Diabetes-Depression Care-management Adoption Trial (DCAT)
The specific aims of the proposed study are to:
- Develop the innovative depression care management technology, including the speech recognition technology for automated monitoring and patient prompts over time, automatic integration of the responses into the patient registry, and evidence-based decision-support algorithms for care actions;
- Conduct the quasi-experiment in eight Los Angeles County Department of Health Services (LAC-DHS) clinics to test the interventions;
- Use mixed-method evaluation to assess the extent of the implementation of the interventions, the acceptance to the providers and to the patients, and the impact on adoption of depression screening and treatment management over time, utilization, and cost of healthcare services, and patient health outcomes; and
- Conduct a cost-effectiveness analysis of the three study arms. Successful completion of the study will demonstrate which Comparative Effectiveness Research (CER) adoption strategies are successful and why, their comparative cost-effectiveness, as well as which strategies are successful under which circumstances to inform system-wide implementation of same.
Hypotheses of the Proposed Study
The following are the main hypotheses of the study:
There will be statistically significant difference in the adoption of depression care screening and management over time among the three study groups.
1.1. The adoption rate will be Technology-supported care (TC) > Supported Care (SC) > Usual Care (UC).
There will be statistically significant difference in the depression symptom reduction, and better functional status, and quality of life among the three study groups.
2.1. The difference between the TC and the SC will not be statistically significant, but both will be greater than the UC group.
There will be statistically significant difference in the diabetes care process and outcomes among the three study groups.
3.1. The difference between the TC and the SC will not be statistically significant, but both will be greater than the UC group.
- There will also be statistically significant differences in healthcare utilization among the three study groups, with least utilization in the TC group where the greatest level of technology is applied.
- Of the three groups compared, the TC group will be the most cost-effective approach for accelerating adoption of the CER depression care results.
|Study Design:||Allocation: Non-Randomized
Endpoint Classification: Efficacy Study
Intervention Model: Parallel Assignment
Masking: Open Label
Primary Purpose: Screening
|Official Title:||Care Management Technology to Facilitate Depression Care in Safety Net Diabetes Clinics|
- Change from baseline in depression outcome at 6-months [ Time Frame: 24-months ] [ Designated as safety issue: No ]Depression is measured using depression scales Patient Health Questionnaire (PHQ)-9. Major depression is classified as PHQ-9>=10.
- Diabetes outcome [ Time Frame: 24 months ] [ Designated as safety issue: No ]Measured using Whitty-9 diabetes symptoms, presence of diabetes complications, number of diabetes complications, Toolbert diabetes self-care, and BMI.
- Health and functional status [ Time Frame: 24 months ] [ Designated as safety issue: No ]Measured using self-rated health, self-rated comorbid illnesses, and Sheehan disability scale
- Patient satisfaction with care [ Time Frame: 24 months ] [ Designated as safety issue: No ]Satisfaction with care available for diabetes and depression
|Study Start Date:||June 2010|
|Estimated Study Completion Date:||September 2013|
|Estimated Primary Completion Date:||September 2013 (Final data collection date for primary outcome measure)|
Experimental: Technology-supported care
This arm consists of Clinic Resource Management (CRM) clinics and serves as our intervention arm where the tested technology is implemented. Our overarching aim in these comparisons is to assess the potential effects of technology-facilitated depression symptom monitoring, relapse prevention, and medication adjustments and to examine depression care receipt and symptom improvement, patient/provider acceptance, and cost.
Other: Technology-supported care
The depression care-management technology that will interact with patients is the Automated Speech Recognition (ASR) for remote monitoring data collection. The ASR will use automated telephone calls to reach out to patients to repeat depression screening using PHQ-9, triggered either by calendar date or upcoming appointments, and to remind patients of their appointments in pre-determined time. In addition, the ASR will apply a structured script to conduct automatic follow-up with patients regarding their depression treatment adherence and side effects in order to provide data to help primary medical providers promptly and optimally adapt treatment. The ASR script will also include structured relapse prevention prompts. For providers and administrators, the depression care-management technology aimed to improve their workflow regarding depression care is Enhanced Disease Registry (EDR)..
No Intervention: Supported-Care
This arm consists of CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study.
No Intervention: Usual Care
This arm consists of non-CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study.
In addition, the study will aim to answer the secondary research questions listed below:
- What is medical provider satisfaction with the technology used in the TC (Technology Care) group?
- What is patient acceptance with the technology used in the TC group?
- What factors are identified by medical providers and clinic administrators as related to satisfaction, barriers, and sustaining the intervention post-trial?
- What are patients' reported satisfaction and facilitating factors and barriers to receipt and acceptance of depression care?
|United States, California|
|El Monte Comprehensive Health Center|
|El Monte, California, United States, 91731|
|High Desert Comprehensive Health Center|
|Lancaster, California, United States, 93536|
|Long Beach Comprehensive Health Center|
|Long Beach, California, United States, 90813|
|H. Claude Hudson Comprehensive Health Center|
|Los Angeles, California, United States, 90007|
|Roybal Comprehensive Health Center|
|Los Angeles, California, United States, 90022|
|Olive View-UCLA Medical Center Diabetes Clinic|
|Sylmar, California, United States, 91342|
|Mid-Valley Comprehensive Health Center|
|Van Nuys, California, United States, 91405|
|Harbor Comprehensive Health Center|
|Wilmington, California, United States, 90744|
|Principal Investigator:||Shinyi Wu, PhD||University of Southern California|