Implementing a prediabetes clinical decision support system in a large primary care system: Design, methods, and pre-implementation results

Jay Desai, Daniel Saman, Jo Ann M. Sperl-Hillen, Rebekah Pratt, Steven P. Dehmer, Clayton Allen, Kris Ohnsorg, Allise Wuorio, Deepika Appana, Paul Hitz, Austin Land, Rashmi Sharma, Lisa Wilkinson, A. Lauren Crain, Benjamin F. Crabtree, Joseph Bianco, Patrick J O'Connor

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Early detection of prediabetes and management of cardiovascular (CV) risk factors to prevent CV disease is essential, but clinicians are often slow to address this risk. Clinical decision support (CDS) systems, with appropriate implementation, can potentially improve prediabetes identification and treatment. Methods/design: 34 Midwestern primary care clinics were randomized to receive or not receive access to a prediabetes (Pre–D) CDS tool. Between October 2016 and December 2019, primary care clinicians (PCPs) received Pre-D CDS alerts during visits with adult patients identified with prediabetes and who met minimal inclusion criteria and had at least one CV risk factor not at goal. The PCP Pre-D CDS included a summary of six modifiable CV risk factors and patient-specific treatment recommendations. Study outcomes included total modifiable CV risk, six modifiable CV risk factors, use of CV medications, and referrals. The Consolidated Framework for Implementation Research was used to examine CDS implementation processes. Discussion: This cluster-randomized pragmatic trial allowed PCPs the opportunity to improve CV risk in a timely manner for patients with prediabetes. Effectiveness will be assessed using an intent-to-treat analysis. Implementation processes and outcomes will be assessed through interviews, surveys, and electronic health record data harvested by the CDS tool itself. Pre-implementation interviews and activities identified key strategies to incorporate as part of the Pre-D CDS implementation process to ensure acceptability and high use rates. Analyses are ongoing and trial results are expected in mid-2021.

Original languageEnglish (US)
Article number106686
JournalContemporary Clinical Trials
Volume114
DOIs
StatePublished - Mar 2022

Bibliographical note

Funding Information:
Funding was provided by the National Heart Lung and Blood Institute , Grant Number R01HL128614 . The funder had no role in the study interpretations or findings.

Publisher Copyright:
© 2022 Elsevier Inc.

Keywords

  • Cardiovascular risk factors
  • Clinical decision support
  • Diabetes prevention
  • Electronic medical records
  • Implementation
  • Prediabetes
  • Primary care
  • Rural health

PubMed: MeSH publication types

  • Journal Article
  • Randomized Controlled Trial
  • Research Support, N.I.H., Extramural

Fingerprint

Dive into the research topics of 'Implementing a prediabetes clinical decision support system in a large primary care system: Design, methods, and pre-implementation results'. Together they form a unique fingerprint.

Cite this