Appl Clin Inform 2018; 09(04): 817-830
DOI: 10.1055/s-0038-1675210
State of the Art/Best Practice Paper
Georg Thieme Verlag KG Stuttgart · New York

An Evidence-Based Tool for Safe Configuration of Electronic Health Records: The eSafety Checklist

Pritma Dhillon-Chattha
1   Alberta Health Services, Edmonton, Alberta, Canada
2   Department of Nursing, Yale University, Orange, Connecticut, United States
Ruth McCorkle
2   Department of Nursing, Yale University, Orange, Connecticut, United States
Elizabeth Borycki
3   School of Health Information Science, University of Victoria, Victoria, British Columbia, Canada
› Author Affiliations
Further Information

Publication History

21 March 2018

10 September 2018

Publication Date:
14 November 2018 (online)


Background Electronic health records (EHRs) are transforming the way health care is delivered. They are central to improving the quality of patient care and have been attributed to making health care more accessible, reliable, and safe. However, in recent years, evidence suggests that specific features and functions of EHRs can introduce new, unanticipated patient safety concerns that can be mitigated by safe configuration practices.

Objective This article outlines the development of a detailed and comprehensive evidence-based checklist of safe configuration practices for use by clinical informatics professionals when configuring hospital-based EHRs.

Methods A literature review was conducted to synthesize evidence on safe configuration practices; data were analyzed to elicit themes of common EHR system capabilities. Two rounds of testing were completed with end users to inform checklist design and usability. This was followed by a four-member expert panel review, where each item was rated for clarity (clear, not clear), and importance (high, medium, low).

Results An expert panel consisting of three clinical informatics professionals and one health information technology expert reviewed the checklist for clarity and importance. Medium and high importance ratings were considered affirmative responses. Of the 870 items contained in the original checklist, 535 (61.4%) received 100% affirmative agreement among all four panelists. Clinical panelists had a higher affirmative agreement rate of 75.5% (656 items). Upon detailed analysis, items with 100% clinician agreement were retained in the checklist with the exception of 47 items and the addition of 33 items, resulting in a total of 642 items in the final checklist.

Conclusion Safe implementation of EHRs requires consideration of both technical and sociotechnical factors through close collaboration of health information technology and clinical informatics professionals. The recommended practices described in this checklist provide systems implementation guidance that should be considered when EHRs are being configured, implemented, audited, or updated, to improve system safety and usability.

Protection of Human and Animal Subjects

This quality improvement project 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 Yale Human Research Protection Program, and Alberta Innovates: A Project Ethics Community Consensus Initiative (ARECCI). The project was granted operational approval by Alberta Health Services (AHS) in accordance with applicable AHS quality improvement policies and procedures.

Supplementary Material

  • References

  • 1 IOM (Institute of Medicine). Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC: The National Academies Press; 2012
  • 2 Kushniruk AW, Triola MM, Borycki EM, Stein B, Kannry JL. Technology induced error and usability: the relationship between usability problems and prescription errors when using a handheld application. Int J Med Inform 2005; 74 (7-8): 519-526
  • 3 Digital Health Canada. eSafety Guidelines: eSafety for eHealth. Toronto, ON; 2013
  • 4 Office of the National Coordinator. Health IT Patient Safety Action & Surveillance Plan. ONC; 2013. Available at: . Accessed September 26, 2015
  • 5 Westbrook JI, Reckmann M, Li L. , et al. Effects of two commercial electronic prescribing systems on prescribing error rates in hospital in-patients: a before and after study. PLoS Med 2012; 9 (01) e1001164
  • 6 Magrabi F, Ong MS, Runciman W, Coiera E. Using FDA reports to inform a classification for health information technology safety problems. J Am Med Inform Assoc 2012; 19 (01) 45-53
  • 7 Palojoki S, Mäkelä M, Lehtonen L, Saranto K. An analysis of electronic health record-related patient safety incidents. Health Informatics J 2017; 23 (02) 134-145
  • 8 Fong A, Howe JL, Adams KT, Ratwani RM. Using active learning to identify health information technology related patient safety events. Appl Clin Inform 2017; 8 (01) 35-46
  • 9 Classen DC, Resar R, Griffin F. , et al. ‘Global trigger tool’ shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff (Millwood) 2011; 30 (04) 581-589
  • 10 Adelman J. Wrong patient errors V. Proceedings of the AHRQ National Web Conference on Assessing Safety Risks Associated with EHRs; August 29, 2016
  • 11 Sengstack P. CPOE configuration to reduce medication errors. J Healthc Inf Manag 2010; 24 (04) 26-34
  • 12 National Center for Cognitive Informatics & Decision Making in Healthcare, The University of Texas School of Biomedical Informatics. Safety enhanced design briefs. Available at: . Accessed September 26, 2016
  • 13 Tsou AY, Lehmann CU, Michel J, Solomon R, Possanza L, Gandhi T. Safe practices for copy and paste in the EHR. Systematic review, recommendations, and novel model for health IT collaboration. Appl Clin Inform 2017; 8 (01) 12-34
  • 14 Sittig DF, Ash JS, Singh H. The SAFER guides: empowering organizations to improve the safety and effectiveness of electronic health records. Am J Manag Care 2014; 20 (05) 418-423
  • 15 Alberta Health Services. Annual Report. Edmonton, AB; 2016–17. Available at: . Accessed September 24, 2017
  • 16 Dang D, Dearholt S. Johns Hopkins Nursing Evidence-Based Practice: Model and Guidelines. 3rd ed. Indianapolis, IN: Sigma Theta Tau International; 2017
  • 17 IOM (Institute of Medicine). Key Capabilities of an Electronic Health Record System: Letter Report. Washington, DC: The National Academies Press; 2003
  • 18 Lazenby M, Dixon J, Coviello J, McCorkle R. Instructions on Using Expert Panels to Rate Evidence-Based Content. New Haven, CT: Yale University; 2014