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Wu S, Ell K, Jin H, Vidyanti I, Chou CP, Lee PJ, Gross-Schulman S, Sklaroff LM, Belson D, Nezu AM, Hay J, Wang CJ, Scheib G, Di Capua P, Hawkins C, Liu P, Ramirez M, Wu BW, Richman M, Myers C, Agustines D, Dasher R, Kopelowicz A, Allevato J, Roybal M, Ipp E, Haider U, Graham S, Mahabadi V, Guterman J. Comparative Effectiveness of a Technology-Facilitated Depression Care Management Model in Safety-Net Primary Care Patients With Type 2 Diabetes: 6-Month Outcomes of a Large Clinical Trial. J Med Internet Res 2018; 20:e147. [PMID: 29685872 PMCID: PMC5938593 DOI: 10.2196/jmir.7692] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 12/10/2017] [Accepted: 01/13/2018] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Comorbid depression is a significant challenge for safety-net primary care systems. Team-based collaborative depression care is effective, but complex system factors in safety-net organizations impede adoption and result in persistent disparities in outcomes. Diabetes-Depression Care-management Adoption Trial (DCAT) evaluated whether depression care could be significantly improved by harnessing information and communication technologies to automate routine screening and monitoring of patient symptoms and treatment adherence and allow timely communication with providers. OBJECTIVE The aim of this study was to compare 6-month outcomes of a technology-facilitated care model with a usual care model and a supported care model that involved team-based collaborative depression care for safety-net primary care adult patients with type 2 diabetes. METHODS DCAT is a translational study in collaboration with Los Angeles County Department of Health Services, the second largest safety-net care system in the United States. A comparative effectiveness study with quasi-experimental design was conducted in three groups of adult patients with type 2 diabetes to compare three delivery models: usual care, supported care, and technology-facilitated care. Six-month outcomes included depression and diabetes care measures and patient-reported outcomes. Comparative treatment effects were estimated by linear or logistic regression models that used generalized propensity scores to adjust for sampling bias inherent in the nonrandomized design. RESULTS DCAT enrolled 1406 patients (484 in usual care, 480 in supported care, and 442 in technology-facilitated care), most of whom were Hispanic or Latino and female. Compared with usual care, both the supported care and technology-facilitated care groups were associated with significant reduction in depressive symptoms measured by scores on the 9-item Patient Health Questionnaire (least squares estimate, LSE: usual care=6.35, supported care=5.05, technology-facilitated care=5.16; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.02); decreased prevalence of major depression (odds ratio, OR: supported care vs usual care=0.45, technology-facilitated care vs usual care=0.33; P value: supported care vs usual care=.02, technology-facilitated care vs usual care=.007); and reduced functional disability as measured by Sheehan Disability Scale scores (LSE: usual care=3.21, supported care=2.61, technology-facilitated care=2.59; P value: supported care vs usual care=.04, technology-facilitated care vs usual care=.03). Technology-facilitated care was significantly associated with depression remission (technology-facilitated care vs usual care: OR=2.98, P=.04); increased satisfaction with care for emotional problems among depressed patients (LSE: usual care=3.20, technology-facilitated care=3.70; P=.05); reduced total cholesterol level (LSE: usual care=176.40, technology-facilitated care=160.46; P=.01); improved satisfaction with diabetes care (LSE: usual care=4.01, technology-facilitated care=4.20; P=.05); and increased odds of taking an glycated hemoglobin test (technology-facilitated care vs usual care: OR=3.40, P<.001). CONCLUSIONS Both the technology-facilitated care and supported care delivery models showed potential to improve 6-month depression and functional disability outcomes. The technology-facilitated care model has a greater likelihood to improve depression remission, patient satisfaction, and diabetes care quality.
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Affiliation(s)
- Shinyi Wu
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.,Roybal Institute on Aging, University of Southern California, Los Angeles, CA, United States.,Daniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.,Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA, United States
| | - Kathleen Ell
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States
| | - Haomiao Jin
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.,Roybal Institute on Aging, University of Southern California, Los Angeles, CA, United States.,Daniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Irene Vidyanti
- Daniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.,Policy Analysis Unit, Los Angeles County Department of Public Health, Los Angeles, CA, United States
| | - Chih-Ping Chou
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Pey-Jiuan Lee
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States
| | | | - Laura Myerchin Sklaroff
- Los Angeles County Department of Health Services, Los Angeles, CA, United States.,College of Social and Behavioral Sciences, California State University, Northridge, Los Angeles, CA, United States
| | - David Belson
- Daniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Arthur M Nezu
- Department of Psychology, Drexel University, Philadelphia, PA, United States
| | - Joel Hay
- Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA, United States
| | - Chien-Ju Wang
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Geoffrey Scheib
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Paul Di Capua
- Caremore Medical Group, East Haven, CT, United States.,Herbert Wertheim College of Medicine, Florida International University, Miami, FL, United States
| | - Caitlin Hawkins
- Daniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Pai Liu
- Daniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Magaly Ramirez
- Daniel J. Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.,Department of Health Policy and Management, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, United States
| | - Brian W Wu
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Mark Richman
- Department of Emergency Medicine, Northwell Health Long Island Jewish Medical Center, New Hyde Park, NY, United States
| | - Caitlin Myers
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Davin Agustines
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Robert Dasher
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Alex Kopelowicz
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Joseph Allevato
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Mike Roybal
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Eli Ipp
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States.,Harbor-UCLA Medical Center, University of California Los Angeles, Los Angeles, CA, United States.,Los Angeles Biomedical Research Institute, Los Angeles, CA, United States
| | - Uzma Haider
- Harbor-UCLA Medical Center, University of California Los Angeles, Los Angeles, CA, United States
| | - Sharon Graham
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Vahid Mahabadi
- Los Angeles County Department of Health Services, Los Angeles, CA, United States
| | - Jeffrey Guterman
- Los Angeles County Department of Health Services, Los Angeles, CA, United States.,David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
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Gross-Schulman S, Sklaroff LM, Hertz CC, Guterman JJ. Safety Evaluation of an Automated Remote Monitoring System for Heart Failure in an Urban, Indigent Population. Popul Health Manag 2017; 20:449-457. [PMID: 28486027 DOI: 10.1089/pop.2016.0186] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Heart Failure (HF) is the most expensive preventable condition, regardless of patient ethnicity, race, socioeconomic status, sex, and insurance status. Remote telemonitoring with timely outpatient care can significantly reduce avoidable HF hospitalizations. Human outreach, the traditional method used for remote monitoring, is effective but costly. Automated systems can potentially provide positive clinical, fiscal, and satisfaction outcomes in chronic disease monitoring. The authors implemented a telephonic HF automated remote monitoring system that utilizes deterministic decision tree logic to identify patients who are at risk of clinical decompensation. This safety study evaluated the degree of clinical concordance between the automated system and traditional human monitoring. This study focused on a broad underserved population and demonstrated a safe, reliable, and inexpensive method of monitoring patients with HF.
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Affiliation(s)
| | - Laura Myerchin Sklaroff
- 1 Los Angeles County Department of Health Services , Los Angeles, California.,2 College of Behavioral and Social Sciences, California State University , Northridge, California
| | | | - Jeffrey J Guterman
- 1 Los Angeles County Department of Health Services , Los Angeles, California.,4 David Geffen School of Medicine at UCLA , Los Angeles, California
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Ramirez M, Wu S, Jin H, Ell K, Gross-Schulman S, Myerchin Sklaroff L, Guterman J. Automated Remote Monitoring of Depression: Acceptance Among Low-Income Patients in Diabetes Disease Management. JMIR Ment Health 2016; 3:e6. [PMID: 26810139 PMCID: PMC4736285 DOI: 10.2196/mental.4823] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2015] [Revised: 09/11/2015] [Accepted: 09/22/2015] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Remote patient monitoring is increasingly integrated into health care delivery to expand access and increase effectiveness. Automation can add efficiency to remote monitoring, but patient acceptance of automated tools is critical for success. From 2010 to 2013, the Diabetes-Depression Care-management Adoption Trial (DCAT)-a quasi-experimental comparative effectiveness research trial aimed at accelerating the adoption of collaborative depression care in a safety-net health care system-tested a fully automated telephonic assessment (ATA) depression monitoring system serving low-income patients with diabetes. OBJECTIVE The aim of this study was to determine patient acceptance of ATA calls over time, and to identify factors predicting long-term patient acceptance of ATA calls. METHODS We conducted two analyses using data from the DCAT technology-facilitated care arm, in which for 12 months the ATA system periodically assessed depression symptoms, monitored treatment adherence, prompted self-care behaviors, and inquired about patients' needs for provider contact. Patients received assessments at 6, 12, and 18 months using Likert-scale measures of willingness to use ATA calls, preferred mode of reach, perceived ease of use, usefulness, nonintrusiveness, privacy/security, and long-term usefulness. For the first analysis (patient acceptance over time), we computed descriptive statistics of these measures. In the second analysis (predictive factors), we collapsed patients into two groups: those reporting "high" versus "low" willingness to use ATA calls. To compare them, we used independent t tests for continuous variables and Pearson chi-square tests for categorical variables. Next, we jointly entered independent factors found to be significantly associated with 18-month willingness to use ATA calls at the univariate level into a logistic regression model with backward selection to identify predictive factors. We performed a final logistic regression model with the identified significant predictive factors and reported the odds ratio estimates and 95% confidence intervals. RESULTS At 6 and 12 months, respectively, 89.6% (69/77) and 63.7% (49/77) of patients "agreed" or "strongly agreed" that they would be willing to use ATA calls in the future. At 18 months, 51.0% (64/125) of patients perceived ATA calls as useful and 59.7% (46/77) were willing to use the technology. Moreover, in the first 6 months, most patients reported that ATA calls felt private/secure (75.9%, 82/108) and were easy to use (86.2%, 94/109), useful (65.1%, 71/109), and nonintrusive (87.2%, 95/109). Perceived usefulness, however, decreased to 54.1% (59/109) in the second 6 months of the trial. Factors predicting willingness to use ATA calls at the 18-month follow-up were perceived privacy/security and long-term perceived usefulness of ATA calls. No patient characteristics were significant predictors of long-term acceptance. CONCLUSIONS In the short term, patients are generally accepting of ATA calls for depression monitoring, with ATA call design and the care management intervention being primary factors influencing patient acceptance. Acceptance over the long term requires that the system be perceived as private/secure, and that it be constantly useful for patients' needs of awareness of feelings, self-care reminders, and connectivity with health care providers. TRIAL REGISTRATION ClinicalTrials.gov NCT01781013; https://clinicaltrials.gov/ct2/show/NCT01781013 (Archived by WebCite at http://www.webcitation.org/6e7NGku56).
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Affiliation(s)
- Magaly Ramirez
- Daniel J Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, United States
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Wu S, Ell K, Gross-Schulman SG, Sklaroff LM, Katon WJ, Nezu AM, Lee PJ, Vidyanti I, Chou CP, Guterman JJ. Technology-facilitated depression care management among predominantly Latino diabetes patients within a public safety net care system: comparative effectiveness trial design. Contemp Clin Trials 2013; 37:342-54. [PMID: 24215775 DOI: 10.1016/j.cct.2013.11.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Revised: 10/29/2013] [Accepted: 11/01/2013] [Indexed: 01/15/2023]
Abstract
Health disparities in minority populations are well recognized. Hispanics and Latinos constitute the largest ethnic minority group in the United States; a significant proportion receives their care via a safety net. The prevalence of diabetes mellitus and comorbid depression is high among this group, but the uptake of evidence-based collaborative depression care management has been suboptimal. The study design and baseline characteristics of the enrolled sample in the Diabetes-Depression Care-management Adoption Trial (DCAT) establishes a quasi-experimental comparative effectiveness research clinical trial aimed at accelerating the adoption of collaborative depression care in safety net clinics. The study was conducted in collaboration with the Los Angeles County Department of Health Services at eight county-operated clinics. DCAT has enrolled 1406 low-income, predominantly Hispanic/Latino patients with diabetes to test a translational model of depression care management. This three-group study compares usual care with a collaborative care team support model and a technology-facilitated depression care model that provides automated telephonic depression screening and monitoring tailored to patient conditions and preferences. Call results are integrated into a diabetes disease management registry that delivers provider notifications, generates tasks, and issues critical alerts. All subjects receive comprehensive assessments at baseline, 6, 12, and 18 months by independent English-Spanish bilingual interviewers. Study outcomes include depression outcomes, treatment adherence, satisfaction, acceptance of assessment and monitoring technology, social and economic stress reduction, diabetes self-care management, health care utilization, and care management model cost and cost-effectiveness comparisons. DCAT's goal is to optimize depression screening, treatment, follow-up, outcomes, and cost savings to reduce health disparities.
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Affiliation(s)
- Shinyi Wu
- Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, United States; RAND Corporation, United States.
| | - Kathleen Ell
- School of Social Work, University of Southern California, United States.
| | | | | | - Wayne J Katon
- Department of Psychiatry and Behavioral Sciences, University of Washington, United States.
| | - Art M Nezu
- Drexel University College of Arts and Sciences, United States.
| | - Pey-Jiuan Lee
- School of Social Work, University of Southern California, United States.
| | - Irene Vidyanti
- Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, United States.
| | - Chih-Ping Chou
- Keck School of Medicine, Department of Preventive Medicine, University of Southern California, United States.
| | - Jeffrey J Guterman
- Los Angeles County Department of Health Services, United States; David Geffen School of Medicine at UCLA, United States.
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