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Appl Clin Inform 2021; 12(05): 1049-1060
DOI: 10.1055/s-0041-1739196
Research Article

Electronic Medication Management Systems: Analysis of Enhancements to Reduce Errors and Improve Workflow

Madaline Kinlay
1   Biomedical Informatics and Digital Health, School of Medical Sciences, Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
,
Lai Mun Rebecca Ho
2   Pharmacy Services, Sydney Local Health District, Sydney, Australia
,
Wu Yi Zheng
3   Black Dog Institute, Sydney, Australia
,
Rosemary Burke
2   Pharmacy Services, Sydney Local Health District, Sydney, Australia
,
Ilona Juraskova
4   School of Psychology, Faculty of Science, The University of Sydney, Sydney, Australia
,
Rebekah Moles
5   School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
,
Melissa Baysari
1   Biomedical Informatics and Digital Health, School of Medical Sciences, Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
› Author Affiliations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Abstract

Background Electronic medication management (eMM) has been shown to reduce medication errors; however, new safety risks have also been introduced that are associated with system use. No research has specifically examined the changes made to eMM systems to mitigate these risks.

Objectives To (1) identify system-related medication errors or workflow blocks that were the target of eMM system updates, including the types of medications involved, and (2) describe and classify the system enhancements made to target these risks.

Methods In this retrospective qualitative study, documents detailing updates made from November 2014 to December 2019 to an eMM system were reviewed. Medication-related updates were classified according to “rationale for changes” and “changes made to the system.”

Results One hundred and seventeen updates, totaling 147 individual changes, were made to the eMM system over the 4-year period. The most frequent reasons for changes being made to the eMM were to prevent medication errors (24% of reasons), optimize workflow (22%), and support “work as done” on paper (16%). The most frequent changes made to the eMM were options added to lists (14% of all changes), extra information made available on the screen (8%), and the wording or phrasing of text modified (8%). Approximately a third of the updates (37%) related to high-risk medications. The reasons for system changes appeared to vary over time, as eMM functionality and use expanded.

Conclusion To our knowledge, this is the first study to systematically review and categorize system updates made to overcome new safety risks associated with eMM use. Optimization of eMM is an ongoing process, which changes over time as users become more familiar with the system and use is expanded to more sites. Continuous monitoring of the system is necessary to detect areas for improvement and capitalize on the benefits an electronic system can provide.

Keywords

order entry systems - safety - medication errors - workflows

Author Contributions

M.K., M.B., W.Y.Z., and R.B. designed the study. L.M.H. provided expertise in the electronic medication system and refining the classification. M.K. analyzed the data, with assistance from M.B., W.Y.Z., and L.M.H. All authors assisted in interpreting results and writing the manuscript. All authors read and approved the final manuscript.


Protection of Human and Animal Subjects

No human or animal subjects were directly involved in this project.


Supplementary Material



Publication History

Received: 14 June 2021

Accepted: 02 October 2021

Article published online:
10 November 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Nuckols TK, Smith-Spangler C, Morton SC. et al. The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis. Syst Rev 2014; 3: 56
    CrossrefPubMedGoogle Scholar
  • 2 Gates PJ, Hardie RA, Raban MZ, Li L, Westbrook JI. How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis. J Am Med Inform Assoc 2021; 28 (01) 167-176
    CrossrefPubMedGoogle Scholar
  • 3 NSW Therapeutic Advisory Group Inc. and eHealth NSW.. Building Sustainable Governance of Electronic Medication Management: Guiding Principles for Drug and Therapeutic Committees in NSW. Sydney: NSW TAG; 2017
    Google Scholar
  • 4 Baysari MT, Westbrook J, Braithwaite J, Day RO. The role of computerized decision support in reducing errors in selecting medicines for prescription: narrative review. Drug Saf 2011; 34 (04) 289-298
    CrossrefPubMedGoogle Scholar
  • 5 Harrison MI, Koppel R, Bar-Lev S. Unintended consequences of information technologies in health care–an interactive sociotechnical analysis. J Am Med Inform Assoc 2007; 14 (05) 542-549
    CrossrefPubMedGoogle Scholar
  • 6 Westbrook JI, Baysari MT, Li L, Burke R, Richardson KL, Day RO. The safety of electronic prescribing: manifestations, mechanisms, and rates of system-related errors associated with two commercial systems in hospitals. J Am Med Inform Assoc 2013; 20 (06) 1159-1167
    CrossrefPubMedGoogle Scholar
  • 7 Sittig DF, Singh H. Defining health information technology-related errors: new developments since to err is human. Arch Intern Med 2011; 171 (14) 1281-1284
    CrossrefPubMedGoogle Scholar
  • 8 Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2006; 13 (05) 547-556
    CrossrefPubMedGoogle Scholar
  • 9 Armada ER, Villamañán E, López-de-Sá E. et al. Computerized physician order entry in the cardiac intensive care unit: effects on prescription errors and workflow conditions. J Crit Care 2014; 29 (02) 188-193
    CrossrefPubMedGoogle Scholar
  • 10 Mills PR, Weidmann AE, Stewart D. Hospital staff views of prescribing and discharge communication before and after electronic prescribing system implementation. Int J Clin Pharm 2017; 39 (06) 1320-1330
    CrossrefPubMedGoogle Scholar
  • 11 Warrick C, Naik H, Avis S, Fletcher P, Franklin BD, Inwald D. A clinical information system reduces medication errors in paediatric intensive care. Intensive Care Med 2011; 37 (04) 691-694
    CrossrefPubMedGoogle Scholar
  • 12 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
    CrossrefPubMedGoogle Scholar
  • 13 Pontefract SK, Hodson J, Slee A. et al. Impact of a commercial order entry system on prescribing errors amenable to computerised decision support in the hospital setting: a prospective pre-post study. BMJ Qual Saf 2018; 27 (09) 725-736
    CrossrefPubMedGoogle Scholar
  • 14 Hernandez F, Majoul E, Montes-Palacios C. et al. An observational study of the impact of a computerized physician order entry system on the rate of medication errors in an orthopaedic surgery unit. PLoS One 2015; 10 (07) e0134101
    CrossrefPubMedGoogle Scholar
  • 15 Brown CL, Mulcaster HL, Triffitt KL. et al. A systematic review of the types and causes of prescribing errors generated from using computerized provider order entry systems in primary and secondary care. J Am Med Inform Assoc 2017; 24 (02) 432-440
    CrossrefPubMedGoogle Scholar
  • 16 Magrabi F, Ong MS, Runciman W, Coiera E. An analysis of computer-related patient safety incidents to inform the development of a classification. J Am Med Inform Assoc 2010; 17 (06) 663-670
    CrossrefPubMedGoogle Scholar
  • 17 Debono D, Taylor N, Lipworth W. et al. Applying the Theoretical Domains Framework to identify barriers and targeted interventions to enhance nurses' use of electronic medication management systems in two Australian hospitals. Implement Sci 2017; 12 (01) 42
    CrossrefPubMedGoogle Scholar
  • 18 Baysari MT, Hardie RA, Lake R. et al. Longitudinal study of user experiences of a CPOE system in a pediatric hospital. Int J Med Inform 2018; 109: 5-14
    CrossrefPubMedGoogle Scholar
  • 19 Niazkhani Z, Pirnejad H, van der Sijs H, de Bont A, Aarts J. Computerized provider order entry system–does it support the inter-professional medication process? Lessons from a Dutch academic hospital. Methods Inf Med 2010; 49 (01) 20-27
    Article in Thieme ConnectPubMedGoogle Scholar
  • 20 Singh H, Mani S, Espadas D, Petersen N, Franklin V, Petersen LA. Prescription errors and outcomes related to inconsistent information transmitted through computerized order entry: a prospective study. Arch Intern Med 2009; 169 (10) 982-989
    CrossrefPubMedGoogle Scholar
  • 21 Slight SP, Eguale T, Amato MG. et al. The vulnerabilities of computerized physician order entry systems: a qualitative study. J Am Med Inform Assoc 2016; 23 (02) 311-316
    CrossrefPubMedGoogle Scholar
  • 22 Lichtner V, Baysari M, Gates P, Dalla-Pozza L, Westbrook JI. Medication safety incidents in paediatric oncology after electronic medication management system implementation. Eur J Cancer Care (Engl) 2019; 28 (06) e13152
    CrossrefPubMedGoogle Scholar
  • 23 Kinlay M, Zheng WY, Burke R, Juraskova I, Moles R, Baysari M. Medication errors related to computerized provider order entry systems in hospitals and how they change over time: a narrative review. Res Social Adm Pharm 2021; 17 (09) 1546-1552
    CrossrefPubMedGoogle Scholar
  • 24 Westbrook JI, Lichtner V. Why is measuring the effects of information technology on medication errors so difficult?. Lancet Digit Health 2019; 1 (08) e378-e379
    CrossrefPubMedGoogle Scholar
  • 25 Williams J, Bates DW, Sheikh A. Optimising electronic prescribing in hospitals: a scoping review protocol. BMJ Health Care Inform 2020; 27 (01) e100117
    CrossrefPubMedGoogle Scholar
  • 26 Australian Commission on Safety and Quality in Health Care. A Health IT-Related Classification System. Sydney: ACSQHC; 2019
    Google Scholar
  • 27 Australian Commission on Safety and Quality in Health Care. A Medicine Incident Classification System. Sydney: ACSQHC; 2019
    Google Scholar
  • 28 Amato MG, Salazar A, Hickman TT. et al. Computerized prescriber order entry-related patient safety reports: analysis of 2522 medication errors. J Am Med Inform Assoc 2017; 24 (02) 316-322
    CrossrefPubMedGoogle Scholar
  • 29 Magrabi F, Baker M, Sinha I. et al. Clinical safety of England's national programme for IT: a retrospective analysis of all reported safety events 2005 to 2011. Int J Med Inform 2015; 84 (03) 198-206
    CrossrefPubMedGoogle Scholar
  • 30 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
    CrossrefPubMedGoogle Scholar
  • 31 Kim MO, Coiera E, Magrabi F. Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. J Am Med Inform Assoc 2017; 24 (02) 246-250
    CrossrefPubMedGoogle Scholar
  • 32 Magrabi F, Liaw ST, Arachi D, Runciman W, Coiera E, Kidd MR. Identifying patient safety problems associated with information technology in general practice: an analysis of incident reports. BMJ Qual Saf 2016; 25 (11) 870-880
    CrossrefPubMedGoogle Scholar
  • 33 Cohen MR. Medication Errors. Washington, DC: American Pharmacist Association; 2007
    CrossrefGoogle Scholar
  • 34 Clinical Excellence Commission. High-Risk Medicines Management. Sydney: NSW Health; 2020
    Google Scholar
  • 35 Westbrook JI, Sunderland NS, Woods A, Raban MZ, Gates P, Li L. Changes in medication administration error rates associated with the introduction of electronic medication systems in hospitals: a multisite controlled before and after study. BMJ Health Care Inform 2020; 27 (03) e100170
    CrossrefPubMedGoogle Scholar
  • 36 Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc 2013; 20 (03) 470-476
    CrossrefPubMedGoogle Scholar
  • 37 Van de Vreede M, McGrath A, de Clifford J. Review of medication errors that are new or likely to occur more frequently with electronic medication management systems. Aust Health Rev 2019; 43 (03) 276-283
    CrossrefPubMedGoogle Scholar
  • 38 Holmgren AJ, Co Z, Newmark L, Danforth M, Classen D, Bates D. Assessing the safety of electronic health records: a national longitudinal study of medication-related decision support. BMJ Qual Saf 2020; 29 (01) 52-59
    CrossrefPubMedGoogle Scholar
  • 39 Payne TH. EHR-related alert fatigue: minimal progress to date, but much more can be done. BMJ Qual Saf 2019; 28 (01) 1-2
    CrossrefPubMedGoogle Scholar
  • 40 Baysari MT, Tariq A, Day RO, Westbrook JI. Alert override as a habitual behavior - a new perspective on a persistent problem. J Am Med Inform Assoc 2017; 24 (02) 409-412
    CrossrefPubMedGoogle Scholar
  • 41 McGreevey III JD, Mallozzi CP, Perkins RM, Shelov E, Schreiber R. Reducing alert burden in electronic health records: state of the art recommendations from four health systems. Appl Clin Inform 2020; 11 (01) 1-12
    Article in Thieme ConnectPubMedGoogle Scholar
  • 42 Slight SP, Tolley CL, Bates DW. et al. Medication errors and adverse drug events in a UK hospital during the optimisation of electronic prescriptions: a prospective observational study. Lancet Digit Health 2019; 1 (08) e403-e412
    CrossrefPubMedGoogle Scholar
  • 43 Cresswell KM, Bates DW, Williams R. et al. Evaluation of medium-term consequences of implementing commercial computerized physician order entry and clinical decision support prescribing systems in two ‘early adopter’ hospitals. J Am Med Inform Assoc 2014; 21 (Suppl. 02) e194-e202
    CrossrefPubMedGoogle Scholar
  • 44 Schiff GD, Amato MG, Eguale T. et al. Computerised physician order entry-related medication errors: analysis of reported errors and vulnerability testing of current systems. BMJ Qual Saf 2015; 24 (04) 264-271
    CrossrefPubMedGoogle Scholar
  • 45 Odukoya OK, Chui MA. Relationship between e-prescriptions and community pharmacy workflow. J Am Pharm Assoc (2003) 2012; 52 (06) e168-e174
    CrossrefPubMedGoogle Scholar
  • 46 Khajouei R, Jaspers MW. The impact of CPOE medication systems' design aspects on usability, workflow and medication orders: a systematic review. Methods Inf Med 2010; 49 (01) 3-19
    Article in Thieme ConnectPubMedGoogle Scholar
  • 47 Nielsen J. Enhancing the explanatory power of usability heuristics. Paper presented at: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; April 24, 1994; Boston, Massachusetts, United States
    PubMedGoogle Scholar
  • 48 Blecker S, Pandya R, Stork S. et al. Interruptive versus noninterruptive clinical decision support: Usability study. JMIR Human Factors 2019; 6 (02) e12469-e12469
    CrossrefPubMedGoogle Scholar
  • 49 Teich JM, Osheroff JA, Pifer EA, Sittig DF, Jenders RA, Panel CDSER. CDS Expert Review Panel. Clinical decision support in electronic prescribing: recommendations and an action plan: report of the joint clinical decision support workgroup. J Am Med Inform Assoc 2005; 12 (04) 365-376
    CrossrefPubMedGoogle Scholar
  • 50 Institute for Safe Medication Practices (ISMP). (US). Your high-alert medication list: relatively useless without associated risk-reduction strategies. 2013. Accessed 29 April 2021 at: https://www.ismp.org/resources/your-high-alert-medication-list-relatively-useless-without-associated-risk-reduction?id=45
    PubMedGoogle Scholar
  • 51 Australian Commission on Safety and Quality in Health Care. Electronic Medication Management Systems: A Guide to Safe Implementation. 3rd edn.. Sydney: ACSQHC; 2019
    Google Scholar
  • 52 Baysari MT, Raban MZ. The safety of computerised prescribing in hospitals. Aust Prescr 2019; 42 (04) 136-138
    CrossrefPubMedGoogle Scholar
  • 53 Sittig DF, Campbell E, Guappone K, Dykstra R, Ash JS. Recommendations for monitoring and evaluation of in-patient computer-based provider order entry systems: results of a Delphi survey. AMIA Annu Symp Proc 2007; 2007: 671-675
    PubMedGoogle Scholar