Comprehension clinical trials at University of California Health

For adults ≥65 years and their providers, the investigators will test the usability and design of a tool to replace standard uniform reporting of lab results to patients and their providers with a new personalized Electronic Health Record (EHR) lab result communication tool that: 1) extracts patient-level data from the EHR; 2) calculates individual risk; and 3) for patients with very low risk, communicates the individualized risk information. The investigators will employ a range of User Experience (UX) research methods to understand how patient and provider users interact with the new lab result communication tool and to assess their comprehension of the lab results. This study will be conducted with both patient and provider participants. The patient participant portion of this study uses a four-arm, design to evaluate three newly designed laboratory result communication template reports compared with the current standard (control) communication. The provider participant portion of this study is non-randomized; all provider participants will review all four template reports. This will include live semi-structured interviews with the participants and review of the template report(s) of the Chronic Kidney Disease (CKD) lab result communication tool. An anonymous in-person template report feedback survey will be provided to the participants to gauge their understanding of the template report(s), clarity of the information presented, and overall satisfaction with the tool. This will be a single-visit interaction with the participant in the UCLA Health geriatric or general medicine patient waiting room.

This study evaluates whether adding machine learning-based risk information to electronic health record (EHR) lab result messages helps older adults better understand their risk of developing diabetes and influences their emotional responses, quality of life, and healthcare use. Eligible participants are adults aged 65 years and older with a UCLA primary care provider and a hemoglobin A1c level in the range (5.7-6.0%). Participants are identified automatically at the time their lab results are processed and are randomly assigned to receive either standard lab result messages or modified messages that include a "very low risk" label generated by a machine learning model. All participants who are randomized are invited to complete two surveys: one shortly after their lab result is posted in MyChart and a follow-up survey approximately 30 days later. The study also uses de-identified EHR data to examine patterns of healthcare utilization and progression to diabetes. Provider comments related to lab result messaging will be analyzed to explore differences in response patterns between the two groups.