A fidelity measure for integrated management of depression in primary care.
BACKGROUND: Integrated models of primary care depression management improve outcomes. Subsequent dissemination efforts and their evaluation need a fidelity measure. OBJECTIVES: We sought to develop and validate a fidelity measure using data gathered during routine clinical application of the clinical model. METHODS: Longitudinal outcome data on depression severity were obtained from 224 subjects experiencing major depression or dysthymia and assigned to a 3-component model (3CM) intervention. Data on 10 essential 3CM process-of-care components were obtained from telephone logs maintained by care managers administering 3CM care. Stakeholders (n = 23), including researchers, health care administrators, and care managers, independently rated the importance of the 10 elements distributing 100 points among the elements. Mean ratings were used as weights to construct a fidelity score. Predictive validity was assessed using logistic regression for patient response and remission at 3 and 6 months. RESULTS: 3CM fidelity was high, with a mean of 74.1 at 3 months and 75.9 at 6 months. Given a large gap in the scores' distribution, subjects were classified into zero, low-, and high-fidelity groups. Logistic regressions adjusting for baseline depression found a distinct continuum. Patients that were provided high fidelity 3CM were significantly more likely to achieve treatment response and remission at 3 months. At 6 months, high-fidelity care was again significantly more likely to produce a response, but remission rate did not differ from patients provided low fidelity. CONCLUSIONS: Most patients received a substantially implemented "3CM dose." Even within this high implementation, however, a higher fidelity score was associated with better outcomes. The easily applied measure is a promising tool for monitoring the quality of implementation of integrated care.
Oxman, TE; Schulberg, HC; Greenberg, RL; Dietrich, AJ; Williams, JW; Nutting, PA; Bruce, ML
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