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

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 effectiv...

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Detalles Bibliográficos
Autores principales: Jones, Mavis, Talebi, Reza, Littlejohn, Jennifer, Bosnic, Olivera, Aprile, Jason
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320431/
https://www.ncbi.nlm.nih.gov/pubmed/30578203
http://dx.doi.org/10.2196/humanfactors.9889
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author Jones, Mavis
Talebi, Reza
Littlejohn, Jennifer
Bosnic, Olivera
Aprile, Jason
author_facet Jones, Mavis
Talebi, Reza
Littlejohn, Jennifer
Bosnic, Olivera
Aprile, Jason
author_sort Jones, Mavis
collection PubMed
description 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 illuminate 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.
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spelling pubmed-63204312019-01-28 An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study Jones, Mavis Talebi, Reza Littlejohn, Jennifer Bosnic, Olivera Aprile, Jason JMIR Hum Factors Original Paper 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 illuminate 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. JMIR Publications 2018-12-21 /pmc/articles/PMC6320431/ /pubmed/30578203 http://dx.doi.org/10.2196/humanfactors.9889 Text en ©Mavis Jones, Reza Talebi, Jennifer Littlejohn, Olivera Bosnic, Jason Aprile. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 21.12.2018. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on http://humanfactors.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Jones, Mavis
Talebi, Reza
Littlejohn, Jennifer
Bosnic, Olivera
Aprile, Jason
An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study
title An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study
title_full An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study
title_fullStr An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study
title_full_unstemmed An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study
title_short An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study
title_sort optimization program to help practices assess data quality and workflow with their electronic medical records: observational study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320431/
https://www.ncbi.nlm.nih.gov/pubmed/30578203
http://dx.doi.org/10.2196/humanfactors.9889
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