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.

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.

Studying and Incorporating Efficiency into Gastrointestinal Endoscopy Centers.

Efficiency is defined as the use of resources in such a way as to maximize the production of goods and services. Improving efficiency has been the focus of management in many industries; however, it has not been until recently that incorporating efficiency models into healthcare has occurred. In particular, the study and development of improvement projects aimed at enhancing efficiency in GI have been growing rapidly in recent years. This focus on improving efficiency in GI has been spurred by the dramatic rise in the demand for endoscopic procedures as well as the rising number of insured patients requiring GI care coupled at the same time with limited resources in terms of staffing and space in endoscopy centers. This paper will critically review the history of efficiency in endoscopy centers, first by looking at other healthcare industries that have extensively studied and improved efficiency in their fields, examine a number of proposed efficiency metrics and benchmarks in endoscopy centers, and finally discuss opportunities where endoscopy centers could improve their efficiency.

Optimizing efficiency and operations at a California safety-net endoscopy center: a modeling and simulation approach.

BACKGROUND: Improvements in endoscopy center efficiency are needed, but scant data are available. OBJECTIVE: To identify opportunities to improve patient throughput while balancing resource use and patient wait times in a safety-net endoscopy center. SETTING: Safety-net endoscopy center. PATIENTS: Outpatients undergoing endoscopy. INTERVENTION: A time and motion study was performed and a discrete event simulation model constructed to evaluate multiple scenarios aimed at improving endoscopy center efficiency. MAIN OUTCOME MEASUREMENTS: Procedure volume and patient wait time. RESULTS: Data were collected on 278 patients. Time and motion study revealed that 53.8 procedures were performed per week, with patients spending 2.3 hours at the endoscopy center. By using discrete event simulation modeling, a number of proposed changes to the endoscopy center were assessed. Decreasing scheduled endoscopy appointment times from 60 to 45 minutes led to a 26.4% increase in the number of procedures performed per week, but also increased patient wait time. Increasing the number of endoscopists by 1 each half day resulted in increased procedure volume, but there was a concomitant increase in patient wait time and nurse utilization exceeding capacity. By combining several proposed scenarios together in the simulation model, the greatest improvement in performance metrics was created by moving patient endoscopy appointments from the afternoon to the morning. In this simulation at 45- and 40-minute appointment times, procedure volume increased by 30.5% and 52.0% and patient time spent in the endoscopy center decreased by 17.4% and 13.0%, respectively. The predictions of the simulation model were found to be accurate when compared with actual changes implemented in the endoscopy center. LIMITATIONS: Findings may not be generalizable to non-safety-net endoscopy centers. CONCLUSIONS: The combination of minor, cost-effective changes such as reducing appointment times, minimizing and standardizing recovery time, and making small increases in preprocedure ancillary staff maximized endoscopy center efficiency across a number of performance metrics.