RESUMEN
BACKGROUND: Interventions to support patients with complex needs have proliferated in recent years, but the question of how to identify patients with complex needs has received relatively little attention. There are innumerable ways to structure inclusion and exclusion criteria for complex care interventions, and little is known about the implications of choices made in designing patient selection criteria. OBJECTIVE: To provide insights into the design of patient selection criteria for interventions, by implementing criteria sets within a large health plan member population and comparing the characteristics of the resulting complex patient cohorts. DESIGN: Retrospective observational descriptive study. SUBJECTS: Patients identified as having complex needs, within the membership population of Kaiser Permanente Southern California, a large, population-based health plan with more than 4 million members. We characterize five commonly used archetypes of complex needs: high-cost patients, patients with multiple chronic conditions, frail elders, emergency department high-utilizers, and inpatient high-utilizers. MEASURES: We selected an initial set of criteria for identifying patients in each of the archetypical complex populations, based on available administrative data. We then tested multiple variants of each definition. We compared the resulting patient cohorts using univariate and bivariate descriptive statistics. KEY RESULTS: Overall, 32.7% of the 3,112,797 adults in our population-based sample were selected by at least one of the 25 definitions of complexity we tested. Across definitions the total number of patients identified as complex ranged from 622,560 to 1583 and complex patient cohorts varied widely in demographic characteristics, chronic conditions, healthcare utilization, spending, and survival. CONCLUSIONS: Choice of patient population is critical to the design of complex care programs. Exploratory analyses of population criteria can provide useful information for program planning in the setting of limited resources for interventions. Data such as these should be generated as a key step in program design.