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.

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.

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.