Publications by authors named "Lauren Patty Daskivich"
Article Synopsis
- Developed a web-based tool called DRRisk for assessing diabetic retinopathy (DR) risk using machine learning and electronic health records, aimed at preventing vision loss in underserved populations.
- Utilizes Python's Flask for the backend and HTML, CSS, and JavaScript for the user interface, with input from clinical experts on its design.
- Categorizes DR risk into low, moderate, or high levels to help healthcare providers prioritize high-risk patients and identify unscreened individuals with potential undiagnosed diabetic retinopathy.
Article Synopsis
- Clinical guidelines advise annual eye exams for diabetic patients to catch diabetic retinopathy (DR), but timely detection is challenging in underserved areas in the U.S.
- Researchers analyzed data from over 40,000 diabetic patients to evaluate various machine learning models for identifying undiagnosed DR, finding that a deep neural network showed the best results.
- The study concludes that machine learning can assist healthcare providers in safety-net settings by identifying diabetic patients who haven't been screened for DR, potentially improving early detection and treatment.
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.
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.
View Article and Find Full Text PDFFostering a Commitment to Quality: Best Practices in Safety-net Hospitals.
J Health Care Poor Underserved
April 2018
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.
View Article and Find Full Text PDFObjective: Teleretinal screening with nonmydriatic cameras has been presented as a means of increasing the number of patients assessed for diabetic retinopathy in urban safety net clinics. It has been hypothesized that automated nonmydriatic cameras may improve screening rates by reducing the learning curve for camera use. In this article, we examine the impact of introducing automated nonmydriatic cameras to urban safety net clinics whose photographers had previously used manual cameras.
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