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 Pythons Flask framework. Its user-interface is implemented using HTML, CSS and Javascript. Clinical experts were consulted on the tools 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 persons current DR risk. It may be useful for identifying unscreened diabetic patients who have undiagnosed DR.

Introduction: Timely diabetic retinopathy detection remains a problem in medically underserved settings in the US; diabetic patients in these locales have limited access to eye specialists. Teleretinal screening programs have been introduced to address this problem. Methods: Using data on ethnicity, gender, age, hemoglobin A1C, insulin dependence, time since last eye examination, subjective diabetes control, and years with diabetes from 27,116 diabetic patients participating in a Los Angeles County teleretinal screening program, we compared different machine learning methods for predicting retinopathy. The dataset exhibited a class imbalance. Results: Six classifiers learned on the data were predictive of retinopathy. The best model had an AUC of 0.754, sensitivity of 58% and specificity of 80%. Discussion: Successfully detecting retinopathy from diabetic patients' routinely collected clinical data could help clinicians in medically underserved areas identify unscreened diabetic patients who are at risk of developing retinopathy. This work is a step towards that goal.

Greater transparency in salaries overall and in factors associated with differing salaries can help students and professionals plan their careers, discover biases and obstacles, and help advance professional disciplines broadly. In March 2018, we conducted the first salary survey of American Medical Informatics Association members. Our goal was to summarize salary information and provide a nuanced view pertaining to the diverse biomedical informatics community. To identify factors associated with higher salaries, we reviewed average salaries for different groups (physician status, academic status, and different leadership positions) by gender. We also fitted multiple linear regression models for all participants (N = 201) and for gender, physician- and academic-status subgroup. The mean (standard deviation) salary was $181,774 ($99,566). Men earned more than women on average, and especially among professionals from academic settings. More years working in informatics and full-time employment were two factors that were consistently associated with higher salary.

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.