[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100592588":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":18,"centralContacts":25,"locations":31,"responsibleParty":54,"collaborators":57,"id":61,"slug":62,"hasResults":63,"nctId":64,"briefTitle":65,"officialTitle":66,"acronym":18,"eligibilityCriteria":67,"healthyVolunteers":63,"sex":68,"minAge":69,"maxAge":18,"enrollmentInfo":70,"targetDuration":18,"studyType":73,"phases":74,"briefSummary":76,"conditions":77,"keywords":81,"overallStatus":87,"whyStopped":18,"lastUpdateSubmitDate":88,"lastUpdatePostDateStruct":89,"startDateStruct":92,"completionDateStruct":94,"leadSponsor":96,"locationsCount":97},{"fullName":5,"class":6},"University of California, Los Angeles","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Personalized Lab Result Messaging","EXPERIMENTAL","Participants receive modified electronic health record (EHR) lab result communications in the patient portal (MyChart) and provider-facing EHR interface that include a qualitative \"very low risk\" label generated by a machine learning-based tool, along with brief explanatory text providing context about their current results and indicating a low level of concern at this time.",[13],"Device: Hemoglobin A1c Lab Result Communication Tool",{"label":15,"type":16,"description":17,"interventionNames":18},"Standard Lab Result Messaging","NO_INTERVENTION","Participants receive standard electronic health record (EHR) lab result communications without any machine learning-generated risk labeling or explanatory text providing additional context about level of concern.",null,[20],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":18},"DEVICE","Hemoglobin A1c Lab Result Communication Tool","A behavioral intervention delivered through a personalized Electronic Health Record (EHR)-integrated lab result communication tool designed to improve emotional and cognitive responses to lab results among adults aged 65+. The tool applies behavioral science principles such as risk personalization, simplified messaging, and visual framing to reduce patient anxiety, enhance understanding, and support informed decision-making.",[9],[26],{"name":27,"role":28,"phone":29,"phoneExt":18,"email":30},"Katelyn Nguyen Assistant Clinical Research Coordinator","CONTACT","310-267-5250","katenguyen@mednet.ucla.edu",[32],{"facility":33,"status":18,"city":34,"state":35,"zip":36,"country":37,"countryCode":38,"cosmosGeoPoint":39,"geoPoint":44,"contacts":45},"UCLA Health System","Los Angeles","California","90049","United States","US",{"type":40,"coordinates":41},"Point",[42,43],-118.24368,34.05223,{"lat":43,"lon":42},[46,49,52],{"name":47,"role":28,"phone":29,"phoneExt":18,"email":48},"Katelyn Assistant Clinical Research Coordinator","csarkisian@mednet.ucla.edu",{"name":50,"role":28,"phone":51,"phoneExt":18,"email":48},"Catherine Sarkisian, MD","3102068272",{"name":50,"role":53,"phone":18,"phoneExt":18,"email":18},"PRINCIPAL_INVESTIGATOR",{"type":53,"investigatorFullName":55,"investigatorTitle":56,"investigatorAffiliation":5,"oldNameTitle":18,"oldOrganization":18},"Catherine A. Sarkisian","Professor of Medicine",[58],{"name":59,"class":60},"National Institute on Aging (NIA)","NIH","100592588","study-ehr-risk-stratification-tools-100592588",false,"NCT06995378","Study EHR Risk Stratification Tools","Evaluation of Patient and Provider Facing EHR-embedded Risk Stratification Tools","Inclusion Criteria:\n\n* Age 65 years or older\n* Most recent hemoglobin A1c in the prediabetes range (5.7-6.0%)\n\nExclusion Criteria:\n\n* Have lab results outside the defined inclusion range\n* No UCLA primary care provider\n* Age \\\u003C65 years\n* Eligibility for Surveys:\n\nAll randomized participants are eligible to receive study surveys. No additional eligibility criteria apply for survey participation.","ALL","65 Years",{"count":71,"type":72},1200,"ESTIMATED","INTERVENTIONAL",[75],"NA","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.\n\nEligible 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.\n\nAll 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.",[78,79,80],"Prediabetes","Health Communication","Patient Comprehension",[78,82,83,84,85,86,80],"Machine Learning","Risk Stratification","Electronic Health Record","Lab Result Communication","Predictive Modeling","NOT_YET_RECRUITING","2026-05-06",{"date":90,"type":91},"2026-05-08","ACTUAL",{"date":93,"type":72},"2026-04",{"date":95,"type":72},"2029-09",{"name":5,"class":6},1]