Transforming Population-Based Depression Care: a Quality Improvement Initiative Using Remote, Centralized Care Management.

Abstract

With the growing prevalence of value-based contracts, health systems are incentivized to consider population approaches to service delivery, particularly for chronic conditions like depression. To this end, UW Medicine implemented the Depression-Population Approach to Health (PATH) program in primary care (PC) as part of a system-wide Center for Medicare and Medicaid Innovation (CMMI) quality improvement (QI) initiative. To examine the feasibility of a pilot PATH program and its impact on clinical and process-of-care outcomes. A large, diverse, geographically disparate academic health system in Western Washington State including 28 PC clinics across five networks. The PATH program was a population-level, centralized, measurement-based care intervention that utilized a clinician to provide remote monitoring of treatment progress via chart review and facilitate patient engagement when appropriate. The primary goals of the program were to improve care engagement and increase follow-up PHQ-9 assessments for patients with depression and elevated initial PHQ-9 scores. We employed a prospective, observational study design, including commercially insured adult patients with new depression diagnoses and elevated initial PHQ-9 scores. The pilot intervention group, consisting of accountable care network (ACN) self-enrollees (N = 262), was compared with a similar commercially insured cohort (N = 2527) using difference-in-differences analyses adjusted for patient comorbidities, initial PHQ-9 score, and time trends. The PATH program was associated with three times the odds of PHQ-9 follow-up (OR 3.28, 95% CI 1.79-5.99), twice the odds of a follow-up PC clinic visit (OR 1.74, 95% CI 0.99-3.08), and twice the odds of treatment response, defined as reduction in PHQ-9 score by ≥ 50% (OR 2.02, 95% CI 0.97-4.21). Our results demonstrate that a centralized, remote care management initiative is both feasible and effective for large academic health systems aiming to improve depression outcome ascertainment, treatment engagement, and clinical care.