EMR-Based BPA Model for Type 1 Diabetes

Overseen By Osagie Ebekozien, MD

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: T1D Exchange, United States

Disqualifiers: AID use, others

Compensation: Varies

Number of Visits: Unknown

1178 Participants Needed

Trial Summary

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications. It seems to focus on understanding and improving prescription practices rather than changing your current treatment.

What data supports the effectiveness of the EMR-based BPA model treatment for Type 1 Diabetes?

Research shows that using electronic medical records (EMRs) specifically designed for diabetes care can improve the capture of important health information and adherence to screening guidelines, which helps in better managing diabetes. Additionally, EMRs with decision-making support tools have been beneficial in managing diabetes and improving care standards. ^{1 2 3 4 5}

Is the EMR-Based BPA Model for Type 1 Diabetes safe for humans?

The research articles do not provide specific safety data for the EMR-Based BPA Model for Type 1 Diabetes, but they discuss the use of electronic medical records (EMR) in diabetes care, highlighting challenges and recommendations for safe medication prescribing and patient safety improvements. ^{3 6 7 8 9}

How does the EMR-based BPA model treatment for Type 1 Diabetes differ from other treatments?

The EMR-based BPA model for Type 1 Diabetes is unique because it uses electronic medical records to improve diabetes management by integrating patient data and providing tailored care protocols. This approach enhances patient engagement and healthcare provider workflow, unlike traditional treatments that may not utilize such technology. ^{3 10 11 12 13}

What is the purpose of this trial?

The overall goal of the study is to create a standardized, stakeholder-informed system within EMRs, that will enable an equitable and regular prescription and documentation of advanced diabetes technologies. This will reduce racial disparities and generate an understanding of the reasons behind prescription decisions. The study will highlight the development and implementation an EMR-based Best Practice Advisory (BPA). The study will answer whether the EMR-based BPA can effectively reduce disparities. Additionally, it will explore why providers may not prescribe advanced diabetes technologies. Patients will also be surveyed to understand their perspectives on developing the EMR-based BPA.

Research Team

Risa Wolf

Principal Investigator

Johns Hopkins Pediatrics

Nestoras Mathioudakis

Principal Investigator

Johns Hopkins University

Osagie Ebekozien

Principal Investigator

T1DExchange

Eligibility Criteria

This trial is for people with Type 1 Diabetes. It aims to help those who might not be getting the best tech for managing their diabetes due to racial disparities. Participants should be using an electronic medical record (EMR) system, but specific inclusion and exclusion criteria are not detailed here.

Inclusion Criteria

Non-Hispanic Black and Hispanic individuals

I am over 2 years old, diagnosed with Type 1 Diabetes for at least 6 months, and receiving care at a specified center.

Exclusion Criteria

Patients with evidence of use of automated insulin delivery (AID) at baseline

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Implementation

Development and implementation of an EMR-based Best Practice Advisory (BPA) to standardize the approach for prescribing and documentation of advanced diabetes technologies

12 months

Regular visits as per EMR system updates

Follow-up

Participants are monitored for progression in Advanced Diabetes Technology use and racial disparities in technology prescription

12 months

Treatment Details

Interventions

• EMR-based BPA model

Trial Overview The study tests a new EMR-based Best Practice Advisory (BPA) model against a placebo version without BPA features. The goal is to see if this system can make prescribing advanced diabetes technologies more equitable and understand provider prescription behaviors.

Participant Groups

Group I: ADT use following Best Practice Advisory among non- Hispanic Black and Hispanic Patients with T1D Experimental Treatment 1 Intervention

ADT use following a BPA intervention among non-Hispanic Black and Hispanic PwT1D receiving care at 6 T1DX-QI centers will be assessed. The EMR-based BPA will be designed to recommend ADT prescription to patients not already using some type of ADT using a rule-based algorithm. ADT will include CGM, insulin pumps, and AID systems. We will work with each of the 6 centers to implement the BPA as part of the Epic EMR. The function will generate a BPA if patient is not utilizing a CGM or pump/AID. If the patient is not on a CGM, pump or AID system (if already using CGM and pump), the BPA will suggest discussing and/or prescribing CGM (or pump/AID) to the provider. The provider will answer in the affirmative or say, "not discussed" or "patient declined." If the provider chooses to opt out of prescribing, they will be forced to provide a reason for not prescribing to advance the screen. Providers in each intervention center will be trained on the BPA process prior to implementation.

Group II: ADT use among non- Hispanic Black and Hispanic Patients with T1D Placebo Group 1 Intervention

The arm will comprise of a matched control non-Hispanic Black and Hispanic PwT1D receiving care at a center over a 12-month period. Participants will be matched on the basis of Age categories, biological sex, Insurance status, Area deprivation index, Baseline Technology use, Duration of T1D bins, and Baseline HbA1c.

Who Is Running the Clinical Trial?

T1D Exchange, United States

Lead Sponsor

Trials

2

Recruited

50,000+

Findings from Research

Electronic health records (EHR) can significantly enhance the management of inpatient diabetes by providing clinical decision support tools, such as electronic order sets, which help reduce insulin errors and hypoglycemia rates.

Glycemic management dashboards allow healthcare providers to efficiently monitor blood glucose trends and adjust insulin regimens, improving overall patient care for those with diabetes and hyperglycemia in hospitals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Electronic Health Record-Based Decision-Making Support in Inpatient Diabetes Management. Gerwer, JE., Bacani, G., Juang, PS., et al.[2022]

The implementation of electronic health records (EHR) has introduced challenges in diabetes care, particularly for patients with limited health literacy and English proficiency, highlighting the need for safer medication prescribing practices.

Recommendations for improving patient safety include adopting standardized medication naming, enhancing communication with pharmacies, and conducting research on EHR practices in safety net healthcare systems to better serve diverse populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Challenges of Electronic Health Records and Diabetes Electronic Prescribing: Implications for Safety Net Care for Diverse Populations. Ratanawongsa, N., Chan, LL., Fouts, MM., et al.[2021]

Customizing electronic medical records (EMR) with evidence-based guidelines significantly improved the documentation of body mass index (BMI) and the diagnosis of overweight and obesity in children aged 7 to 18 years.

Despite the increase in diagnosis rates following EMR customization, the overall rates of obesity diagnosis still remain lower than the actual prevalence in the community, indicating a need for further improvement in screening practices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Childhood obesity: Can electronic medical records customized with clinical practice guidelines improve screening and diagnosis? Saviñon, C., Taylor, JS., Canty-Mitchell, J., et al.[2018]

References

1. United States pubmed.ncbi.nlm.nih.gov

Comparison of Two Electronic Systems for Obtaining Diabetes Care Indicators in Clinical Practice. [2021]

2. United States pubmed.ncbi.nlm.nih.gov

Electronic Health Record-Based Decision-Making Support in Inpatient Diabetes Management. [2022]

3. New Zealand pubmed.ncbi.nlm.nih.gov

Implementation of Electronic Medical Record Template Improves Screening for Complications in Children with Type 1 Diabetes Mellitus. [2022]

4. France pubmed.ncbi.nlm.nih.gov

Improving diabetes management with electronic medical records. [2012]

5. England pubmed.ncbi.nlm.nih.gov

Application of electronic medical record data for health outcomes research: a review of recent literature. [2019]

6. United States pubmed.ncbi.nlm.nih.gov

An Automated Risk Index for Diabetic Ketoacidosis in Pediatric Patients With Type 1 Diabetes: The RI-DKA. [2023]

7. England pubmed.ncbi.nlm.nih.gov

Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data. [2020]

8. England pubmed.ncbi.nlm.nih.gov

The Challenges of Electronic Health Records and Diabetes Electronic Prescribing: Implications for Safety Net Care for Diverse Populations. [2021]

9. United States pubmed.ncbi.nlm.nih.gov

Childhood obesity: Can electronic medical records customized with clinical practice guidelines improve screening and diagnosis? [2018]

10. Netherlands pubmed.ncbi.nlm.nih.gov

A formal model of diabetological terminology and its application for data entry. [2007]

11. England pubmed.ncbi.nlm.nih.gov

Automated integration of continuous glucose monitor data in the electronic health record using consumer technology. [2018]

12. France pubmed.ncbi.nlm.nih.gov

Improving diabetes management with electronic health records and patients' health records. [2022]

13. England pubmed.ncbi.nlm.nih.gov

A clinically useful diabetes electronic medical record: lessons from the past; pointers toward the future. [2019]