Appl Clin Inform 2022; 13(04): 836-844
DOI: 10.1055/s-0042-1756369
AIDH Summit 2022

Improving the Quality of Electronic Medical Record Documentation: Development of a Compliance and Quality Program

Rebecca M. Jedwab
1   Department of Nursing and Midwifery Informatics, Monash Health, Melbourne, Victoria, Australia
2   School of Nursing and Midwifery, Centre for Quality and Patient Safety Research, Institute for Health Transformation, Deakin University, Burwood, Melbourne, Victoria, Australia
,
Michael Franco
3   Department of EMR and Informatics, Monash Health, Melbourne, Victoria, Australia
4   Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
,
Denise Owen
5   Department of Digital Health Training and Adoption, Monash Health, Melbourne, Victoria, Australia
,
Anna Ingram
6   Department of Medical Informatics, Monash Health, Melbourne, Victoria, Australia
,
Bernice Redley
7   School of Nursing and Midwifery, Centre for Quality and Patient Safety Research-Monash Health Partnership, Institute for Health Transformation, Deakin University, Melbourne, Victoria, Australia
8   Department of Nursing and Midwifery, Monash Health, Melbourne, Victoria, Australia
,
Naomi Dobroff
3   Department of EMR and Informatics, Monash Health, Melbourne, Victoria, Australia
9   School of Nursing and Midwifery, Deakin University, Melbourne, Victoria, Australia
› Author Affiliations

Abstract

Background Introducing an electronic medical record (EMR) system into a complex health care environment fundamentally changes clinical workflows and documentation processes and, hence, has implications for patient safety. After a multisite “big-bang” EMR implementation across our large public health care organization, a quality improvement program was developed and implemented to monitor clinician adoption, documentation quality, and compliance with workflows to support high-quality patient care.

Objective Our objective was to report the development of an iterative quality improvement program for nursing, midwifery, and medical EMR documentation.

Methods The Model for Improvement quality improvement framework guided cycles of “Plan, Do, Study, Act.” Steps included design, pre- and pilot testing of an audit tool to reflect expected practices for EMR documentation that examined quality and completeness of documentation 1-year post-EMR implementation. Analysis of initial audit results was then performed to (1) provide a baseline to benchmark comparison of ongoing improvement and (2) develop targeted intervention activities to address identified gaps.

Results Analysis of 1,349 EMR record audits as a baseline for the first cycle of EMR quality improvement revealed five out of nine nursing and midwifery documentation components, and four out of ten medical documentation components' completion and quality were classified as good (>80%). Outputs from this work also included a framework for strategies to improve EMR documentation quality, as well as an EMR data dashboard to monitor compliance.

Conclusion This work provides the foundation for the development of quality monitoring frameworks to inform both clinician and EMR optimization interventions using audits and feedback. Discipline-specific differences in performance can inform targeted interventions to maximize the effective use of resources and support longitudinal monitoring of EMR documentation and workflows. Future work will include repeat EMR auditing.

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the health care organization's institutional review board.


Author Contributions

R.M.J. was responsible for conceptualization, methodology, software, validation, formal analysis, resources, data curation, writing—original draft, writing—review and editing, visualization.


M.F. was responsible for conceptualization, methodology, validation, formal analysis, resources, writing—review and editing, visualization, supervision, and project administration.


D.O. was responsible for conceptualization, methodology, validation, resources, writing - original draft, writing—review and editing, visualization, supervision, and project administration.


A.I. was responsible for validation, formal analysis, resources, writing - original draft, writing—review and editing, and visualization.


B.R. was responsible for conceptualization, methodology, software, validation, formal analysis, resources, data curation, writing—review and editing, and visualization.


N.D. was responsible for conceptualization, methodology, validation, resources, writing—original draft, writing—review and editing, visualization, supervision, project administration, and funding acquisition.




Publication History

Received: 28 April 2022

Accepted: 14 July 2022

Article published online:
07 September 2022

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