This project will leverage health information technology to collect depression severity and medication tolerability data from patients and integrate with electronic health record (EHR) data into automated patient- centered feedback to clinicians. The ultimate goal is to improve patient-centered depression care, medication adherence, and depression outcomes in a variety of primary care settings. Primary care physicians (PCPs) prescribe more than half of all antidepressant medications, yet rates of medication adherence and treatment success are low in primary care settings. Depression care management programs involving the use of case managers to collect patient-reported outcomes and provide detailed feedback to PCPs were found to be effective in improving medication adherence and depression symptoms for patients in a small number of integrated primary care practices within one organization. Major drawbacks to these programs include the time required on behalf of clinic nurses and other staff and the monetary burdens on the practice. Our system of electronically collecting data from patients and providing automated and actionable feedback to the clinician has the potential to decrease the burdens on the practice and lead to an increase in effective depression treatment compared to care as usual. The automated system will also be scalable to be implemented in a wide variety of practice settings. In this project, a large, nationl network of primary care practices (eNQUIRENet) will be leveraged to develop and test an automated system to electronically collect data from patients taking an antidepressant to treat depression. The patient-reported outcomes will be collected from patients in-between clinic visits and will include measures of medication-related side effect tolerability and depression severity. Data will be collected from patients at multiple time points over a six-month period and will be electronically integrated into an existing Patient Recommendation Report provided to clinicians at each clinical encounter. Automated ad hoc updates will also be provided to clinicians between clinic visits based on algorithms driven by patient indications of side effect tolerability and depression severity. The data collection and enhanced feedback system will be pilot tested in eight eNQUIRENet practices. At the end of the pilot test, samples of clinicians and participating patients from the eight eNQUIRENet practices will be interviewed in order to assess the content, timing, implementation, utility, and associated resource burden of the data collection and enhanced feedback system.
The specific aims i ncluded in this application directly respond to research areas of interest indicated by AHRQ in PAR-08-269, specifically """"""""Health IT to support patient-centered care,"""""""" and """"""""Health IT to improve health care decision making through the use of integrated data and knowledge management.""""""""

Public Health Relevance

Depression is a major public health concern, affecting approximately 16% of adults in the U.S. at some point in their lifetime. While antidepressants can be highly effective when used correctly, many patients do not use antidepressants long enough to see a change in their depression, often because of medication-related side effects or a lack of response to the medication. An automated and scalable system of collecting data regarding depression severity and side effect tolerability directly from patients between visits and providing the information to the clinician for clinical decision support at the point-of-care hs the potential to increase adherence to and appropriate use of antidepressants. In turn, more people suffering from depression will find effectiveness from their antidepressant treatment.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Exploratory/Developmental Grants (R21)
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Health Care Technology and Decision Science (HTDS)
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White, Jon
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University of Colorado Denver
Other Health Professions
Schools of Pharmacy
United States
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