Impact of healthcare delivery system on where HMO-enrolled seniors purchase medications.
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Aged↗
Biomedical subjects
Paul A Fishman
Publications and source records attributed to Paul A Fishman.
Opportunities and challenges for measuring cost, quality, and clinical effectiveness in health care.
Empirical studies of health care cost, productivity, and output have focused primarily on intermediate goods and services. Consumers are ultimately interested in final goods such as improved health or health-related quality of life, but health services research continues to address whether health services financing and delivery are structured in ways to maximize production of intermediate goods, regardless of the link between these services and final outcomes. Increasing rates of growth of health care cost and dissatisfaction with the quality of U.S. health care force us to reexamine how productivity and cost are analyzed so that research properly informs policy and practice. The authors examine recent changes in the U.S. health care sector that suggest the need to revise how health services research approaches analyses of cost, production, and output; consider alternative notions of final goods; and review the availability and quality of data necessary to conduct this research.
Cost-Benefit Analysis↗
Risk adjustment using automated ambulatory pharmacy data: the RxRisk model.
OBJECTIVES: Develop and estimate the RxRisk model, a risk assessment instrument that uses automated ambulatory pharmacy data to identify chronic conditions and predict future health care cost. The RxRisk model's performance in predicting cost is compared with a demographic-only model, the Ambulatory Clinical Groups (ACG), and Hierarchical Coexisting Conditions (HCC) ICD-9-CM diagnosis-based risk assessment instruments. Each model's power to forecast health care resource use is assessed. DATA SOURCES: Health services utilization and cost data for approximately 1.5 million individuals enrolled in five mixed-model Health Maintenance Organizations (HMOs) from different regions in the United States. STUDY DESIGN: Retrospective cohort study using automated managed care data. SUBJECTS All persons enrolled during 1995 and 1996 in Group Health Cooperative of Puget Sound, HealthPartners of Minnesota and the Colorado, Ohio and Northeast Regions of Kaiser-Permanente. MEASURES RxRisk, an algorithm that classifies prescription drug fills into chronic disease classes for adults and children. RESULTS: HCCs produce the most accurate forecasts of total costs than either RxRisk or ACGs but RxRisk performs similarly to ACGs. Using the R(2) criteria HCCs explain 15.4% of the prospective variance in cost, whereas RxRisk explains 8.7% and ACGs explain 10.2%. However, for key segments of the cost distribution the differences in forecasting power among HCCs, RxRisk, and ACGs are less obvious, with all three models generating similar predictions for the middle 60% of the cost distribution. CONCLUSIONS: HCCs produce more accurate forecasts of total cost, but the pharmacy-based RxRisk is an alternative risk assessment instrument to several diagnostic based models and depending on the nature of the application may be a more appropriate option for medical risk analysis.
Adolescent↗
Predicting costs of care using a pharmacy-based measure risk adjustment in a veteran population.
BACKGROUND: Although most widely used risk adjustment systems use diagnosis data to classify patients, there is growing interest in risk adjustment based on computerized pharmacy data. The Veterans Health Administration (VHA) is an ideal environment in which to test the efficacy of a pharmacy-based approach. OBJECTIVE: To examine the ability of RxRisk-V to predict concurrent and prospective costs of care in VHA and compare the performance of RxRisk-V to a simple age/gender model, the original RxRisk, and two leading diagnosis-based risk adjustment approaches: Adjusted Clinical Groups and Diagnostic Cost Groups/Hierarchical Condition Categories. METHODS: The study population consisted of 161,202 users of VHA services in Washington, Oregon, Idaho, and Alaska during fiscal years (FY) 1996 to 1998. We examined both concurrent and predictive model fit for two sequential 12-month periods (FY 98 and FY 99) with the patient-year as the unit of analysis, using split-half validation. RESULTS: Our results show that the Diagnostic Cost Group /Hierarchical Condition Categories model performs best (R2 = 0.45) among concurrent cost models, followed by ADG (0.31), RxRisk-V (0.20), and age/sex model (0.01). However, prospective cost models other than age/sex showed comparable R2: Diagnostic Cost Group /Hierarchical Condition Categories R2 = 0.15, followed by ADG (0.12), RxRisk-V (0.12), and age/sex (0.01). CONCLUSIONS: RxRisk-V is a clinically relevant, open source risk adjustment system that is easily tailored to fit specific questions, populations, or needs. Although it does not perform better than diagnosis-based measures available on the market, it may provide a reasonable alternative to proprietary systems where accurate computerized pharmacy data are available.
Adolescent↗
Using risk-adjustment models to identify high-cost risks.
BACKGROUND: We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data. METHODS: Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995-1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for "high-cost" prevalences of 1% and 0.5% was calculated, as was the proportion of "high-cost" dollars correctly identified. Results are based on a separate 106,000-observation validation dataset. MAIN RESULTS: For "high-cost" prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83-0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured $963,000 (approximately 3%) more "high-cost" sample dollars than other models. DCGs captured the most "high-cost" dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models. CONCLUSIONS: Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.
Adolescent↗
Health care costs among smokers, former smokers, and never smokers in an HMO.
OBJECTIVE: We estimate long-term health care costs of former smokers compared with continuing and never smokers using a retrospective cohort study of HMO enrollees. Previous research on health care costs associated with former smokers has suggested that quitters may incur greater health care costs than continuing smokers, therefore, getting people to quit creates more expensive health care consumers. We studied the trend in cost for former smokers over seven years after they quit to assess how the cessation experience impacts total health care cost. DATA SOURCES/STUDY SETTING: Group Health Cooperative (GHC), a nonprofit mixed model health maintenance organization in western Washington state. STUDY DESIGN: Retrospective cohort study using automated and primary data collected through telephone interviews. PRINCIPAL FINDINGS: We find that former smokers' costs are significantly greater (p<.05) in the year immediately following cessation relative to continuing smokers, but former smokers' costs fall in year two. This decrease maintains throughout the six-year follow-up period. Although former smokers cost more than continuing smokers in the year after cessation, this increase appears to be transient. Long-term costs for former smokers are not statistically different from those of continuing smokers and cumulative health care expenses are lower by the seventh year postquit. Our evidence suggests that smoking cessation does not increase long-term heath care costs. CONCLUSIONS: Health care costs among former smokers increase relative to continuing smokers in the year after cessation but fall to a level that is statistically indistinguishable in the second year postquit. Any net increase in costs among former smokers relative to continuing smokers appears compensated for within two years post-quit and is maintained for at least six years after cessation.
Adult↗
Lipid-lowering drug use and cardiovascular events after myocardial infarction.
BACKGROUND: The benefits of lipid-lowering drug treatment for the secondary prevention of coronary heart disease have been well established by randomized, controlled trials. Nonetheless, the risk of events has not been compared directly for inhibitors of hydroxymethylglutaryl coenzyme A reductase (statins) and non-statin lipid-lowering drugs. Further, it remains uncertain whether patients in usual practice who are treated with lipid-lowering drugs after myocardial infarction (MI) gain a similar benefit with regard to the risk of cardiovascular events compared with patients in randomized, controlled trials. OBJECTIVE: To assess the association between lipid-lowering drug therapies in usual clinical practice and the risk of cardiovascular events in patients with a first MI who were discharged alive from the hospital. METHODS: An inception-cohort study was performed among 1956 enrollees of Group Health Cooperative who sustained an incident MI between July 1986 and December 1996 and survived for at least 6 months after hospitalization. Subjects with untreated low-density-lipoprotein cholesterol concentrations > 130 mg/dL or untreated total cholesterol concentrations >200 mg/dL were included. The median duration of follow-up after the first MI was 3.3 years. Medical record review was used to collect information on cardiovascular risk factors. Computerized pharmacy records were used to assess antihyperlipidemic drug use during the first 6 months after hospitalization. RESULTS: Compared with 1263 subjects who did not receive lipid-lowering drug treatment, 373 subjects who received statins had a lower risk of recurrent coronary events (relative risk [RR] 0.59; 95% CI 0.39 to 0.89), stroke (RR 0.82; 95% CI 0.35 to 1.95), atherosclerotic cardiovascular mortality (RR 0.49; 95% CI 0.21 to 1.13), and any atherosclerotic cardiovascular event (RR 0.63; 95% CI 0.40 to 0.98). Among 320 subjects who used non-statin drug therapies, the RRs were 0.66 (95% CI 0.45 to 0.97) for recurrent coronary events, 0.95 (95% CI 0.46 to 1.95) for stroke, 0.68 (95% CI 0.35 to 1.32) for cardiovascular mortality, and 0.77 (95% CI 0.53 to 1.11) for any atherosclerotic cardiovascular event, compared with untreated hyperlipidemic patients. CONCLUSIONS: In this study of MI survivors, the use of lipid-lowering drug therapies after hospitalization was associated with a reduced risk of cardiovascular events. These results emphasize the importance of lipid-lowering drug treatment in patients with hyperlipidemia who survive a first MI.
Cholesterol↗
Issues in pooling administrative data for economic evaluation.
Managed care, in particular the health maintenance organization (HMO), now dominates US healthcare delivery, and economic evaluation is receiving increasing attention as a management tool that can be tailored to its perceived business needs. This encourages use of HMO administrative data as an efficient source of resource utilization and cost measures. Use of administrative data coincides with growing research interest in multisite analyses that increase external validity. The best alternative to a nationally representative data set is to pool administrative data from multiple sites within one database. However, pooling administrative data is problematic because HMO data sources reflect differences in systems of care, costing, and coding. This paper describes issues inherent in the pooling of HMO administrative cost data for use in multisite economic evaluations. We describe the attributes of administrative data that are relevant to costing and discuss their implications for multisite economic evaluations. We then briefly describe our experience with pooling multisite cost data, discuss lessons learned, and offer suggestions for researchers working with such data, followed by concluding comments. Multisite administrative data provide unique opportunities to conduct population-based clinical and economic research.
Community Health Planning↗