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

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Hillestad R, Bigelow J, Bower A. et al. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood) 2005; 24 (05) 1103-1117
  • 2 Bingham G, Tong E, Poole S, Ross P, Dooley M. A longitudinal time and motion study quantifying how implementation of an electronic medical record influences hospital nurses' care delivery. Int J Med Inform 2021; 153: 104537
  • 3 Williams C, Hamadi H, Cummings CL. Optimizing the cognitive space of nursing work through electronic medical records. Comput Inform Nurs 2020; 38 (11) 545-550
  • 4 Plebani S. Unlocking meaning in EMRs, in Hospital+Healthcare. 2019
  • 5 Pham A. et al. Development of nursing workflows and device requirement principles with the implementation of an electronic medical record system. In: Honey M. et al., eds. Nurses and Midwives in the Digital Age. The Netherlands: IOS Press; 2021: 113-117
  • 6 Australian Commission on Safety and Quality in Health Care. National Safety and Quality Health Service Standards. 2nd ed.. Sydney, NSW: ACSQHC; 2017
  • 7 Moy AJ, Schwartz JM, Chen R. et al. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc 2021; 28 (05) 998-1008
  • 8 Soto CM, Kleinman KP, Simon SR. Quality and correlates of medical record documentation in the ambulatory care setting. BMC Health Serv Res 2002; 2 (01) 22
  • 9 Mordaunt DA. et al. The carousel: South Australia's approach to clinical optimisation and improvement of a state-wide Electronic Medical Record. Office of the Chief Medical Information Officer South Australia. 2020
  • 10 Najaftorkaman M. et al. A taxonomy of antecedents to user adoption of health information systems: a synthesis of thirty years of research. J Assoc Inf Sci Technol 2015; 66 (03) 576-598
  • 11 Braithwaite J. Changing how we think about healthcare improvement. BMJ 2018; 361: k2014
  • 12 Institute for Healthcare Improvement (IHI). IHI resources: How to improve. 2018 . Accessed August 02, 2022 at: http://www.ihi.org/resources/Pages/HowtoImprove/default.aspx
  • 13 The Health Foundation. Quality Improvement made simple: what everyone should know about quality improvement. 2013 . Accessed August 02, 2022 at: https://www.health.org.uk/publication/quality-improvement-made-simple
  • 14 Jones B, Vaux E, Olsson-Brown A. How to get started in quality improvement. BMJ 2019; 364: k5408
  • 15 De Leeuw JA, Woltjer H, Kool RB. Identification of factors influencing the adoption of health information technology by nurses who are digitally lagging: in-depth interview study. J Med Internet Res 2020; 22 (08) e15630
  • 16 Shala DR, Jones A, Fairbrother G, Thuy Tran D. Completion of electronic nursing documentation of inpatient admission assessment: insights from Australian metropolitan hospitals. Int J Med Inform 2021; 156: 104603
  • 17 Beiter PA, Sorscher J, Henderson CJ, Talen M. Do electronic medical record (EMR) demonstrations change attitudes, knowledge, skills or needs?. Inform Prim Care 2008; 16 (03) 221-227
  • 18 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (02) 104-112
  • 19 Harmon CS, Adams SA, Davis JE. Nursing cognitive-overload and electronic documentation burden: a literature review. J Inform Nurs 2020; 5 (03) 16-30
  • 20 De Benedictis A, Lettieri E, Gastaldi L, Masella C, Urgu A, Tartaglini D. Electronic medical records implementation in hospital: an empirical investigation of individual and organizational determinants. PLoS One 2020; 15 (06) e0234108