structure that was developed to address the project's dual goals of improving the quality of care delivered statewide while also advancing public accountability. Numerous trade-offs were encountered in developing reports that would satisfy a range of purchaser and provider constituencies. The final result was a reporting framework that emphasized preserving detail to ensure visibility for each participating hospital's strengths as well as its priority improvement areas. By avoiding oversimplification of the results, the measurement project helped to support a broad range of successful improvement activity statewide. (+info)
The hospital multistay rate as an indicator of quality of care.
(10/409)
OBJECTIVES: To evaluate the hospital multistay rate to determine if it has the attributes necessary for a performance indicator that can be applied to administrative databases. DATA SOURCES/STUDY SETTING: The fiscal year 1994 Veterans Affairs Patient Treatment File (PTF), which contains discharge data on all VA inpatients. STUDY DESIGN: Using a retrospective study design, we assessed cross-hospital variation in (a) the multistay rate and (b) the standardized multistay ratio. A hospital's multistay rate is the observed average number of hospitalizations for patients with one or more hospital stays. A hospital's standardized multistay ratio is the ratio of the geometric mean of the observed number of hospitalizations per patient to the geometric mean of the expected number of hospitalizations per patient, conditional on the types of patients admitted to that hospital. DATA COLLECTION/EXTRACTION METHODS: Discharge data were extracted for the 135,434 VA patients who had one or more admissions in one of seven disease groups. PRINCIPAL FINDINGS: We found that 17.3 percent (28,300) of the admissions in the seven disease categories were readmissions. The average number of stays per person (multistay rate) for an average of seven months of follow-up ranged from 1.15 to 1.45 across the disease categories. The maximum standardized multistay ratio ranged from 1.12 to 1.39. CONCLUSIONS: This study has shown that the hospital multistay rate offers sufficient ease of measurement, frequency, and variation to potentially serve as a performance indicator. (+info)
Validation of risk adjustment models for in-hospital percutaneous transluminal coronary angioplasty mortality on an independent data set.
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OBJECTIVES: We sought to validate recently proposed risk adjustment models for in-hospital percutaneous transluminal coronary angioplasty (PTCA) mortality on an independent data set of high risk patients undergoing PTCA. BACKGROUND: Risk adjustment models for PTCA mortality have recently been reported, but external validation on independent data sets and on high risk patient groups is lacking. METHODS: Between July 1, 1994 and June 1, 1996, 1,476 consecutive procedures were performed on a high risk patient group characterized by a high incidence of cardiogenic shock (3.3%) and acute myocardial infarction (14.3%). Predictors of in-hospital mortality were identified using multivariate logistic regression analysis. Two external models of in-hospital mortality, one developed by the Northern New England Cardiovascular Disease Study Group (model NNE) and the other by the Cleveland Clinic (model CC), were compared using receiver operating characteristic (ROC) curve analysis. RESULTS: In this patient group, an overall in-hospital mortality rate of 3.4% was observed. Multivariate regression analysis identified risk factors for death in the hospital that were similar to the risk factors identified by the two external models. When fitted to the data set, both external models had an area under the ROC curve >0.85, indicating overall excellent model discrimination, and both models were accurate in predicting mortality in different patient subgroups. There was a trend toward a greater ability to predict mortality for model NNE as compared with model CC, but the difference was not significant. CONCLUSIONS: Predictive models for PTCA mortality yield comparable results when applied to patient groups other than the one on which the original model was developed. The accuracy of the two models tested in adjusting for the relatively high mortality rate observed in this patient group supports their application in quality assessment or quality improvement efforts. (+info)
Definition and adjustment of Cesarean section rates and assessments of hospital performance.
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BACKGROUND: Demand is growing for comparative data such as Cesarean section rates, but little effort has been made to develop either standardized definitions or risk adjustment approaches. OBJECTIVE: To determine to what extent a seemingly straightforward indicator like Cesarean section rate will vary when calculated according to differing definitions used by various performance measurement systems. DESIGN: Retrospective data abstraction of 200 deliveries per hospital. SETTING: Fifteen acute care hospitals including two from outside the USA. MEASUREMENTS: Four widely-used performance measurement systems provided specifications for their Cesarean section indicators. Indicator specifications varied on inclusion criteria (whether the population was defined using Diagnostic Related Groups or ICD-9-CM procedure codes or ICD-9-CM diagnosis codes) and risk-adjustment methods and factors. Rates and rankings were compared across hospitals using different Cesarean section indicator definitions and indicators with and without risk adjustment. RESULTS: Calculated Cesarean section rates changed substantially depending on how the numerator and denominator cases were identified. Relative performance based on Cesarean section rankings is affected less by differing indicator definitions than by whether and how risk adjustment is performed. CONCLUSIONS: Judgments about organizational performance should only be made when the comparisons are based upon identical indicators. Research leading to a uniform indicator definition and standard risk adjustment methodology is needed. (+info)
A framework for assessing the effectiveness, efficiency, and equity of behavioral healthcare.
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OBJECTIVE: To evaluate the effectiveness, efficiency, and equity of behavioral healthcare and to guide an assessment of the current state of the art of behavioral health-oriented health services research. STUDY DESIGN: The framework is grounded in previous conceptual work by the authors in defining a prevention- and outcomes-oriented continuum of healthcare and in identifying and integrating the concepts and methods of health services research and policy analysis for assessing healthcare system performance. PATIENTS AND METHODS: The defining assumptions are that (1) the denominator for behavioral healthcare services must encompass a look at the population, not just the patients, who manifest behavioral health risks; and (2) the delivery system to address these needs must extend beyond acute, treatment-oriented services to include both primary prevention and aftercare services for chronic relapsing conditions. RESULTS: Current policy and practice in behavioral healthcare reveal the absence of a comprehensive, coordinated continuum of care; substantial variation in policy and financial incentives to encourage such development; and poorly defined or articulated outcome goals and objectives. The current state of the art of research in this area reflects considerable imprecision in conceptualizing and measuring the effectiveness, efficiency, and equity criteria. Further, these 3 criteria have not been examined together in evaluating system performance. CONCLUSIONS: The first era of behavioral healthcare focused on cost savings in managed care alternatives; the second is focusing on quality and outcomes; a third must consider the issues of equity and access to behavioral healthcare, especially for the most seriously ill and vulnerable, in an increasingly managed care-dominated public and private policy environment. (+info)
Predicting costs of stem-cell transplantation.
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PURPOSE: Few studies have formally evaluated the relationship between costs, baseline patient characteristics, and major complications of stem-cell transplantation. We sought (1) to determine whether obtaining baseline information enabled identification of patients whose treatments would be the most costly and (2) to estimate inpatient costs for managing specific transplantation complications. PATIENTS AND METHODS: We collected inpatient costs and clinical information for 236 consecutive patients undergoing transplantation at a single institution between July 1, 1994, and February 20, 1997. Multivariable linear regression was used to evaluate the associations between baseline patient characteristics and costs of hospitalization for initial transplantation and between clinical events and such costs. RESULTS: The median initial inpatient cost in 1997 dollars was $55,500 for autologous transplantation (range, $28,200 to $148,200) and $105,300 for allogeneic transplantation (range, $32,500 to $338,000). When only baseline variables were considered, use of a mismatched allogeneic donor and year of transplantation were significant predictors of costs. No characteristics predicted which patients would incur the highest 10% of costs. When clinical events were considered, infection and in-hospital death were associated with higher costs in autologous transplant recipients ($18,400 and $20,500, respectively), whereas infection, veno-occlusive disease, acute graft-versus-host disease, and death were predicted to add between $15,300 and $28,100 each to allogeneic transplantation costs. CONCLUSION: We were not able to identify before transplantation the patients whose treatments would be the most costly. However, the association between clinical complications and higher costs suggests that prevention may have significant economic benefits. Interventions that decrease these complications may have favorable cost-benefit ratios even if they do not affect overall survival. (+info)
Early experience with a new model of employer group purchasing in Minnesota.
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The Buyers Health Care Action Group (BHCAG) in the Twin Cities has implemented a new purchasing initiative that offers employees a choice among care systems with nonoverlapping networks of primary care providers. These systems offer a standardized benefit package, submit annual bids, and are paid on a risk-adjusted basis. Employees are provided with information on quality and other differences among systems, and most have financial incentives to choose lower-cost systems. Generally, providers have responded favorably to direct contracting and to risk-adjusted payments but have concerns about the risk-adjustment mechanism used and, more importantly, the strength of employers' commitment to the purchasing model. (+info)
Risk-adjusting acute myocardial infarction mortality: are APR-DRGs the right tool?
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OBJECTIVE: To determine if a widely used proprietary risk-adjustment system, APR-DRGs, misadjusts for severity of illness and misclassifies provider performance. DATA SOURCES: (1) Discharge abstracts for 116,174 noninstitutionalized adults with acute myocardial infarction (AMI) admitted to nonfederal California hospitals in 1991-1993; (2) inpatient medical records for a stratified probability sample of 974 patients with AMIs admitted to 30 California hospitals between July 31, 1990 and May 31, 1991. STUDY DESIGN: Using the 1991-1993 data set, we evaluated the predictive performance of APR-DRGs Version 12. Using the 1990/1991 validation sample, we assessed the effect of assigning APR-DRGs based on different sources of ICD-9-CM data. DATA COLLECTION/EXTRACTION METHODS: Trained, blinded coders reabstracted all ICD-9-CM diagnoses and procedures, and established the timing of each diagnosis. APR-DRG Risk of Mortality and Severity of Illness classes were assigned based on (1) all hospital-reported diagnoses, (2) all reabstracted diagnoses, and (3) reabstracted diagnoses present at admission. The outcome variables were 30-day mortality in the 1991-1993 data set and 30-day inpatient mortality in the 1990/1991 validation sample. PRINCIPAL FINDINGS: The APR-DRG Risk of Mortality class was a strong predictor of death (c = .831-.847), but was further enhanced by adding age and sex. Reabstracting diagnoses improved the apparent performance of APR-DRGs (c = .93 versus c = .87), while using only the diagnoses present at admission decreased apparent performance (c = .74). Reabstracting diagnoses had less effect on hospitals' expected mortality rates (r = .83-.85) than using diagnoses present at admission instead of all reabstracted diagnoses (r = .72-.77). There was fair agreement in classifying hospital performance based on these three sets of diagnostic data (K = 0.35-0.38). CONCUSIONS: The APR-DRG Risk of Mortality system is a powerful risk-adjustment tool, largely because it includes all relevant diagnoses, regardless of timing. Although some late diagnoses may not be preventable, APR-DRGs appear suitable only if one assumes that none is preventable. (+info)