Depression impairs functional status and worsens clinical outcomes including morbidity, mortality, utilization, and cost. A major complication is the co-morbidity of depression in diabetic patients, who are twice as likely to experience clinically significant depressive symptoms as the general population. A significant public health challenge is that the translation of strong comparative effectiveness research (CER) results in routine depression care has been sub-optimal, especially for urban low-income patients in primary care practices. We propose an applied, quasi-experimental study to develop and test an innovative, scalable package of care management technology to facilitate the adoption of routine depression screening and appropriate treatment. The experimental design will target low-income, racially/ethnically diverse diabetes patients in six safety net clinics run by the Los Angeles County Department of Health Services (LAC-DHS) that serve over 1,500 adult diabetes patients (~30% with major depression) and will include three comparison arms-usual care (UC), supported care (SC) and technology-facilitated care (TC). A mixed-method evaluation will be conducted to assess the implementation of the interventions and their effects on adoption of depression screening and treatment management over time, utilization and cost of healthcare services, and patient health outcomes. Patients in the UC arm will receive no intervention except for baseline depression screening, physician notification, and patient depression education pamphlet. Patients in the SC arm group receive care as the LA-DHS clinical resource management program is currently structured, while those in the TC arm will be provided with state-of-the-art care management technology to facilitate better depression care. The technology will be an application and integration of an automated comprehensive appointment reminder system, speech recognition for remote monitoring of depressive symptoms and treatment effects, and an enhanced registry automating follow-up reminders, depression symptom alerts, and guideline-based depression treatment algorithms. This automated technology will offer an unprecedented approach to fill in gaps in current implementation of depression care at a low cost to facilitate optimal adaptive depression management in primary care that could improve healthcare outcomes at reduced utilization costs. The proposed interventions will accelerate adoption of CER results related to USPSTF screening recommendations for adults with depression, and the need to identify successful antidepressant choices. This work will build on an effective Multifaceted Depression and Diabetes Program developed and tested by the research team, which applies evidence-based practice guidelines and is responsive to known barriers to treatment among patients in safety net clinics. The results of this investigation will contribute to effective, transferable approaches of care delivery to accelerate the adoption and translation of CER evidence.
The comorbidity of depression and diabetes presents major public challenges. We will develop an innovative depression care management technology to improve the adoption of existing CER information and incorporate these findings into practice in a large urban public health system.
