J. William Thomas

The concept of access: definition and relationship to consumer satisfaction.

Access is an important concept in health policy and health services research, yet it is one which has not been defined or employed precisely. To some authors “access” refers to entry into or use of the health care system, while to others it characterizes factors influencing entry or use. The purpose of this article is to propose a taxonomic definition of “access.” Access is presented here. as a general concept that summarizes a set of more specific dimensions describing the fit between the patient and the health care system. The specific dimensions are availability, accessibility, accommodation, affordability and acceptability. Using interview data on patient satisfaction, the discriminant validity of these dimensions is investigated. Results provide strong support for the view that differentiation does exist among the five areas and that the measures do relate to the phenomena with which they are identified.

Including health status in medicare’s adjusted average per capita cost capitation formula

Actuarial factors currently comprising Medicares HMO capitation formula, the Adjusted Average Per Capita Cost (AAPCC), are considered by many researchers to be inadequate as predictors of future period health care costs. While it is often suggested that the formula should incorporate beneficiary health status, no measure of health status suitable for this purpose has yet been identified. The authors present initial results from a study of 1,934 randomly selected Medicare beneficiaries in Michigan. Beneficiaries were surveyed to obtain data on several alternative measures of health status. Medicare claims were used to estimate beneficiary health care costs for periods before and after the survey. In regressions on future period Medicare payments, equations including the AAPCC factors plus a health status measure achieved R2 values ranging from 0.013 to 0.072, depending on the health status measure, compared with an R2 value of 0.003 for the equation with AAPCC factors alone.

Selection Bias in Tefra At-risk Hmos

The issue of selection bias was investigated using data from 22 HMOs who are enrolling Medicare beneficiaries under Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) at-risk contracts. The study differs from previously published analyses of this issue in that it deals with the current Medicare risk program (TEFRA) rather than with earlier Demonstration Programs; as an indicator of selection bias, it utilizes beneficiary functional health status at enrollment; and it examines selection not only at the mean of the health status distribution, but at the two tails (very disabled, very able) as well. For each of the participating HMOs, the functional health status of recent Medicare enrollees was compared with that of a control group of randomly chosen fee-for-service beneficiaries. None of the HMOs experienced adverse selection, whether measured in terms of overall (mean) health status of enrollees or in terms of the proportion of the very disabled population that chose to join. Nine of the 22 HMOs were considered to have experienced favorable selection on the basis of the mean health status of new enrollees. In addition, ten more HMOs were found to have experienced favorable selection in one or both tails of the health status distribution. Although a specific cause for the observed enrollment patterns is not identified, speculation is made on factors that may or may not contribute. Evidence suggests that beneficiary self-selection is probably a more important explanation of these patterns than purposeful actions of HMOs to discourage enrollment by sicker beneficiaries (i.e., “skimming”).

Comparing Accuracy of Risk-Adjustment Methodologies Used in Economic Profiling of Physicians

This paper examines the relative accuracy of risk-adjustment methodologies used to profile primary care physician practice efficiency. Claims and membership data from an independent practice association health maintenance organization (HMO) were processed through risk-adjustment software of six different profiling methodologies. The Group R 2 statistic was used to measure, for simulated panels of HMO members, how closely each methodologys cost predictions matched the panels actual costs. All but one methodology explained at least 50% of panel cost variance with panels as small as 25 patients. Group R 2 performance tended to be better when high-cost cases were included rather than excluded from the analyses.

A comparison of self-reported measures of perceived health and functional health in an elderly population

In studies of large elderly populations, two types of measures of physical health status, perceived health and functional health, are commonly used. Although they represent very different conceptions of health, these two types of measures appear often to be used interchangeably. In this paper, we examine changes over time in self-reported measures of perceived health and functional health for a sample of Medicare beneficiaries. By investigating the patterns of change in the two measures for different subgroups of the population, we are able to draw inferences about the appropriateness of each type of measure for specific administrative and/or research situations. The perceived health status measure appears suitable for descriptive studies of the health of elderly populations, while the greater stability of functional health makes this type of measure generally more appropriate in studies investigating relationships between an individuals physical health status and subsequent behavior.

HMO marketing and selection bias: are TEFRA HMOs skimming?

The research evidence indicates that health maintenance organizations (HMOs) participating in the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) At-Risk Program tend to experience favorable selection. Although favorable selection might result from patient decisions, a common conjecture is that it can be induced by HMOs through their marketing activities. The purpose of this study is to examine the relationship between HMO marketing strategies and selection bias in TEFRA At-Risk HMOs. A purposive sample of 22 HMOs that were actively marketing their TEFRA programs was selected and data on organizational characteristics, market area characteristics, and HMO marketing decisions were collected. To measure selection bias in these HMOs, the functional health status of approximately 300 enrollees in each HMO was compared to that of 300 nonenrolling beneficiaries in the same area. Three dependent variables, reflecting selection bias at the mean, the low health tail, and the high health tail of the health status distribution were created. Weighted least squares regressions were then used to identify relationships between marketing elements and selection bias. Subject to the statistical limitations of the study, our conclusion is that it is doubtful that HMO marketing decisions are responsible for the prevalence of favorable selection in HMO enrollment. It also appears unlikely that HMOs were differentially targeting healthy and unhealthy segments of the Medicare market.

Techniques for defining geographic boundaries for health regions

Abstract Many federal and state programs require the geographic partitioning of states into regions for health services planning, monitoring, and/or administration. A common consideration for such programs is that region boundaries should be drawn so as to maximize the proportion of the states population that receives health care services in its region of residence. Defining region boundaries thus may be viewed as a problem of partitioning a set of N small areal units (e.g. counties) into M subsets (regions) so as to minimize interactions (patient flow) among subsets. This paper describes three algorithms for region design and compares them in terms of computer-processing efficiency and solution value based on results from a number of test cases. Application of two of the algorithms, one based on the greedy heuristic and the other incorporating a max-flow/min-cut procedure, to a problem of dividing a metropolitan region into separate service areas for clusters of hospitals is also described.