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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.
For the proposed study we intend to implement an automatically delivered center decision support system provided at the time and location of the decision making along with actionable recommendations and computer based actionable recommendations. The provider will be required to opt out if not prescribing ADT and to provide a reason for not prescribing in order to advance on the EMR screen (Kawamoto et al, 2015).
Project Objectives Aim 1: To develop and implement an EMR-based BPA using stakeholder feedback to standardize the approach for prescribing and documentation of advanced diabetes technologies (ADT) (CGM, insulin pump, AID) among adult and pediatric PwT1D.
Aim 2: To determine the effectiveness of an EMR-based BPA in reducing racial inequities in ADT.
Primary Objective: To compare the proportion of non-white (non-Hispanic Black and Hispanic) PwT1D with progression in ADT use (CGM, insulin pump, AID) at 6 centers in the T1DX-QI (3 pediatric and 3 adult centers) where the BPA intervention is implemented with matched control non-white PwT1D at T1DX-QI centers not receiving the intervention over a 12-month period, adjusting for baseline level of ADT use and other confounders.
Secondary Objective: To assess the difference in ADT use between White and non-white (non-Hispanic Black and Hispanic) PwT1D receiving care at the intervention centers compared with the racial difference in ADT use in matched control PwT1D.
Aim 3: To explore the reasons identified for providers decision to not prescribe ADT and whether they were patient or provider led, and the association between the reason provided and the patient's race/ethnicity.
Methodology Overview Focus group discussions and structured interviews will be conducted among adult and pediatric endocrine providers based on the Agency for Healthcare Research and Quality (AHRQ) "Five Rights" (Flores et al, 2019) which engenders effective clinical decision support. Also other guiding frameworks such as the T1d Exchange Health Equity Framework (Ebekozien et al, 2019) will guide this process. The BPA as well as the approach to be followed for the documenting discussions between the provider and patients will be refined by the results of the qualitative study.
Using a non-randomized matched-pair intervention design, we will compare ADT use following a BPA intervention among non-Hispanic Black and Hispanic PwT1D receiving care at 6 T1DX-QI centers with matched control non-Hispanic Black and Hispanic PwT1D receiving care at a non-intervention center over a 12-month period.
Six T1DX-QI centers that are data mapped to the T1DX-QI database by study initiation will be selected for the intervention centers. There will be four additional centers as possible back-up if one or more of the 6 anticipated centers are not able to participate. Matched "control" PwT1D will also be selected from centers with mapped data.
Primary Outcome Definition: Primary outcome: Progression in ADT use (as documented in EMR) during the 12-month study period. The primary outcome would be defined as positive for an individual if any of the following occurs:
Based on this definition, a PwT1D in any of the following technology transition states would be considered as meeting the primary endpoint:
Data Analysis for Aim2 Primary: Progression in ADT use in the intervention group compared with matched-pair controls adjusting for number of encounters, correlation of matched pairs, and random center effects.
Secondary: Analysis will mimic the primary analysis for adjustment of confounders.
We will explore if the reasons technologies are not recommended/accepted differ by race and other factors. This aim is exploratory, and the extent of the analysis will ultimately depend on information collected as part of the BPA developed during Aim 1. If patient race is associated with differences in rates of technology prescribing/use, these findings will inform further interventions to address the contributing causes.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| ADT use following Best Practice Advisory among non- Hispanic Black and Hispanic Patients with T1D | Experimental | 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. |
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| ADT use among non- Hispanic Black and Hispanic Patients with T1D | Placebo Comparator | 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| EMR-based BPA model | Other | Advanced Diabetes Technologies including AIDs, insulin pumps and CGMs will be prescribed to patients after an EMR-based BPA implementation at six centers. |
| Measure | Description | Time Frame |
|---|---|---|
| Progression of Advanced Diabetes Technology use | Defined as positive for an individual if: • MDI/smartpen + no CGM begins using CGM
| 12 months |
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Age ≥ 2 years with an EMR diagnosis of T1D for at least 6 months at baseline and receiving care at one of six intervention centers or a matched control at another T1DX-QI center.
Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Osagie Ebekozien, MD | Contact | 857-205-2388 | oebekozein@t1dexchange.org | |
| Trevon Wright, MHA | Contact | 6178927564 | twright@t1dexchange.org |
| Name | Affiliation | Role |
|---|---|---|
| Osagie Ebekozien | T1DExchange | Principal Investigator |
| Nestoras Mathioudakis | Johns Hopkins University | Study Director |
| Risa Wolf |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| T1D Exchange | Recruiting | Boston | Massachusetts | 02110 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36072814 | Background | Ebekozien O, Mungmode A, Buckingham D, Greenfield M, Talib R, Steenkamp D, Haw JS, Odugbesan O, Harris M, Mathias P, Dickinson JK, Agarwal S. Achieving Equity in Diabetes Research: Borrowing From the Field of Quality Improvement Using a Practical Framework and Improvement Tools. Diabetes Spectr. 2022 Summer;35(3):304-312. doi: 10.2337/dsi22-0002. Epub 2022 Aug 15. | |
| 15767266 |
| Label | URL |
|---|---|
| Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success | View source |
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There will be no Individual Participant data collection.
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| ID | Term |
|---|---|
| D003922 | Diabetes Mellitus, Type 1 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| Placebo (EMR-based without BPA model) | Behavioral | Advanced Diabetes Technologies including AIDs, insulin pumps and CGMs will be prescribed to patients after an EMR-based system without BPA at the implementation at six centers. |
|
| Johns Hopkins Pediatrics |
| Study Director |
| T1D Exchange | Not yet recruiting | Boston | Massachusetts | 02111 | United States |
|
| Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005 Apr 2;330(7494):765. doi: 10.1136/bmj.38398.500764.8F. Epub 2005 Mar 14. |
| 31672660 | Result | Flores EJ, Jue JJ, Giradi G, Schoelles K, Mull NK, Umscheid CA. AHRQ EPC Series on Improving Translation of Evidence: Use of a Clinical Pathway for C. Difficile Treatment to Facilitate the Translation of Research Findings into Practice. Jt Comm J Qual Patient Saf. 2019 Dec;45(12):822-828. doi: 10.1016/j.jcjq.2019.10.002. Epub 2019 Oct 28. |
| 40434817 | Derived | Mathioudakis N, Wolf R, Choudhary A, Davis G, Gallagher MP, Gupta M, Kamboj M, Rioles N, Ospelt E, Thapa S, Weinstock RS, Wright T, Ebekozien O. Implementation and Evaluation of a Best Practice Advisory to Reduce Inequities in Technology Use for People With Type 1 Diabetes: Protocol for a Mixed Methods, Nonrandomized Controlled Trial. JMIR Res Protoc. 2025 May 28;14:e71038. doi: 10.2196/71038. |
| AHRQ EPC Series on Improving Translation of Evidence: Use of a Clinical Pathway for C. Difficile Treatment to Facilitate the Translation of Research Findings into Practice | View source |
| Achieving Equity in Diabetes Research: Borrowing From the Field of Quality Improvement Using a Practical Framework and Improvement Tools | View source |
| D004700 | Endocrine System Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |