Recruiting

Machine Learning Risk

Sponsor:

UCLA

Code:

NCT06995378

Conditions

Prediabetes

Health Communication

Patient Comprehension

Eligibility Criteria

Sex: All

Age: 65+

Healthy Volunteers: Not accepted

Interventions

Hemoglobin A1c Lab Result Communication Tool

Study Details

Brief summary:

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.

Conditions

Prediabetes

Health Communication

Patient Comprehension

Study ID

NCT06995378

Start date

May 27, 2026

Status verified date

Apr, 2026

Completion date

Sep, 2029

Anticipated

Primary completion date

Nov, 2026

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 65+

Healthy Volunteers: Not accepted

Inclusion Criteria:

  • Age 65 years or older
  • Hemoglobin A1c in the prediabetes range (5.7- but not including 6.0%)

Exclusion Criteria:

  • Have lab results outside the defined inclusion range
  • No UCLA primary care provider
  • Age <65 years
  • Eligibility for Surveys:

All randomized participants are eligible to receive study surveys. No additional eligibility criteria apply for survey participation.

HgbA1c of 6.0 or above is not eligible.

Study Design

Enrollment

1200 participants

Anticipated

Allocation

Randomized

Intervention Model

Parallel Assignment

Primary purpose

Health Services Research

Interventions and Outcome Measures

Arms

experimental: Personalized Lab Result Messaging

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.

no intervention: Standard Lab Result Messaging

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.

Interventions

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.

Primary outcome measure

  • Prediabetes- Related Healthcare Utilization [ Time Frame: 365 days after result ]

Central Contacts and Locations

Central contacts

Katelyn Nguyen Assistant Clinical Research Coordinator

310-267-5250katenguyen@mednet.ucla.edu

Locations

UCLA Health System

Recruiting

Los Angeles, California, United States, 90049

Contacts

Katelyn Assistant Clinical Research Coordinator

310-267-5250csarkisian@mednet.ucla.edu

Catherine Sarkisian, MD

3102068272csarkisian@mednet.ucla.edu

Principal Investigator:

Catherine Sarkisian, MD

More Information

Sponsor

University of California, Los Angeles

Last update posted

Jul 9, 2026

Last verified

Apr, 2026

Keywords

  • Prediabetes
  • Machine Learning
  • Risk Stratification
  • Electronic Health Record
  • Lab Result Communication
  • Predictive Modeling
  • Patient Comprehension

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-09. This information was provided to ClinicalTrials.gov by University of California, Los Angeles on 2026-07-09.