Diabetes-Depression Care-management Adoption Trial

Summary

The specific aims of the proposed study are to:

1. 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;
2. Conduct the quasi-experiment in eight Los Angeles County Department of Health Services (LAC-DHS) clinics to test the interventions;
3. 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
4. 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:

1. 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).
2. 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.
3. 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.
4. 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.
5. Of the three groups compared, the TC group will be the most cost-effective approach for accelerating adoption of the CER depression care results.

Conditions

• Depression
• Diabetes Mellitus

Interventions

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)..

Sponsors & Collaborators

• Department of Health and Human Services

  collaborator FED

• University of Southern California

  lead OTHER

Principal Investigators

• Shinyi Wu, PhD · University of Southern California

Study Design

Allocation

NON_RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2010-06-30

Primary Completion

2013-09-30

Completion

2013-09-30

Countries

• United States

Study Locations