Preventing Diabetes: Impact of an EMR-Based Intervention for Enhanced Pre-Diabetes Management in Primary Care
NCT ID: NCT07252700
Last Updated: 2026-01-14
Study Results
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Basic Information
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RECRUITING
NA
6000 participants
INTERVENTIONAL
2025-09-02
2028-03-31
Brief Summary
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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
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Detailed Description
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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
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Study Design
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RANDOMIZED
PARALLEL
PREVENTION
NONE
Study Groups
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EMR-Based Intervention - Practice advisories (OPA) in electronic medical records (EMR)
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
Electronic Medical Record-Based Clinical Decision Support
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.
Comparison Group
Control clinics continue to use the clinical workflow without EMR-based OPAs, representing usual care.
No interventions assigned to this group
Interventions
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Electronic Medical Record-Based Clinical Decision Support
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.
Eligibility Criteria
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Inclusion Criteria
* Prediabetes is defined as impaired fasting glucose or impaired glucose tolerance according to standard clinical criteria, with either diagnosis documented in the EMR problem list or visit diagnosis.
* Individuals with a prior diagnosis of diabetes will be excluded.
* 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
Exclusion Criteria
* Severe acute or chronic liver or kidney disease
* Pregnancy
* Cognitive impairment
* Inability to communicate in English
21 Years
59 Years
ALL
No
Sponsors
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National University Health System, Singapore
OTHER
National University of Singapore
OTHER
Lynette Goh
OTHER
Responsible Party
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Lynette Goh
Senior Principal Dietitian
Locations
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National University Polyclinics, Singapore
Singapore, , Singapore
Countries
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Central Contacts
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Facility Contacts
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Role: backup
References
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Goh 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.
Shuyu 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.
Ang 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.
Tomic 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.
Saito 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.
Keck 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.
Jiang 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.
Li 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.
Ramachandran 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.
Lim 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.
Cheah 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.
Holliday 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.
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Meigs JB, Cagliero E, Dubey A, Murphy-Sheehy P, Gildesgame C, Chueh H, Barry MJ, Singer DE, Nathan DM. A controlled trial of web-based diabetes disease management: the MGH diabetes primary care improvement project. Diabetes Care. 2003 Mar;26(3):750-7. doi: 10.2337/diacare.26.3.750.
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Related Links
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National population health survey 2024
Other Identifiers
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ECOS Ref No.: 2025-1224
Identifier Type: -
Identifier Source: org_study_id
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