Biomedical subjects

Michael J Goodman

Publications and source records attributed to Michael J Goodman.

7 recordsLinked to original sources

PubMed2004

Does payment drive procedures? Payment for specialty services and procedure rate variations in 3 HMOs.

OBJECTIVE: To study how payment for specialty services affects the rates of performance of invasive procedures by physicians in a number of specialties. STUDY DESIGN: Observational study. PATIENTS AND METHODS: Administrative data from 1996-1997 and 1997-1998 from 3 large health maintenance organizations (HMOs) in the Midwestern and western United States were used to study variations in procedure rates associated with different methods of paying for cardiology, gastroenterology, ophthalmology, orthopedic, and ear, nose and throat services within each HMO. The age-, sex-, and comorbidity adjusted probabilities of undergoing selected, potentially discretionary procedures, were compared within each plan by payment method. RESULTS: After adjustment, rates under fee-for-service payment tended to be higher than those under capitation or salary payment, whereas there was no clear pattern for salary versus capitation payment. Even within a single specialty in a single plan, however, rates did not always follow the same pattern for different procedures. CONCLUSIONS: The payment method for specialty services used by these 3 health plans was variably associated with how likely patients were to undergo a variety of invasive procedures. The effects of contract payment methods for specialty services on health care costs, quality, and outcomes should be further studied, but such studies will challenge the capabilities of health plan data systems.

Adult↗

PubMed2003

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↗

PubMed2003

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↗

PubMed2003

Community-based care and risk of nursing home placement.

OBJECTIVE: To test the substitution hypothesis, that community-based care reduces the probability of institutional placement for at-risk elderly. RESEARCH DESIGN: The closure of the Social Health Maintenance Organization (Social HMO) at HealthPartners (HP) in Minnesota in 1994 and the continuation of the Social HMO at Kaiser Permanente Northwest (KPNW) in Oregon/Washington comprised a "natural experiment." Using multinomial logistic regression analyses, we followed cohorts of Social HMO enrollees for up to 5 years, 1995 to 1999. To adjust for site effects and secular trends, we also followed age- and gender-matched Medicare-Tax Equity and Fiscal Responsibility Act (TEFRA) cohorts, enrolled in the same HMOs but not in the Social HMOs. SUBJECTS: All enrollees in the Social HMO for at least 4 months in 1993 and an age-gender matched sample of Medicare-TEFRA enrollees. To be included, individuals had to be alive and have a period out of an institution after January 1, 1995 (total n = 18,143). MEASURES: The primary data sources were the electronic databases at HP and KPNW. The main outcomes were long-term nursing home placement (90+ days) or mortality. Covariates were age, gender, a comorbidity index, and geographic site effect. RESULTS: Adjusting for variations in the 2 sites, we found no difference in probability of mortality between the 2 cohorts, but approximately a 40% increase in long-term institutional placement associated with the termination of the Social HMO at HealthPartners (odds ratio, 1.43; 95% confidence interval, 1.15-1.79). CONCLUSIONS: The Social HMO appears to help at-risk elderly postpone long-term nursing home placement.

Aged↗

PubMed2002

Chronic disease score as a predictor of hospitalization.

BACKGROUND: The Chronic Disease Score is a risk-adjustment metric based on age, gender, and history of dispensed drugs. We compared four versions of the score for their ability to predict hospitalization among members of eight health maintenance organizations nationwide. METHODS: The study included 29,247 women age 45 years and older. Logistic regression models were constructed using rank quintile and rank decile indicators for each of four scores as predictors of hospitalization during the year after 1 October 1995. Discrimination and model fit were compared using several model properties including the C statistic and the odds ratio comparing highest with lowest quantiles. RESULTS: All Chronic Disease Score versions performed similarly, with the version that predicts total healthcare cost, proposed by Clark et al. (Med Care 1995;33:783-795), performing somewhat better than the other three. The overall risk of hospitalization was 12%. Individuals with higher quantile ranks had a higher risk of hospitalization. Among the Chronic Disease Score versions, the risk of hospitalization ranged from 4% for the lowest decile to 27-29% for the highest decile. Odds ratios comparing the highest with the lowest deciles ranged from 8.9 to 10.2. CONCLUSIONS: The Chronic Disease Score predicts hospitalization and therefore may be a useful indicator of baseline comorbidity for control of confounding.

Age Factors↗

PubMed2002

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↗

PubMedDate not supplied

Pharmaceutical care and health care utilization in an HMO.

CONTEXT: The belief that expanding the role of pharmacists in patient care could improve the safety and efficacy of drug therapy is growing. Specifically, pharmaceutical care programs through which pharmacists provide direct and ongoing counseling to patients have been introduced. Whether such programs reduce medication-related problems or health care utilization is unknown. OBJECTIVE: To assess whether a pharmaceutical care program decreases health care utilization, medication use, or charges. DESIGN: Nonrandomized, controlled trial. SETTING: Staff clinic and freestanding contract pharmacies affiliated with a large HMO in greater Minneapolis-St. Paul (6 intervention pharmacies, 143 control pharmacies). STUDY POPULATION: Adult HMO enrollees (n = 921) with heart or lung disease who used one of the selected pharmacies. INTERVENTION: Patients at intervention pharmacies were invited to participate in the pharmaceutical care program. The protocol-based program consisted of scheduled meetings between trained pharmacists and patients to assess drug therapy, plan goals, and intervene through counseling and/or consultation with other health professionals. OUTCOME MEASURES: Change in number of outpatient clinic visits, unique medications dispensed, and total charges over 1 year of follow-up. RESULTS: In an intention-to-treat analysis (after adjustment for gender, age, Charlson Comorbidity Index, disease category, and the baseline value of the utilization measure), the number of unique medications for patients in the pharmaceutical care group increased more than in the usual care group (1.0 vs. 0.4 unique medications; P = 0.03). There was no difference between the two groups in the change in total number of clinic visits or total costs. In secondary adherence analyses, participants were more likely than the usual care group to increase the number of clinic visits (1.2 vs. -0.9; P = < 0.01) and number of unique medications (1.0 vs. 0.2; P = 0.02). CONCLUSION: Pharmaceutical care for patients with chronic health conditions appears to be associated with a modest increase rather than a decrease in health care utilization.

Counseling↗