Pre DiabetesNCT07252700
Preventing Diabetes: Impact of an EMR-Based Intervention for Enhanced Pre-Diabetes Management in Primary Care
A study testing Electronic Medical Record-Based Clinical Decision Support in people with Pre Diabetes and Diabetes (DM), run by Lynette Goh at 1 site in Singapore.
Trial summary
The goal of this study is to find out if adding electronic medical record (EMR) prompts helps prevent people with pre-diabetes from developing diabetes. It will also look at how these prompts affect doctor and patient behaviors. Read more about this study
- Who is it for?
- Ages 21 to 59 years
- Conditions the study is for
- Pre Diabetes, Diabetes (DM)
- What is being tested?
- Electronic Medical Record-Based Clinical Decision Support
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 6,000 participants
- Where
- 1 siteSingapore
- Study dates
- 2 Sep 2025to 31 Mar 2028
- 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 21 to 59 years
- All sexes
- No healthy volunteers
- Adult
Who may be able to join
- Adults aged 40-59 years
- Diagnosed with prediabetes (impaired fasting glucose or impaired glucose tolerance)
- Receiving routine follow-up care at NUP polyclinics
- Able to provide written informed consent
- Able to complete questionnaires in English
- All clinicians that consult patients in National University Polyclinics
- No exclusion criteria specified for clinicians
Who cannot join
- Prior diagnosis of diabetes mellitus or gestational diabetes
- Severe acute or chronic liver or kidney disease
- Pregnancy
- Cognitive impairment
- Inability to communicate in English
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Singapore1 site, 1 recruiting
- Recruiting
National University Polyclinics, Singapore
Singapore, 643664
Lynette Goh, BNutrDiet, 6598235619, lynette_ml_goh@nuhs.edu.sg
lynette_ml_goh@nuhs.edu.sg
About this study
The goal of this study is to find out if adding electronic medical record (EMR) prompts helps prevent people with pre-diabetes from developing diabetes. It will also look at how these prompts affect doctor and patient behaviors.
The main questions are:
Read more
Does it improve follow-up care, such as blood tests, referrals, and medication? Does the EMR prompt reduce the number of patients who progress to diabetes within six months?
Researchers will compare clinics that use EMR prompts with clinics that do not.
Participants will:
Receive usual care for pre-diabetes at their polyclinic In some clinics, doctors will see EMR prompts suggesting tests, referrals, and medication Complete surveys about their health and lifestyle at different time points
Read the detailed description
This is a two-year cluster-randomized controlled trial conducted across eight primary care polyclinics within the National University Polyclinics (NUP) network in Singapore. These clinics provide multidisciplinary family medicine and chronic disease management services to a large and diverse population. All sites use a unified Electronic Medical Record (EMR) system (Epic, National University Health System cluster), which supports standardized clinical workflows, integrates decision-support tools, and enables secure extraction of de-identified data for research.
The study targets adults aged 21-59 years with prediabetes. All clinicians in both intervention and control clinics will receive standardized clinical training on updated prediabetes clinical practice guidelines and patient education materials. The updated workflow emphasizes lifestyle modification and behavioural counselling as the foundation of diabetes prevention. Clinicians are guided to refer patients to a dietitian or structured lifestyle programme if body mass index (BMI) is 23 kg/m² or above, counsel patients on nutrition and physical activity, schedule a six-month follow-up review, order HbA1c testing prior to the next review, and consider metformin initiation if HbA1c exceeds 6.5% after six months of lifestyle intervention, particularly in adults under 60 years with BMI ≥ 23 kg/m². Training will be conducted virtually during protected lunchtime sessions.
The study consists of three sequential phases. Phase 0 (Baseline) involves no workflow intervention, during which baseline EMR and survey data are collected. Phase 1 (Workflow Phase) introduces the standardized prediabetes clinical workflow across all clinics. Phase 2 (Prompt Phase) introduces EMR-based smart-set prompts only in intervention clinics to evaluate whether prompts further increase referrals, follow-up scheduling, HbA1c testing, and metformin prescribing beyond the workflow alone. Control clinics continue to use the standardized workflow without EMR prompts. The smart-set prompts are designed to be non-intrusive and provide decision support without interrupting workflow or overriding clinical judgment. Clinicians retain full autonomy to accept, modify, or dismiss suggested actions.
A sub-sample of approximately 300 patients will complete questionnaires assessing lifestyle behaviours and patient activation using the Consumer Health Activation Index (CHAI) to complement EMR-derived outcomes. Approximately 80-100 clinicians are expected to complete voluntary, anonymous surveys assessing knowledge, confidence, and clinical behaviours using the COM-B framework. Baseline clinical and survey data will be collected prior to intervention implementation, with follow-up data collected at multiple time points to evaluate short- and longer-term outcomes.
Intervention components include standardized workflow implementation and clinician education across all clinics, with additional EMR-based prompts implemented only in intervention clinics. Smart-set prompts integrated within Epic display automated reminders at the point of care, with options to facilitate orders for laboratory tests, referrals, medications, and follow-up scheduling. Prompts are non-mandatory to preserve clinician autonomy. The intervention is informed by the COM-B model to enhance clinician capability (through training and guidelines), opportunity (through EMR-enabled workflows and referral pathways), and motivation (through feedback and reinforcement). The Transtheoretical Model (TTM) will be used to monitor stages of change among both patients and clinicians.
Survey data will be collected electronically using FormSG, a secure, government-hosted platform approved for research use. All study data will be stored on institution-approved, PDPA-compliant servers with access restricted to authorized study personnel. Identifiable and de-identified datasets will be stored separately. De-identified datasets will be transferred to analysts using encrypted, password-protected channels. Only the Principal Investigator will have access to the linkage file containing study identifiers and personal identifiers. Hard-copy consent forms will be stored in locked cabinets accessible only to the Principal Investigator. Study data will be retained for six years following study completion in accordance with institutional policy, after which electronic data will be securely deleted and physical records destroyed.
Analyses will follow the intention-to-treat principle, with participants analysed according to their assigned clinic groups. Baseline characteristics will be summarized descriptively. Changes over time and differences between intervention and control clinics will be examined using regression models appropriate to outcome type, including mixed-effects logistic regression to account for clustering at the clinic level. Time-to-event analyses using Cox proportional hazards regression will be used to assess progression to diabetes while accounting for variable follow-up durations. Missing data will be addressed using multiple imputation, and sensitivity analyses will be conducted to assess robustness of findings. All analyses will use two-sided tests with a significance level of 0.05.
Outcome data will be collected at baseline, 6 months, 12 months, and at 18 and 24 months to assess short- and longer-term effects of workflow and EMR-based decision-support implementation.
This study will contribute evidence on the effectiveness of a non-intrusive, EMR-embedded clinical decision-support system for improving guideline-concordant prediabetes care in primary care and inform scalable strategies for diabetes prevention.
- Conditions
- Pre Diabetes, Diabetes (DM)
- Being tested
- Electronic Medical Record-Based Clinical Decision Support
- Keywords
- prediabetes, primary care, electronic medical records (EMR), OurPractice Advisories
- Medical subject headings
- Glucose Intolerance, Diabetes Mellitus, Prediabetic State
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Electronic Medical Record-Based Clinical Decision SupportBehavioural
A non-intrusive OurPractice Advisories (OPA) will be implemented in the Epic EMR system. The OPA will appear in the Visit Navigator and will be automatically triggered for patients with pre-diabetes. It will provide clinicians with reminders and decision-support options to complete the recommended clinical workflow for pre-diabetes management, including referrals, follow-up scheduling, HbA1c testing, and medication initiation when indicated.
| Study group | Role | What they receive |
|---|---|---|
| EMR-Based Intervention - Practice advisories (OPA) in electronic medical records (EMR) | Active comparator | To implement a non-intrusive OPA in EMR (Epic) to appear in the OPA section of the Visit Navigator in Epic that will be triggered for patients with prediabetes to remind clinicians to fulfil the clinical workflow for managing patients with prediabetes Receives: Behavioral: Electronic Medical Record-Based Clinical Decision Support |
| Comparison Group | No intervention | Control clinics continue to use the clinical workflow without EMR-based OPAs, representing usual care. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Guideline-concordant prediabetes care
The primary outcome of this study is the proportion of patients receiving guideline-concordant prediabetes care within 6 months of the index consultation, defined as the first prediabetes consultation during the study period at which patient meets eligibility criteria for prediabetes.
- HbA1c test ordered and recorded in the EMR.
- Referral to a dietitian or structured lifestyle programme placed and recorded in the EMR.
- Follow-up visit ordered by the attending clinician.
- Metformin prescription recorded in the EMR for eligible patients (HbA1c ≥ 6.5%).
Scale: Guideline-concordant care is defined as a composite measure in which patients have at least two of the following four clinician-initiated care processes captured in EMR data:
Method: EMR data extracted from the Epic system, including laboratory orders, referrals, visit scheduling, and medication prescriptions.
Measured: From enrollment to the end of intervention period at 24 months. collected at baseline, 6 months, 12, months, 18 months and 24 months.
Secondary outcomes (3)
Progression to Diabetes
Progression from prediabetes to diabetes at 6, 12, 18 and 24 months. Diabetes will be defined according to the Agency for Care Effectiveness (ACE) Appropriate Care Guide as fasting plasma glucose ≥7.0 mmol/L, HbA1c ≥7.0%, or 2-hour plasma glucose ≥11.1 mmol/L during an oral glucose tolerance test, as recorded in the EMR.
Scale: Progression to diabetes, defined as the occurrence of diabetes during follow-up among patients with prediabetes at baseline.
Method: Data extraction from EMR records
Measured: Up to 24 months after index consultation
Patient health activation
Measured using the Consumer Health Activation Index (CHAI) questionnaire. Scores reflect patients' knowledge, skills, and confidence in managing their health. Minimum value: 10 Maximum value: 60. This will then be transformed into a 0 to 100 scale.
Interpretation: Higher scores indicate greater activation. Method: Surveys administered to a sub-sample of approximately 300 patients
Measured: Baseline, 6 months, and 12 months.
Clinician perceptions and satisfaction with the EMR-based intervention
Clinician perceptions and satisfaction with the EMR-based intervention will be assessed through an anonymous survey.
Description: Survey assessing clinician confidence, familiarity with guidelines, current practices, use of EMR tools, and satisfaction with intervention. Includes multiple-choice and Likert-scale questions (4-point confidence/familiarity scales, 5-point frequency scales, 4-point agreement scales).
Key areas:
Clinician capability (knowledge, skills, confidence) Current practices in pre-diabetes management Use and integration of EMR tools Perceived barriers Impact of EMR tools on care delivery Overall satisfaction
Interpretation: Responses will be analyzed individually and in aggregate to assess changes in clinician perceptions, practices, and satisfaction over time. Higher scores on agreement scales generally indicate more positive perceptions or greater satisfaction.
Measured: Baseline, 6 months and 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Nov 2025
- Study start2 Sep 2025
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Mar 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (19)Papers the sponsor has linked to this study.
- Other trials for Pre DiabetesStudies of the same condition, open and under way.
- 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
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 6,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Lynette Goh
- Sponsor type
- University, hospital or other
- Collaborators
- National University Health System, Singapore, National University of Singapore
- Responsible party
- Sponsor-investigator, Lynette Goh, National University Polyclinics, Singapore
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07252700
- Sponsor's protocol ID
- ECOS Ref No.: 2025-1224
- Status last verified
- Jan 2026
- First submitted
- 18 Nov 2025
- Last updated
- 14 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Privacy and Confidentiality: Our study involves some sensitive health information. Even with de-identification, there remains a risk of re-identification, particularly given the detailed nature of our data and the specific population studied. Ethical Constraints: Our informed consent process and ethics approval did not explicitly include provisions for broad data sharing beyond the immediate research team and oversight committees. Regulatory Compliance: Local health data protection regulations place strict limitations on the sharing of individual-level health data, even in anonymized form. However, we are committed to scientific transparency and reproducibility. Therefore, we will make aggregate data, statistical codes, and detailed study protocols available upon reasonable request, subject to approval by our institutional review board and data access committee.
Publications
Wolf MS, Smith SG, Pandit AU, Condon DM, Curtis LM, Griffith J, O'Conor R, Rush S, Bailey SC, Kaplan G, Haufle V, Martin D. Development and Validation of the Consumer Health Activation Index. Med Decis Making. 2018 Apr;38(3):334-343. doi: 10.1177/0272989X17753392. Epub 2018 Feb 13.
PubMed 29436308Goh SY, Ang SB, Bee YM, Chen YT, Gardner DS, Ho ET, Adaikan K, Lee YC, Lee CH, Lim FS, Lim HB, Lim SC, Seow J, Soh AW, Sum CF, Tai ES, Thai AC, Wong TY, Yap F. Ministry of Health Clinical Practice Guidelines: Diabetes Mellitus. Singapore Med J. 2014 Jun;55(6):334-47. doi: 10.11622/smedj.2014079.
PubMed 25017409Shuyu Ng C, Toh MP, Ko Y, Yu-Chia Lee J. Direct medical cost of type 2 diabetes in singapore. PLoS One. 2015 Mar 27;10(3):e0122795. doi: 10.1371/journal.pone.0122795. eCollection 2015.
PubMed 25816299Ang YG, WU CX, Toh MP, Chia KS, Heng BH. Progression rate of newly diagnosed impaired fasting glycemia to type 2 diabetes mellitus: a study using the National Healthcare Group Diabetes Registry in Singapore. J Diabetes. 2012 Jun;4(2):159-63. doi: 10.1111/j.1753-0407.2011.00169.x.
PubMed 22059651Tomic D, Shaw JE, Magliano DJ. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol. 2022 Sep;18(9):525-539. doi: 10.1038/s41574-022-00690-7. Epub 2022 Jun 6.
PubMed 35668219Saito T, Watanabe M, Nishida J, Izumi T, Omura M, Takagi T, Fukunaga R, Bandai Y, Tajima N, Nakamura Y, Ito M; Zensharen Study for Prevention of Lifestyle Diseases Group. Lifestyle modification and prevention of type 2 diabetes in overweight Japanese with impaired fasting glucose levels: a randomized controlled trial. Arch Intern Med. 2011 Aug 8;171(15):1352-60. doi: 10.1001/archinternmed.2011.275.
PubMed 21824948Keck JW, Roper KL, Hieronymus LB, Thomas AR, Huang Z, Westgate PM, Fowlkes JL, Cardarelli R. Primary Care Cluster RCT to Increase Diabetes Prevention Program Referrals. Am J Prev Med. 2020 Jul;59(1):79-87. doi: 10.1016/j.amepre.2020.02.008. Epub 2020 May 14.
PubMed 32418801Jiang Q, Li JT, Sun P, Wang LL, Sun LZ, Pang SG. Effects of lifestyle interventions on glucose regulation and diabetes risk in adults with impaired glucose tolerance or prediabetes: a meta-analysis. Arch Endocrinol Metab. 2022 Apr 28;66(2):157-167. doi: 10.20945/2359-3997000000441. Epub 2022 Mar 14.
PubMed 35289514Li G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, Li H, Li H, Jiang Y, An Y, Shuai Y, Zhang B, Zhang J, Thompson TJ, Gerzoff RB, Roglic G, Hu Y, Bennett PH. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008 May 24;371(9626):1783-9. doi: 10.1016/S0140-6736(08)60766-7.
PubMed 18502303Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V; Indian Diabetes Prevention Programme (IDPP). The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia. 2006 Feb;49(2):289-97. doi: 10.1007/s00125-005-0097-z. Epub 2006 Jan 4.
PubMed 16391903Lim RBT, Wee WK, For WC, Ananthanarayanan JA, Soh YH, Goh LML, Tham DKT, Wong ML. Health education and communication needs among primary care patients with prediabetes in Singapore: A mixed methods approach. Prim Care Diabetes. 2020 Jun;14(3):254-264. doi: 10.1016/j.pcd.2019.08.008. Epub 2019 Sep 23.
PubMed 31558372Cheah MH, Goh LH, Zheng RM, Burkill S, Young DYL. Prediabetes guidelines adherence and health outcomes at a Singapore primary health care institution. Singapore Med J. 2024 Jan 8. doi: 10.4103/singaporemedj.SMJ-2021-220. Online ahead of print. No abstract available.
PubMed 38189477Holliday CS, Williams J, Salcedo V, Kandula NR. Clinical Identification and Referral of Adults With Prediabetes to a Diabetes Prevention Program. Prev Chronic Dis. 2019 Jun 27;16:E82. doi: 10.5888/pcd16.180540.
PubMed 31255186Lessing SE, Hayman LL. Diabetes Care and Management Using Electronic Medical Records: A Systematic Review. J Diabetes Sci Technol. 2019 Jul;13(4):774-782. doi: 10.1177/1932296818815507. Epub 2018 Dec 17.
PubMed 30556418O'Connor PJ, Crain AL, Rush WA, Sperl-Hillen JM, Gutenkauf JJ, Duncan JE. Impact of an electronic medical record on diabetes quality of care. Ann Fam Med. 2005 Jul-Aug;3(4):300-6. doi: 10.1370/afm.327.
PubMed 16046561
Study at a glance
From the registry record, last updated by the sponsor on 14 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Preventing Diabetes: Impact of an EMR-Based Intervention for Enhanced Pre-Diabetes Management in Primary Care
- ClinicalTrials.gov ID
- NCT07252700
- Acronym / sponsor ID
- ECOS Ref No.: 2025-1224
- Conditions
- Pre Diabetes, Diabetes (DM)
- Keywords
- prediabetes, primary care, electronic medical records (EMR), OurPractice Advisories
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 6,000 participants (estimated)
- Lead sponsor
- Lynette GohUniversity, hospital or other
- Collaborators
- National University Health System, Singapore, National University of Singapore
- Study start
- 2 Sep 2025 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Mar 2028 (estimated)
- First posted
- 28 Nov 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.
The goal of this study is to find out if adding electronic medical record (EMR) prompts helps prevent people with pre-diabetes from developing diabetes. It will also look at how these prompts affect doctor and patient behaviors.
The main questions are:
Does it improve follow-up care, such as blood tests, referrals, and medication? Does the EMR prompt reduce the number of patients who progress to diabetes within six months?
Researchers will compare clinics that use EMR prompts with clinics that do not.
Participants will:
Receive usual care for pre-diabetes at their polyclinic In some clinics, doctors will see EMR prompts suggesting tests, referrals, and medication Complete surveys about their health and lifestyle at different time points
Detailed description
This is a two-year cluster-randomized controlled trial conducted across eight primary care polyclinics within the National University Polyclinics (NUP) network in Singapore. These clinics provide multidisciplinary family medicine and chronic disease management services to a large and diverse population. All sites use a unified Electronic Medical Record (EMR) system (Epic, National University Health System cluster), which supports standardized clinical workflows, integrates decision-support tools, and enables secure extraction of de-identified data for research.
The study targets adults aged 21-59 years with prediabetes. All clinicians in both intervention and control clinics will receive standardized clinical training on updated prediabetes clinical practice guidelines and patient education materials. The updated workflow emphasizes lifestyle modification and behavioural counselling as the foundation of diabetes prevention. Clinicians are guided to refer patients to a dietitian or structured lifestyle programme if body mass index (BMI) is 23 kg/m² or above, counsel patients on nutrition and physical activity, schedule a six-month follow-up review, order HbA1c testing prior to the next review, and consider metformin initiation if HbA1c exceeds 6.5% after six months of lifestyle intervention, particularly in adults under 60 years with BMI ≥ 23 kg/m². Training will be conducted virtually during protected lunchtime sessions.
The study consists of three sequential phases. Phase 0 (Baseline) involves no workflow intervention, during which baseline EMR and survey data are collected. Phase 1 (Workflow Phase) introduces the standardized prediabetes clinical workflow across all clinics. Phase 2 (Prompt Phase) introduces EMR-based smart-set prompts only in intervention clinics to evaluate whether prompts further increase referrals, follow-up scheduling, HbA1c testing, and metformin prescribing beyond the workflow alone. Control clinics continue to use the standardized workflow without EMR prompts. The smart-set prompts are designed to be non-intrusive and provide decision support without interrupting workflow or overriding clinical judgment. Clinicians retain full autonomy to accept, modify, or dismiss suggested actions.
A sub-sample of approximately 300 patients will complete questionnaires assessing lifestyle behaviours and patient activation using the Consumer Health Activation Index (CHAI) to complement EMR-derived outcomes. Approximately 80-100 clinicians are expected to complete voluntary, anonymous surveys assessing knowledge, confidence, and clinical behaviours using the COM-B framework. Baseline clinical and survey data will be collected prior to intervention implementation, with follow-up data collected at multiple time points to evaluate short- and longer-term outcomes.
Intervention components include standardized workflow implementation and clinician education across all clinics, with additional EMR-based prompts implemented only in intervention clinics. Smart-set prompts integrated within Epic display automated reminders at the point of care, with options to facilitate orders for laboratory tests, referrals, medications, and follow-up scheduling. Prompts are non-mandatory to preserve clinician autonomy. The intervention is informed by the COM-B model to enhance clinician capability (through training and guidelines), opportunity (through EMR-enabled workflows and referral pathways), and motivation (through feedback and reinforcement). The Transtheoretical Model (TTM) will be used to monitor stages of change among both patients and clinicians.
Survey data will be collected electronically using FormSG, a secure, government-hosted platform approved for research use. All study data will be stored on institution-approved, PDPA-compliant servers with access restricted to authorized study personnel. Identifiable and de-identified datasets will be stored separately. De-identified datasets will be transferred to analysts using encrypted, password-protected channels. Only the Principal Investigator will have access to the linkage file containing study identifiers and personal identifiers. Hard-copy consent forms will be stored in locked cabinets accessible only to the Principal Investigator. Study data will be retained for six years following study completion in accordance with institutional policy, after which electronic data will be securely deleted and physical records destroyed.
Analyses will follow the intention-to-treat principle, with participants analysed according to their assigned clinic groups. Baseline characteristics will be summarized descriptively. Changes over time and differences between intervention and control clinics will be examined using regression models appropriate to outcome type, including mixed-effects logistic regression to account for clustering at the clinic level. Time-to-event analyses using Cox proportional hazards regression will be used to assess progression to diabetes while accounting for variable follow-up durations. Missing data will be addressed using multiple imputation, and sensitivity analyses will be conducted to assess robustness of findings. All analyses will use two-sided tests with a significance level of 0.05.
Outcome data will be collected at baseline, 6 months, 12 months, and at 18 and 24 months to assess short- and longer-term effects of workflow and EMR-based decision-support implementation.
This study will contribute evidence on the effectiveness of a non-intrusive, EMR-embedded clinical decision-support system for improving guideline-concordant prediabetes care in primary care and inform scalable strategies for diabetes prevention.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| EMR-Based Intervention - Practice advisories (OPA) in electronic medical records (EMR) | Active comparator | Behavioral: Electronic Medical Record-Based Clinical Decision SupportTo implement a non-intrusive OPA in EMR (Epic) to appear in the OPA section of the Visit Navigator in Epic that will be triggered for patients with prediabetes to remind clinicians to fulfil the clinical workflow for managing patients with prediabetes |
| Comparison Group | No intervention | –Control clinics continue to use the clinical workflow without EMR-based OPAs, representing usual care. |
Who may qualify
- Ages
- 21 to 59 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (7 listed)
- Adults aged 40-59 years
- Diagnosed with prediabetes (impaired fasting glucose or impaired glucose tolerance)
- Receiving routine follow-up care at NUP polyclinics
- Able to provide written informed consent
- Able to complete questionnaires in English
Key exclusion criteria (5 listed)
- Prior diagnosis of diabetes mellitus or gestational diabetes
- Severe acute or chronic liver or kidney disease
- Pregnancy
- Cognitive impairment
- Inability to communicate in English
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 |
|---|---|---|
| Electronic Medical Record-Based Clinical Decision SupportA non-intrusive OurPractice Advisories (OPA) will be implemented in the Epic EMR system. The OPA will appear in the Visit Navigator and will be automatically triggered for patients with pre-diabetes. It will provide clinicians with reminders and decision-support options to complete the recommended clinical workflow for pre-diabetes management, including referrals, follow-up scheduling, HbA1c testing, and medication initiation when indicated. | Behavioural | EMR-Based Intervention - Practice advisories (OPA) in electronic medical records (EMR) |
Primary outcome
Guideline-concordant prediabetes care
Time frame: From enrollment to the end of intervention period at 24 months. collected at baseline, 6 months, 12, months, 18 months and 24 months.
The primary outcome of this study is the proportion of patients receiving guideline-concordant prediabetes care within 6 months of the index consultation, defined as the first prediabetes consultation during the study period at which patient meets eligibility criteria for prediabetes. 1. HbA1c test ordered and recorded in the EMR. 2. Referral to a dietitian or structured lifestyle programme placed and recorded in the EMR. 3. Follow-up visit ordered by the attending clinician. 4. Metformin prescription recorded in the EMR for eligible patients (HbA1c ≥ 6.5%). Scale: Guideline-concordant care is defined as a composite measure in which patients have at least two of the following four clinician-initiated care processes captured in EMR data: Method: EMR data extracted from the Epic system, including laboratory orders, referrals, visit scheduling, and medication prescriptions.
3 secondary outcomes
Progression to Diabetes
Time frame: Up to 24 months after index consultation
Patient health activation
Time frame: Baseline, 6 months, and 12 months.
Clinician perceptions and satisfaction with the EMR-based intervention
Time frame: Baseline, 6 months and 12 months
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 14 Jan 2026.
- Background: PubMed 29436308Wolf MS, Smith SG, Pandit AU, Condon DM, Curtis LM, Griffith J, O'Conor R, Rush S, Bailey SC, Kaplan G, Haufle V, Martin D. Development and Validation of the Consumer Health Activation Index. Med Decis Making. 2018 Apr;38(3):334-343. doi: 10.1177/0272989X17753392. Epub 2018 Feb 13.
- Background: PubMed 25017409Goh SY, Ang SB, Bee YM, Chen YT, Gardner DS, Ho ET, Adaikan K, Lee YC, Lee CH, Lim FS, Lim HB, Lim SC, Seow J, Soh AW, Sum CF, Tai ES, Thai AC, Wong TY, Yap F. Ministry of Health Clinical Practice Guidelines: Diabetes Mellitus. Singapore Med J. 2014 Jun;55(6):334-47. doi: 10.11622/smedj.2014079.
- Background: PubMed 25816299Shuyu Ng C, Toh MP, Ko Y, Yu-Chia Lee J. Direct medical cost of type 2 diabetes in singapore. PLoS One. 2015 Mar 27;10(3):e0122795. doi: 10.1371/journal.pone.0122795. eCollection 2015.
- Background: PubMed 22059651Ang YG, WU CX, Toh MP, Chia KS, Heng BH. Progression rate of newly diagnosed impaired fasting glycemia to type 2 diabetes mellitus: a study using the National Healthcare Group Diabetes Registry in Singapore. J Diabetes. 2012 Jun;4(2):159-63. doi: 10.1111/j.1753-0407.2011.00169.x.
- Background: PubMed 35668219Tomic D, Shaw JE, Magliano DJ. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol. 2022 Sep;18(9):525-539. doi: 10.1038/s41574-022-00690-7. Epub 2022 Jun 6.
- Background: PubMed 21824948Saito T, Watanabe M, Nishida J, Izumi T, Omura M, Takagi T, Fukunaga R, Bandai Y, Tajima N, Nakamura Y, Ito M; Zensharen Study for Prevention of Lifestyle Diseases Group. Lifestyle modification and prevention of type 2 diabetes in overweight Japanese with impaired fasting glucose levels: a randomized controlled trial. Arch Intern Med. 2011 Aug 8;171(15):1352-60. doi: 10.1001/archinternmed.2011.275.
- Background: PubMed 32418801Keck JW, Roper KL, Hieronymus LB, Thomas AR, Huang Z, Westgate PM, Fowlkes JL, Cardarelli R. Primary Care Cluster RCT to Increase Diabetes Prevention Program Referrals. Am J Prev Med. 2020 Jul;59(1):79-87. doi: 10.1016/j.amepre.2020.02.008. Epub 2020 May 14.
- Background: PubMed 35289514Jiang Q, Li JT, Sun P, Wang LL, Sun LZ, Pang SG. Effects of lifestyle interventions on glucose regulation and diabetes risk in adults with impaired glucose tolerance or prediabetes: a meta-analysis. Arch Endocrinol Metab. 2022 Apr 28;66(2):157-167. doi: 10.20945/2359-3997000000441. Epub 2022 Mar 14.
- Background: PubMed 18502303Li G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, Li H, Li H, Jiang Y, An Y, Shuai Y, Zhang B, Zhang J, Thompson TJ, Gerzoff RB, Roglic G, Hu Y, Bennett PH. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008 May 24;371(9626):1783-9. doi: 10.1016/S0140-6736(08)60766-7.
- Background: PubMed 16391903Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V; Indian Diabetes Prevention Programme (IDPP). The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia. 2006 Feb;49(2):289-97. doi: 10.1007/s00125-005-0097-z. Epub 2006 Jan 4.
- Background: PubMed 31558372Lim RBT, Wee WK, For WC, Ananthanarayanan JA, Soh YH, Goh LML, Tham DKT, Wong ML. Health education and communication needs among primary care patients with prediabetes in Singapore: A mixed methods approach. Prim Care Diabetes. 2020 Jun;14(3):254-264. doi: 10.1016/j.pcd.2019.08.008. Epub 2019 Sep 23.
- Background: PubMed 38189477Cheah MH, Goh LH, Zheng RM, Burkill S, Young DYL. Prediabetes guidelines adherence and health outcomes at a Singapore primary health care institution. Singapore Med J. 2024 Jan 8. doi: 10.4103/singaporemedj.SMJ-2021-220. Online ahead of print. No abstract available.
- Individual participant dataSharing planned: No. Privacy and Confidentiality: Our study involves some sensitive health information. Even with de-identification, there remains a risk of re-identification, particularly given the detailed nature of our data and the specific population studied. Ethical Constraints: Our informed consent process and ethics approval did not explicitly include provisions for broad data sharing beyond the immediate research team and oversight committees. Regulatory Compliance: Local health data protection regulations place strict limitations on the sharing of individual-level health data, even in anonymized form. However, we are committed to scientific transparency and reproducibility. Therefore, we will make aggregate data, statistical codes, and detailed study protocols available upon reasonable request, subject to approval by our institutional review board and data access committee.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Electronic Medical Record-Based Clinical Decision Support in people with Pre Diabetes and Diabetes (DM), run by Lynette Goh at 1 site in Singapore.
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 7 inclusion and 5 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 14 Jan 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 Singapore. 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 Lynette ML Goh, BNutrDiet (6598235619, lynette_ml_goh@nuhs.edu.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Guideline-concordant prediabetes care (time frame: From enrollment to the end of intervention period at 24 months. collected at baseline, 6 months, 12, months, 18 months and 24 months.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07252700, which also lists 19 linked publications.
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 14 Jan 2026.