Implementation of a pilot teleretinal screening protocol for hydroxychloroquine retinopathy in a Los Angeles County safety net clinic.

INTRODUCTION: This study aimed to determine whether teleretinal screening for hydroxychloroquine retinopathy (HCQR) improves clinical efficiency and adherence to recommended screening guidelines compared to face-to-face screening among patients in a large safety net medical system. METHODS: In this retrospective cohort study of a consecutive sample of 590 adult patients with active HCQ prescriptions seen in the outpatient ophthalmology clinic at Los Angeles County + University of Southern California Medical Center from 1 September 2018 to 25 November 2019, 203 patients underwent technician-only tele-HCQR screening (THRS), and 387 patients underwent screening with traditional face-to-face visits (F2FV) with an eye-care provider. Data on clinic efficiency measures (appointment wait time and encounter duration) and adherence to recommended screening guidelines were collected and compared between the two cohorts. RESULTS: Compared to F2FV, the THRS cohort experienced significantly shorter median (interquartile range) time to appointment (2.5 (1.5-4.6) vs. 5.1 (2.9-8.4) months; p < 0.0001), shorter median encounter duration (1 (0.8-1.4) vs. 3.7 (2.5-5.2) hours; p < 0.0001) and higher proportion of complete baseline screening (102/104 (98.1%) vs. 68/141 (48.2%); p < 0.001) and complete chronic screening (98/99 (99%) vs. 144/246 (58.5%); p < 0.001). DISCUSSION: A pilot THRS protocol was successfully implemented at a major safety net eye clinic in Los Angeles County, resulting in a 50.9% reduction in wait times for screening, 72.9% reduction in encounter duration and 49.9% and 40.5% increases in proportions of complete baseline and chronic screening, respectively. Tele-HCQ retinal screening protocols may improve timeliness to care and screening adherence for HCQR in the safety net setting.

DRRisk: A Web-based tool to Assess the Risk of Diabetic Retinopathy through Machine Learning on Electronic Health Records.

Objective: We developed a web-based tool for diabetic retinopathy (DR) risk assessment called DRRisk (https://drandml.cdrewu.edu/) using machine learning on electronic health record (EHR) data, with a goal of preventing vision loss in persons with diabetes, especially in underserved settings. Methods: DRRisk uses Python's Flask framework. Its user-interface is implemented using HTML, CSS and Javascript. Clinical experts were consulted on the tool's design. Results: DRRisk assesses current DR risk for people with diabetes, categorizing their risk level as low, moderate, or high, depending on the percentage of DR risk assigned by the underlying machine learning model. Discussion: A goal of our tool is to help providers prioritize patients at high risk for DR in order to prevent blindness. Conclusion: Our tool uses DR risk factors from EHR data to calculate a diabetic person's current DR risk. It may be useful for identifying unscreened diabetic patients who have undiagnosed DR.

Detecting diabetic retinopathy through machine learning on electronic health record data from an urban, safety net healthcare system.

OBJECTIVE: Clinical guidelines recommend annual eye examinations to detect diabetic retinopathy (DR) in patients with diabetes. However, timely DR detection remains a problem in medically underserved and under-resourced settings in the United States. Machine learning that identifies patients with latent/undiagnosed DR could help to address this problem. MATERIALS AND METHODS: Using electronic health record data from 40 631 unique diabetic patients seen at Los Angeles County Department of Health Services healthcare facilities between January 1, 2015 and December 31, 2017, we compared ten machine learning environments, including five classifier models, for assessing the presence or absence of DR. We also used data from a distinct set of 9300 diabetic patients seen between January 1, 2018 and December 31, 2018 as an external validation set. RESULTS: Following feature subset selection, the classifier with the best AUC on the external validation set was a deep neural network using majority class undersampling, with an AUC of 0.8, the sensitivity of 72.17%, and specificity of 74.2%. DISCUSSION: A deep neural network produced the best AUCs and sensitivity results on the test set and external validation set. Models are intended to be used to screen guideline noncompliant diabetic patients in an urban safety-net setting. CONCLUSION: Machine learning on diabetic patients' routinely collected clinical data could help clinicians in safety-net settings to identify and target unscreened diabetic patients who potentially have undiagnosed DR.

Fostering a Commitment to Quality: Best Practices in Safety-net Hospitals.

In 2007, the Martin Luther King, Jr.-Harbor Hospital (MLK-Harbor), which served a large safety-net population in South Los Angeles, closed due to quality challenges. Shortly thereafter, an agreement was made to establish a new hospital, Martin Luther King, Jr. Community Hospital (MLKCH), to serve the unmet needs of the community. To assist the newly appointed MLKCH Board of Directors in building a culture of quality, we conducted a series of interviews with five high-performing hospital systems. In this report, we describe our findings. The hospitals we interviewed achieved a culture of quality by: 1) developing guiding principles that foster quality; 2) hiring and retaining personnel who are stewards of quality; 3) promoting efficient resource utilization; 4) developing a well-organized quality improvement infrastructure; and 5) cultivating integrated, patient-centric care. The institutions highlighted in this report provide important lessons for MLKCH and other safety-net institutions.

Understanding the Knowledge Gap Experienced by U.S. Safety Net Patients in Teleretinal Screening.

Safety-net patients' socioeconomic barriers interact with limited digital and health literacies to produce a "knowledge gap" that impacts the delivery of healthcare via telehealth technologies. Six focus groups (2 African- American and 4 Latino) were conducted with patients who received teleretinal screening in a U.S. urban safety-net setting. Focus groups were analyzed using a modified grounded theory methodology. Findings indicate that patients' knowledge gap is primarily produced at three points during the delivery of care: (1) exacerbation of patients' pre-existing personal barriers in the clinical setting; (2) encounters with technology during screening; and (3) lack of follow up after the visit. This knowledge gap produces confusion, potentially limiting patients' perceptions of care and their ability to manage their own care. It may be ameliorated through delivery of patient education focused on both disease pathology and specific role of telehealth technologies in disease management.