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D J Bachman

Publications and source records attributed to D J Bachman.

4 recordsLinked to original sources
PubMed1999

The sensitivity and specificity of forecasting high-cost users of medical care.

OBJECTIVES: This study compares the ability of 3 risk-assessment models to distinguish high and low expense-risk status within a managed care population. Models are the Global Risk-Assessment Model (GRAM) developed at the Kaiser Permanente Center for Health Research; a logistic version of GRAM; and a prior-expense model. GRAM was originally developed for use in adjusting Medicare payments to health plans. METHODS: Our sample of 98,985 cases was drawn from random samples of memberships of 3 staff/group health plans. Risk factor data were from 1992 and expenses were measured for 1993. Models produced distributions of individual-level annual expense forecasts (or predicted probabilities of high expense-risk status for logistic) for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution to analyze the models' ability to distinguish high and low expense-risk status. Forecast stability was analyzed through bootstrapping. RESULTS: GRAM discriminates better overall than its comparators (although the models are similar for policy-relevant thresholds). All models forecast the highest-cost cases relatively well. GRAM forecasts high expense-risk status better than its comparators within chronic and serious disease categories that are amenable to early intervention but also generates relatively more false positives within these categories. CONCLUSIONS: This study demonstrates the potential of risk-assessment models to inform care management decisions by efficiently screening managed care populations for high expense-risk. Such models can act as preliminary screens for plans that can refine model forecasts with detailed surveys. Future research should involve multiple-year data sets to explore the temporal stability of forecasts.

Adolescent↗
PubMed1998

Building health plan databases to risk adjust outcomes and payments.

OBJECTIVES: To highlight the types and sources of data on medical risk and outcomes routinely collected by managed care organizations over time; to summarize the quality and consistency of these data; and to describe some of the difficulties that arise in collecting, pooling, and using these data. DESIGN: Synthesis of the experiences of two risk-adjustment modeling projects in assembling large volumes of demographic, diagnostic, and expense data from several health maintenance organizations (HMOs) over multiple years. SETTING: Six large HMOs from the Northwest, North Central, and Northeast regions of the USA. INTERVENTIONS: Health plans were approached to participate in a risk-adjustment study, presented with an extensive variable-by-variable data request, and, if willing to participate, asked to specify a desired process for extracting, copying, and transferring selected variables to the study site for purposes of research. Depending on local circumstances, three different approaches were used: (i) health plan staff obtained the data and organized them into the requested study format; (ii) study staff were provided access to health plan data systems to perform the extractions directly; and (iii) health plans hired contract programmers to perform the extractions under the direction of the study team. Key measures of risk and cost were extracted and merged into analysis files. MAIN OUTCOME MEASURES: Complete and consistent eligibility maps, demographic information, inpatient and outpatient diagnoses, and total health plan expense for each enrollee. RESULTS: We have been successful in collecting and integrating complete utilization, morbidity, demographic, and cost data on total memberships of five large HMOs as well as a subset from a sixth HMO, all for multiple years. CONCLUSION: While HMOs vary greatly in the quality and comprehensiveness of their data systems, these attributes have been improving across the board over time. Automated health plan data systems represent potentially valuable sources of data on health risks and outcomes and can be used to benchmark disease management programs and risk adjust capitation payments and medical outcomes.

Databases, Factual↗