Risk-adjusted capitation based on the Diagnostic Cost Group Model: an empirical evaluation with health survey information.
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OBJECTIVE: To evaluate the predictive accuracy of the Diagnostic Cost Group (DCG) model using health survey information. DATA SOURCES/STUDY SETTING: Longitudinal data collected for a sample of members of a Dutch sickness fund. In the Netherlands the sickness funds provide compulsory health insurance coverage for the 60 percent of the population in the lowest income brackets. STUDY DESIGN: A demographic model and DCG capitation models are estimated by means of ordinary least squares, with an individual's annual healthcare expenditures in 1994 as the dependent variable. For subgroups based on health survey information, costs predicted by the models are compared with actual costs. Using stepwise regression procedures a subset of relevant survey variables that could improve the predictive accuracy of the three-year DCG model was identified. Capitation models were extended with these variables. DATA COLLECTION/EXTRACTION METHODS: For the empirical analysis, panel data of sickness fund members were used that contained demographic information, annual healthcare expenditures, and diagnostic information from hospitalizations for each member. In 1993, a mailed health survey was conducted among a random sample of 15,000 persons in the panel data set, with a 70 percent response rate. PRINCIPAL FINDINGS: The predictive accuracy of the demographic model improves when it is extended with diagnostic information from prior hospitalizations (DCGs). A subset of survey variables further improves the predictive accuracy of the DCG capitation models. The predictable profits and losses based on survey information for the DCG models are smaller than for the demographic model. Most persons with predictable losses based on health survey information were not hospitalized in the preceding year. CONCLUSIONS: The use of diagnostic information from prior hospitalizations is a promising option for improving the demographic capitation payment formula. This study suggests that diagnostic information from outpatient utilization is complementary to DCGs in predicting future costs. (+info)
Dissociable deficits in the decision-making cognition of chronic amphetamine abusers, opiate abusers, patients with focal damage to prefrontal cortex, and tryptophan-depleted normal volunteers: evidence for monoaminergic mechanisms.
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We used a novel computerized decision-making task to compare the decision-making behavior of chronic amphetamine abusers, chronic opiate abusers, and patients with focal lesions of orbital prefrontal cortex (PFC) or dorsolateral/medial PFC. We also assessed the effects of reducing central 5-hydroxytryptamine (5-HT) activity using a tryptophan-depleting amino acid drink in normal volunteers. Chronic amphetamine abusers showed suboptimal decisions (correlated with years of abuse), and deliberated for significantly longer before making their choices. The opiate abusers exhibited only the second of these behavioral changes. Importantly, both sub-optimal choices and increased deliberation times were evident in the patients with damage to orbitofrontal PFC but not other sectors of PFC. Qualitatively, the performance of the subjects with lowered plasma tryptophan was similar to that associated with amphetamine abuse, consistent with recent reports of depleted 5-HT in the orbital regions of PFC of methamphetamine abusers. Overall, these data suggest that chronic amphetamine abusers show similar decision-making deficits to those seen after focal damage to orbitofrontal PFC. These deficits may reflect altered neuromodulation of the orbitofrontal PFC and interconnected limbic-striatal systems by both the ascending 5-HT and mesocortical dopamine (DA) projections. (+info)
Evaluating and improving the delivery of heart care: the University of Michigan experience.
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With increasing pressure to curb escalating costs in medical care, there is particular emphasis on the delivery of cardiovascular services, which account for a substantial portion of the current healthcare dollar spent in the United States. A variety of tools were used to improve performance at the University of Michigan Health System, one of the oldest university-affiliated hospitals in the United States. The tools included initiatives to understand outcomes after coronary bypass operations and coronary angioplasty through use of proper risk-adjusted models. Critical pathways and guidelines were implemented to streamline care and improve quality in interventional cardiology, management of myocardial infarction, and preoperative assessment of patients undergoing vascular operations. Strategies to curb unnecessary costs included competitive bidding of vendors for expensive cardiac commodities, pharmacy cost reductions, and changes in nursing staff. Methods were instituted to improve guest services and partnerships with the community in disease prevention and health promotion. (+info)
Health-based payment and computerized patient record systems.
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Health care information technology is changing rapidly and dramatically. A small but growing number of clinicians, especially those in staff and group model HMOs and hospital-affiliated practices, are automating their patient medical records in response to pressure to improve quality and reduce costs. Computerized patient record systems in HMOs track risks, diagnoses, patterns of care, and outcomes across large populations. These systems provide access to large amounts of clinical information; as a result, they are very useful for risk-adjusted or health-based payment. The next stage of evolution in health-based payment is to switch from fee-for-service (claims) to HMO technology in calculating risk coefficients. This will occur when HMOs accumulate data sets containing records on provider-defined disease episodes, with every service linked to its appropriate disease episode for millions of patients. Computerized patient record systems support clinically meaningful risk-assessment models and protect patients and medical groups from the effects of adverse selection. They also offer significant potential for improving quality of care. (+info)
Risk-adjusted outcome models for public mental health outpatient programs.
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OBJECTIVE: To develop and test risk-adjustment outcome models in publicly funded mental health outpatient settings. We developed prospective risk models that used demographic and diagnostic variables; client-reported functioning, satisfaction, and quality of life; and case manager clinical ratings to predict subsequent client functional status, health-related quality of life, and satisfaction with services. DATA SOURCES/STUDY SETTING: Data collected from 289 adult clients at five- and ten-month intervals, from six community mental health agencies in Washington state located primarily in suburban and rural areas. Data sources included client self-report, case manager ratings, and management information system data. STUDY DESIGN: Model specifications were tested using prospective linear regression analyses. Models were validated in a separate sample and comparative agency performance examined. PRINCIPAL FINDINGS: Presence of severe diagnoses, substance abuse, client age, and baseline functional status and quality of life were predictive of mental health outcomes. Unadjusted versus risk-adjusted scores resulted in differently ranked agency performance. CONCLUSIONS: Risk-adjusted functional status and patient satisfaction outcome models can be developed for public mental health outpatient programs. Research is needed to improve the predictive accuracy of the outcome models developed in this study, and to develop techniques for use in applied settings. The finding that risk adjustment changes comparative agency performance has important consequences for quality monitoring and improvement. Issues in public mental health risk adjustment are discussed, including static versus dynamic risk models, utilization versus outcome models, choice and timing of measures, and access and quality improvement incentives. (+info)
Anthem Blue Cross and Blue Shield's coronary services network: a managed care organization's approach to improving the quality of cardiac care for its members.
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OBJECTIVE: To describe a managed care organization's efforts to improve value for its members by forming a coronary services network (CSN). DESIGN: To identify high-quality facilities for its CSN, Anthem Blue Cross and Blue Shield reviewed claims data and clinical data from hospitals that met its general quality standards. An external firm measured and risk-adjusted applicant hospitals' mortality rates. Hospitals that demonstrated superior performance were eligible to join the CSN. In 1996, 2 years after the CSN was formed, clinical outcomes of participants and new applicants were analyzed again by the same external firm. PATIENTS AND METHODS: Data on more than 10,000 consecutive (all-payer) inpatients discharged after coronary bypass surgery in 1993 were collected from 16 applicant hospitals using a uniform format and data definitions. This analysis was expanded to 23 participating and applicant hospitals that discharged more than 13,000 patients who underwent either bypass surgery or coronary revascularization in 1995. We compared risk-adjusted routine length of stay (a measure of efficiency), mortality rates, and adverse outcome rates between CSN and non-CSN facilities. RESULTS: From 1993 to 1995, overall length of stay in the network decreased by 20%, from 12.3 to 9.8 days (P < or = 0.01) and severity-adjusted mortality rates decreased by 7.3%, from 2.9% to 2.7%. Initially, facilities outside the network had comparable efficiency but much higher mortality. However, they improved so much in both measures that their severity-adjusted mortality rate for bypass surgery in 1995 was no more than 10% higher than that of CSN hospitals. CONCLUSION: The creation of a statewide CSN that emphasized and improved the level of performance among providers ultimately benefited the carrier's managed care members. The desirability of participation was evidenced by an increase in the number of applicant hospitals over the 2 years. This may have stimulated quality improvement among competing providers in the region and among CSN facilities themselves. (+info)
Comparing AMI mortality among hospitals in patients 65 years of age and older: evaluating methods of risk adjustment.
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BACKGROUND: Interest in the reporting of risk-adjusted outcomes for patients with acute myocardial infarction is growing. A useful risk-adjustment model must balance parsimony and ease of data collection with predictive ability. METHODS AND RESULTS: From our analysis of 82 359 patients >/=65 years of age admitted with acute myocardial infarction to 2401 hospitals, we derived a parsimonious model that predicts 30-day mortality. The model was validated on a similar group of 78 699 patients from 2386 hospitals. Of the 73 candidate predictor variables examined, 7 variables describing patient characteristics on arrival were selected for inclusion in the final model: age, cardiac arrest, anterior or lateral location of myocardial infarction, systolic blood pressure, white blood cell count, serum creatinine, and congestive heart failure. The area under the receiver-operating characteristic curve for the final model was 0.77 in the derivation cohort and 0.77 in the validation cohort. The rankings of hospitals by performance (in deciles) with this model were most similar to a comprehensive 27-variable model based on medical chart review and least similar to models based on administrative billing codes. CONCLUSIONS: A simple 7-variable risk model performs as well as more complex models in comparing hospital outcomes for acute myocardial infarction. Although there is a continuing need to improve methods of risk adjustment, our results provide a basis for hospitals to develop a simple approach to compare outcomes. (+info)
Case mix adjustment in nursing systems research: the case of resident outcomes in nursing homes.
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Case mix indicates, for a resident population, the degree of risk for developing favorable or unfavorable outcomes. In a study of 164 nursing homes, we explored two methods for combining resident assessment data into a case mix index (CMI). We compared a facility-level, composite CMI to a prevalence-based CMI comprised of 22 separate resident characteristics for their adequacy in explaining resident outcomes. The prevalence-based CMI consistently explained more variance in outcomes than the facility level, composite CMI. This study indicates a reasonable method for using administrative databases containing resident assessment data to adjust for the influence of case mix on nursing home resident outcomes. (+info)