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Accepted for/Published in: JMIR Human Factors

Date Submitted: Jan 19, 2018
Open Peer Review Period: Jan 22, 2018 - May 8, 2018
Date Accepted: Aug 30, 2018
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study

Jones M, Talebi R, Littlejohn J, Bosnic O, Aprile J

An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study

JMIR Hum Factors 2018;5(4):e30

DOI: 10.2196/humanfactors.9889

PMID: 30578203

PMCID: 6320431

An Optimization Program to Assess Data Quality and Workflow Helps Practices Realize More Value From Their Electronic Medical Records

  • Mavis Jones; 
  • Reza Talebi; 
  • Jennifer Littlejohn; 
  • Olivera Bosnic; 
  • Jason Aprile

ABSTRACT

Background:

Electronic medical record (EMR) adoption among Canadian primary care physicians continues to grow. In Ontario, >80% of primary care providers now use EMRs. Adopting an EMR does not guarantee better practice management or patient care; however, EMR users must understand how to effectively use it before they can realize its full benefit. OntarioMD developed an EMR Practice Enhancement Program (EPEP) to overcome challenges of clinicians and staff in finding time to learn a new technology or workflow. EPEP deploys practice consultants to work with clinicians onsite to harness their EMR toward practice management and patient care goals.

Objective:

This paper aims to illustrate the application of the EPEP approach to address practice-level factors that impede or enhance the effective use of EMRs to support patient outcomes and population health. The secondary objective is to draw attention to the potential impact of this practice-level work to population health (system-level), as priority population health indicators are addressed by quality improvement work at the practice-level.

Methods:

EPEP’s team of practice consultants work with clinicians to identify gaps in their knowledge of EMR functionality, analyze workflow, review EMR data quality, and develop action plans with achievable tasks. Consultants establish baselines for data quality in key clinical indicators and EMR proficiency using OntarioMD-developed maturity assessment tools. We reassessed and compared postengagement, data quality, and maturity. Three examples illustrating the EPEP approach and results are presented to illustrate strengths, limitations, and implications for further analysis. In each example, a different consultant was responsible for engaging with the practice to conduct the EPEP method. No standard timeframe exists for an EPEP engagement, as requirements differ from practice to practice, and EPEP tailors its approach and timeframe according to the needs of the practice.

Results:

After presenting findings of the initial data quality review, workflow, and gap analysis to the practice, consultants worked with practices to develop action plans and begin implementing recommendations. Each practice had different objectives in engaging the EPEP; here, we compared improvements across measures that were common priorities among all 3—screening (colorectal, cervical, and breast), diabetes diagnosis, and documentation of the smoking status. Consultants collected postengagement data at intervals (approximately 6, 12, and 18 months) to assess the sustainability of the changes. The postengagement assessment showed data quality improvements across several measures, and new confidence in their data enabled practices to implement more advanced functions (such as toolbars) and targeted initiatives for subpopulations of patients.

Conclusions:

Applying on-site support to analyze gaps in EMR knowledge and use, identify efficiencies to improve workflow, and correct data quality issues can make dramatic improvements in a practice’s EMR proficiency, allowing practices to experience greater benefit from their EMR, and consequently, improve their patient care.


 Citation

Please cite as:

Jones M, Talebi R, Littlejohn J, Bosnic O, Aprile J

An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study

JMIR Hum Factors 2018;5(4):e30

DOI: 10.2196/humanfactors.9889

PMID: 30578203

PMCID: 6320431

Per the author's request the PDF is not available.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.