Assessing physicians' use of treatment algorithms: Project IMPACTS study design and rationale

Effective treatments for major depressive disorder have been available for 35 years, yet inadequate pharmacotherapy continues to be widespread leading to suboptimal outcomes. Evidence-based medication algorithms have the potential to bring much-needed improvement in effectiveness of antidepressant treatment in "real-world" clinical settings. Project IMPACTS (Implementation of Algorithms using Computerized Treatment Systems) addresses the critical question of how best to facilitate integration of depression treatment algorithms into routine care. It tests an algorithm implemented through a computerized decision support system using a measurement-based care approach for depression against a paper-and-pencil version of the same algorithm and non-algorithm-based, specialist-delivered usual care. This paper reviews issues related to the Project IMPACTS study rationale, design, and procedures. Patient outcomes include symptom severity, social and work function, and quality of life. The economic impact of treatment is assessed in terms of health care utilization and cost. Data collected on physician behavior include degree of adherence to guidelines and physician attitudes about the perceived utility, ease of use, and self-reported effect of the use of algorithms on workload. Novel features of the design include a two-tiered study enrollment procedure, which initially enroll physicians as subjects, and then following recruitment of physicians, enrollment of subjects takes place based initially on an independent assessment by study staff to determine study eligibility. The study utilizes brief, easy-to-use symptom severity measures that facilitate physician decision making, and it employs a validated, phone-based, follow-up assessment protocol in order to minimize missing data, a problem common in public sector and longitudinal mental health studies. IMPACTS will assess the success of algorithm implementation and subsequent physician adherence using study-developed criteria and related statistical approaches. These new procedures and data points will also allow a more refined assessment of algorithm-driven treatment in the future.