PrediabetesNCT06995378
Study EHR Risk Stratification Tools
A study testing Hemoglobin A1c Lab Result Communication Tool in people with Prediabetes and Health Communication, run by University of California, Los Angeles at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 65 years and older
- Conditions the study is for
- Prediabetes, Health Communication, Patient Comprehension
- What is being tested?
- Hemoglobin A1c Lab Result Communication Tool
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,200 participants
- Where
- 1 siteUnited States
- Study dates
- 27 May 2026to Sep 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 years and older
- All sexes
- No healthy volunteers
- Older adult
Who may be able to join
- Age 65 years or older
- Hemoglobin A1c in the prediabetes range (5.7- but not including 6.0%)
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Katelyn Nguyen Assistant Clinical Research Coordinator contact
United States1 site, 1 recruiting
- Recruiting
UCLA Health System
Los Angeles, California, 90049
Katelyn Assistant Clinical Research Coordinator, 310-267-5250, csarkisian@mednet.ucla.edu
Catherine Sarkisian, MD, 3102068272, csarkisian@mednet.ucla.edu
About this study
Official title: Evaluation of Patient and Provider Facing EHR-embedded Risk Stratification Tools
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.
Read the detailed description
Prediabetes thresholds based on hemoglobin A1c were originally developed using younger, healthier populations and may not reflect the slower and more variable glycemic changes observed in older adults. Evidence from large community-based cohorts suggests that adults aged 65 years and older with A1c values in the prediabetes range are often more likely to return to normal glycemia than to progress to diabetes, creating uncertainty for patients and providers when interpreting lab results.
Machine learning models developed using de-identified UCLA Health EHR data from multiple annual cohorts between 2020 and 2024 demonstrated strong performance in predicting progression to diabetes. The final model uses a CatBoost architecture and incorporates approximately 94 routinely collected clinical variables to generate patient-specific risk scores. Model performance was evaluated across yearly cohorts, and the selected model is locked for the duration of the study without updating or adapting to new data.
The study follows a real-world, randomized deployment design in which eligible individuals in the lowest 15% of model-predicted risk within the eligible study population are identified automatically at the time lab results are processed and assigned to either modified or standard lab result messaging. De-identified EHR data and free-text provider comments are used to examine healthcare utilization, disease progression, and provider response patterns over time.
All participants who are randomized are invited to complete two surveys. The first survey is administered shortly after receipt of the laboratory result and is designed to assess immediate patient understanding of the result and emotional responses such as anxiety or reassurance. A second survey is administered approximately one month later and uses validated instruments to measure health-related quality of life, food-related quality of life and eating behavior, and perceived burden of healthcare. Both study arms receive the same surveys, allowing comparison of patient-reported outcomes between standard and modified laboratory result messaging. Surveys are distributed only to participants who have been randomized to either modified or standard laboratory result messaging. Therefore, no additional eligibility criteria apply for survey participation beyond randomization.
By embedding model-generated risk information directly into routine EHR workflows, this study aims to generate evidence on whether precision-based communication can support more individualized, patient-centered care and inform future implementation across broader patient populations and clinical use cases.
- Conditions
- Prediabetes, Health Communication, Patient Comprehension
- Being tested
- Hemoglobin A1c Lab Result Communication Tool
- Keywords
- Prediabetes, Machine Learning, Risk Stratification, Electronic Health Record, Lab Result Communication, Predictive Modeling, Patient Comprehension
- Medical subject headings
- Prediabetic State
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Hemoglobin A1c Lab Result Communication ToolDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: Hemoglobin A1c Lab Result Communication Tool |
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediabetes- Related Healthcare Utilization
Total count of prediabetes-related healthcare utilization defined as the sum of outpatient visits to endocrinology, repeat hemoglobin A1c tests, and new prescriptions for diabetes-related medications following the index A1c result.
Measured: 365 days after result
Secondary outcomes (11)
Number of Repeat Hemoglobin A1c Tests
Total number of repeat hemoglobin A1c laboratory tests performed after the index test. This measure reflects follow-up glycemic testing and serves as an indicator of diabetes-related monitoring and healthcare utilization.
Measured: 365 days after result
Number of Prescriptions for Diabetes-Related Medications
Total number of prescriptions issued for medications commonly used for glycemic management (e.g., metformin) following the index hemoglobin A1c result. This outcome captures initiation of pharmacologic treatment related to diabetes risk.
Measured: 180 days after result
Total Number of Outpatient Healthcare
Total count of outpatient visits across all specialties following the index A1c result
Measured: 180 days after result
Numbers of Referrals to Endocrinology
Total number of outpatient referrals to an endocrinologist occurring after the index hemoglobin A1c laboratory result. This measure is used to quantify diabetes-related specialty care utilization potentially associated with interpretation of the laboratory result communication.
Measured: 14 days after initial result
Number of Referrals to Nutrition Services
Number of participants with an electronic referral order placed to clinical nutrition services documented in the electronic health record after release of the hemoglobin A1c result.
Measured: 14 days after initial result
Number of Referrals to Diabetes Education
Number of participants with an electronic referral order placed to diabetes education services documented in the electronic health record after release of the hemoglobin A1c result.
Measured: 14 days after initial result
Number of Completed Endocrinology Visit
Number of participants who complete an outpatient endocrinology encounter documented in the electronic health record after laboratory result notification. Visit completion will be identified using encounter records associated with endocrinology clinic services.
Measured: 180 days after result
Completed Nutrition Services Visit
Number of participants who complete an outpatient visit with clinical nutrition services documented in the electronic health record following laboratory result notification. Completion will be determined using encounter data associated with nutrition services.
Measured: 180 days after result
Completed Appointments to Diabetes Education
Number of participants who complete an outpatient visit with diabetes education services documented in the electronic health record following laboratory result notification. Completion will be determined using encounter data associated with diabetes education.
Measured: 180 days after result
Number of Patient MyChart Messages
Count of MyChart Test Result Messages
Measured: 7 days after viewing lab result
Numbers of Phone Calls Received after A1c Results
Count of telephone encounters to ordering provider
Measured: 7 days after viewing lab result
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 May 2025
- Study start27 May 2026
- Primary completionNov 2026 (estimated)
- Study completionSep 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PrediabetesStudies of the same condition, open and under way.
- More trials run by University of California, Los AngelesThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 1,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of California, Los Angeles
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute on Aging (NIA)
- Responsible party
- Principal investigator, Catherine A. Sarkisian, University of California, Los Angeles
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06995378
- Sponsor's protocol ID
- IRB-24-6052
- Status last verified
- Apr 2026
- First submitted
- 20 May 2025
- Last updated
- 9 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Patient and Provider Facing EHR-embedded Risk Stratification Tools
- ClinicalTrials.gov ID
- NCT06995378
- Acronym / sponsor ID
- IRB-24-6052
- Conditions
- Prediabetes, Health Communication, Patient Comprehension
- Keywords
- Prediabetes, Machine Learning, Risk Stratification, Electronic Health Record, Lab Result Communication, Predictive Modeling, Patient Comprehension
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,200 participants (estimated)
- Lead sponsor
- University of California, Los AngelesUniversity, hospital or other
- Collaborators
- National Institute on Aging (NIA)
- Study start
- 27 May 2026 (actual)
- Primary completion
- Nov 2026 (estimated)
- Study completion
- Sep 2029 (estimated)
- First posted
- 29 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Prediabetes thresholds based on hemoglobin A1c were originally developed using younger, healthier populations and may not reflect the slower and more variable glycemic changes observed in older adults. Evidence from large community-based cohorts suggests that adults aged 65 years and older with A1c values in the prediabetes range are often more likely to return to normal glycemia than to progress to diabetes, creating uncertainty for patients and providers when interpreting lab results.
Machine learning models developed using de-identified UCLA Health EHR data from multiple annual cohorts between 2020 and 2024 demonstrated strong performance in predicting progression to diabetes. The final model uses a CatBoost architecture and incorporates approximately 94 routinely collected clinical variables to generate patient-specific risk scores. Model performance was evaluated across yearly cohorts, and the selected model is locked for the duration of the study without updating or adapting to new data.
The study follows a real-world, randomized deployment design in which eligible individuals in the lowest 15% of model-predicted risk within the eligible study population are identified automatically at the time lab results are processed and assigned to either modified or standard lab result messaging. De-identified EHR data and free-text provider comments are used to examine healthcare utilization, disease progression, and provider response patterns over time.
All participants who are randomized are invited to complete two surveys. The first survey is administered shortly after receipt of the laboratory result and is designed to assess immediate patient understanding of the result and emotional responses such as anxiety or reassurance. A second survey is administered approximately one month later and uses validated instruments to measure health-related quality of life, food-related quality of life and eating behavior, and perceived burden of healthcare. Both study arms receive the same surveys, allowing comparison of patient-reported outcomes between standard and modified laboratory result messaging. Surveys are distributed only to participants who have been randomized to either modified or standard laboratory result messaging. Therefore, no additional eligibility criteria apply for survey participation beyond randomization.
By embedding model-generated risk information directly into routine EHR workflows, this study aims to generate evidence on whether precision-based communication can support more individualized, patient-centered care and inform future implementation across broader patient populations and clinical use cases.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Personalized Lab Result Messaging | Experimental | Device: Hemoglobin A1c Lab Result Communication ToolParticipants 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. |
| 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. |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
Key inclusion criteria (2 listed)
- Age 65 years or older
- Hemoglobin A1c in the prediabetes range (5.7- but not including 6.0%)
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Hemoglobin A1c Lab Result Communication ToolA 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. | Device | Personalized Lab Result Messaging |
Primary outcome
Prediabetes- Related Healthcare Utilization
Time frame: 365 days after result
Total count of prediabetes-related healthcare utilization defined as the sum of outpatient visits to endocrinology, repeat hemoglobin A1c tests, and new prescriptions for diabetes-related medications following the index A1c result.
11 secondary outcomes
Number of Repeat Hemoglobin A1c Tests
Time frame: 365 days after result
Number of Prescriptions for Diabetes-Related Medications
Time frame: 180 days after result
Total Number of Outpatient Healthcare
Time frame: 180 days after result
Numbers of Referrals to Endocrinology
Time frame: 14 days after initial result
Number of Referrals to Nutrition Services
Time frame: 14 days after initial result
Number of Referrals to Diabetes Education
Time frame: 14 days after initial result
Number of Completed Endocrinology Visit
Time frame: 180 days after result
Completed Nutrition Services Visit
Time frame: 180 days after result
Completed Appointments to Diabetes Education
Time frame: 180 days after result
Number of Patient MyChart Messages
Time frame: 7 days after viewing lab result
Numbers of Phone Calls Received after A1c Results
Time frame: 7 days after viewing lab result
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jul 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Katelyn Nguyen Assistant Clinical Research Coordinator contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Hemoglobin A1c Lab Result Communication Tool in people with Prediabetes and Health Communication, run by University of California, Los Angeles at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 9 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Katelyn Nguyen Assistant Clinical Research Coordinator (310-267-5250, katenguyen@mednet.ucla.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediabetes- Related Healthcare Utilization (time frame: 365 days after result). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jul 2026; AzTrials last refreshed its copy on 26 Oct 2026.