10th ANNUAL UTAH HEALTH SERVICES RESEARCH CONFERENCE SAVE THE DATE & CALL FOR ABSTRACTS * Due January 16, 2015 ___________________________________________________________________________________ 2015 Meeting Theme: Considering Data Quality in Health Services Research Save the Date – Monday, March 16, 2015 – 9:00 am – 3:00 pm University of Utah Health Sciences Education Building __________________________________________________________________________________ Health services research is a multidisciplinary field of inquiry that examines the use, costs, accessibility, delivery, organization, financing, and outcomes of health care services to increase knowledge and understanding of the structure, processes, and effects of health services for individuals and populations. Abstracts reporting results of studies that address data quality using EHRs, ancillary systems (e.g., lab results), patient reported measures are encouraged. Abstract Title: Using A Systematic Quality-Based Approach to Integrate a Robust Depression Risk Module into an Existing Patient Activation Tool Preferred method of presentation: (check all that apply) Oral Presentation Poster Presentation Presenter: Jason Scott × × Email: [email protected] Org Affiliation: Purpose: Research Design: Degree(s): MPH, MPP Phone: 801-442-3834 Intermountain Healthcare, Institute for Health Care Delivery Research An effective patient engagement tool must provide accurate information to patients in a way that is easy to understand, motivational, and actionable. Intermountain Healthcare undertook the development of a depression-based risk factor module to be integrated into the IndiGO patient activation tool. This tool is designed to be used by providers and care teams based mainly in a primary care setting. This initiative followed an established approach engaging necessary stakeholders to ensure the resulting tool is robust and easy to use. A systematic approach was used to ensure high data quality and usefulness of the final module. First, patient and clinician interviews were conducted to determine which depression-related factors concern patients most and what potential interventions patients would be willing to adopt. Second, a provider-led team contributed clinical expertise to informed areas and interventions of interest. This process was undertaken to ensure the appropriate clinical factors were include in the analysis to ensure the resulting outcomes would be the most likely to engage patients. Third, necessary data elements were identified by the clinical and analytic teams and assessed for data accuracy and completeness. Finally, the provider-led team undertook a formal validation of the tool comparing modeled results to a sample of patient charts. Modeling was conducted by Archimedes model using logistic models with patient-level clinical data. Data consisted of patient demographics, Study Population/Sample: Data Sources: Method(s): Results: Implications for Policy and/or Practice: hospital and clinic visit information, depression diagnosis history and episode data, PHQ-9 survey scores, and depression related medication orders. The study consisted of patients identified within Intermountain Healthcare’s depression registry between 2000 and 2012. To ensure data accuracy, specific combinations of patient encounters were required to be included in the cohort. This consists of patients with depression indicated in the EMR problem list, an inpatient or ED visit with a diagnosis of depression, at least two office visits with a diagnosis of depression within 12 months, or patients with a clinic/ED visit paired with a prescription for antidepressants. The resulting population consisted of 353,674 patients. Interviews, focus groups, expert opinion, EDW, patient charts Mixed methods The patient activation tool interactively shows patients how depression treatment, exercise, and treatment persistence are likely to impact depression response, remission, and relapse. Suicidality was modeled, but eventually omitted because the team felt the included interventions did not show enough benefit to effectively engage patients. Interventions for Depression medication and psychotherapy were combined into a single intervention because there was insufficient data to accurately model the two components independently. Adjustments were made based on data availability and the effectiveness of specific factors to engage patients. In addition, a rigorous validation process encouraged confidence in the tool and accelerated adoption by providers. This information is currently available in 65 Intermountain clinics and is being run on 320,822 patients. Adopting a formal approach to operational initiatives helps ensure development of tools that are accurate, meaningful and effective. Data-based patient engagement tools require much more than data perspective and expertise to implement. Other key stakeholders including providers and patients are essential to understand what factors are most likely to inform effectiveness, usability, and impact. Systematically approaching the design, relevance, validation, and implementation of the tool is an important factor determining adoption to impact patient health and wellbeing. Analysis completed Status of Analyses: Please submit your structured abstract (limit 2 pages) no later than January 16, 2015 Submit via e-mail to: [email protected] Patient Centered Research Methods Core