Linking Automated Databases for Research in Managed Care Settings

The rapid and continuing extension of managed care coverage to larger segments of the general population presents new opportunities for researchers who study the costs and effectiveness of medical care and those with broader interests in public health issues. As larger portions of geographically defined populations (particularly elderly and low-income persons) are covered by managed care organizations, the relevance of event rates and utilization patterns to public health increases. This article describes databases that are currently found in or used by older health maintenance organizations (HMOs) and offers examples of the research that can be conducted by linking these databases. The Opportunity for Research The dream of a computer-based (electronic) medical record is almost as old as the first HMO [1]. With large medical staffs working in multiple medical facilities, the daily problem of missing medical charts led naturally to a vision of electronically stored records that could be accessible at any time from multiple locations. It was also recognized early on that the same data elements needed to maintain electronic records could easily be reconfigured into clinical research databases [2] and that such databases (in the context of defined populations) would create remarkable research opportunities. Progress toward developing practical electronic medical records has quickened in the 1990s because advances in computer technology and networking strategies have led to more efficient, affordable systems than were previously possible. Equally important, the growth of managed health care has increased competition among HMOs, thereby heightening the need for efficient clinical services and information systems that support these services. The adoption of disease management strategies [3] and outcome-based approaches to quality assurance [4], increasing sophistication of employer-purchasers when requesting data on health care and clinical outcomes [5], and emergence of accreditation organizations and report cards [6] make accurate, readily available clinical data on various aspects of medical care a necessity. Researchers are probably fortunate that the decision of many managed care organizations to make large investments in clinical information systems was driven by factors other than an interest in research. Most research projects are self-limited, with budgets that are designed to cover the cost of time-limited primary data collection. By contrast, medical records require ongoing, detailed data on entire populations. Quality-assurance activities also require that data collection continue over time. Therefore, researchers are in the position of being end users of databases that are often larger, more thorough, and more durable than any that would have been designed for research programs. Once these databases are developed, they become available for numerous studies of health care services and epidemiology. From the perspective of researchers studying health care services, the potential exists for capturing the complete health care experience of large, representative populations (including health care utilization patterns, costs of care, and clinical outcomes) in readily retrievable databases. However, most of these databases continue to lack sufficient data on comorbid conditions and disease severity to allow comparisons between populations or to adjust for subtle differences between clinical subgroups. The epidemiologist is similarly drawn to large, defined populations with accurate information on follow-up, clinical events, and to some extent, exposure patterns, risk factors, and other predictors. Again, a concern arises about the adequacy of information on important exposure patterns and covariates in computerized databases. When additional data are needed, two features of managed health care settings can be advantageous. Older, integrated managed care organizations can access complete paper medical records dating back 20 or more years. These paper records provide previously recorded information on exposure patterns (such as records on prescription drugs or screening tests) and many risk factors (for example, data on blood pressure levels or comorbid conditions) and therefore avoid the risk for recall biases. When medical records are inadequate, most managed care organizations have the ability to conduct mail or telephone surveys of beneficiaries to assess exposure patterns, behavioral risk factors, disease severity, functional status, and quality of life. Currently Available Data: Description and Limitations To optimally conduct research or quality-assurance activities, information on beneficiaries needs to be converted into a vast cohort study in which data on membership, demographic characteristics, clinical information utilization, and cost accumulate and are stored over time. These data elements are stored in distinct databases that have been developed at different times and often by different departments. Some important data sets, such as cancer registries or state files on birth and death certificates, are developed and maintained outside managed care organizations. Linkage across databases and over time is therefore needed to fully characterize individual beneficiaries and patient groups. The descriptions that follow reflect automated data presently available at the Division of Research, Kaiser Permanente, Northern California. Databases with similar characteristics are found in many integrated managed care organizations throughout the United States, although compatibility across organizations is minimal. Membership Data Accurate information on dates that members join a health care plan and terminate coverage together with demographic information are essential for characterizing and following patient cohorts. Because patients can leave an HMO at any point, most studies on utilization, cost, clinical outcomes, and epidemiology require construction of person-time denominators from membership data. Departures from a health care plan are as likely to reflect changes in employer or a change in the current employer's choice of health care insurance as they are an employee's decision to change plans. Nevertheless, observation ends when this departure occurs. The data that members join a health care plan also serves as a useful risk adjuster in that recent beneficiaries may be a particularly healthy subgroup, whereas any selection effects tend to decrease over time [7]. Important data elements that are still missing in many databases on HMO membership are race or ethnic group and socioeconomic status. Although these variables can be critical for understanding differences in clinical outcomes or utilization patterns, health care plans have until recently been reluctant to request such information. For research purposes, HMO-based research projects have used geocoding (which links individual addresses to U.S. census data on racial or socioeconomic characteristics of a beneficiary's census tract or block group) to obtain proxy measurements of these variables [8-11]. Data on Utilization and Costs Utilization databases include counts and descriptions of units of service. These databases are generally archived versions of administrative systems used in the provision that contain information on the delivery of health care services (for example, outpatient appointments, registration, pharmaceutical agents, and diagnostic testing) and the services and costs involved in health care referrals and claims for out-of-plan care. Because they must support clinical activities, these systems tend to be highly accurate. When databases on utilization and accounting are linked, costs can be calculated for a unit of health care service and across categories of service at the patient, provider, or medical facility level. Software that can effect these linkages is operational at many HMOs and has been used to report the costs of illnesses [12, 13] and to add an empirical cost-effectiveness component in randomized and observational studies [14]. The assignment of costs is an assumption-laden process that often results in dubious values for specific units of service. Nevertheless, within a single HMO, costs can serve as a useful common denominator for comparing total utilization patterns between selected groups of members. Clinical Data Information on both inpatient and outpatient diagnoses, pharmaceutical records, laboratory results, and results of diagnostic tests are among the data elements commonly available in automated databases. Inpatient diagnoses have generally been available for years as part of state- and Medicare-man-dated reporting systems. Databases on outpatient diagnoses are not yet universally available in prepaid systems of health care, are generally newer than databases on hospitalization, and often have not been extensively validated. Clinical data on out-patient diagnoses depend on the willingness of health care providers to record accurate information and are subject to inaccuracies if providers believe that their income or work load may be affected by what they record. In general, these records do not capture the reason (or reasons) for a visit with sufficient accuracy to attribute the visit or its costs to a single diagnosis. Nevertheless, when these records are present, researchers are able to identify large cohorts of patients with specific diseases. Measures of comorbid conditions or case mix have been developed on the basis of both outpatient diagnoses [15, 16] and outpatient use of prescription drugs [17]. These measurements have been shown to predict subsequent utilization at least as effectively as self-reported health status [18] and can be readily calculated for entire populations from automated data. Each system captures comorbid conditions and, to a limited extent, disease severity, which is shown by the ability of the systems to predict mortality as well as utilization patterns [19]. Disease Registri