Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Australian Health Review Australian Health Review Society
Journal of the Australian Healthcare & Hospitals Association
RESEARCH ARTICLE (Open Access)

Review of medication errors that are new or likely to occur more frequently with electronic medication management systems

Melita Van de Vreede A D , Anne McGrath B and Jan de Clifford C
+ Author Affiliations
- Author Affiliations

A Eastern Health, Box Hill Hospital Pharmacy, Nelson Road, Box Hill, Vic. 3128, Australia.

B Pharmacy Department, Austin Health, 145 Studley Road, Heidelberg, Vic. 3084, Australia. Email: anne.mcgrath@austin.org.au

C Peninsula Health, Frankston Hospital Pharmacy Department, Hastings Road, Frankston, Vic. 3199, Australia. Email: jdeclifford@phcn.vic.gov.au

D Corresponding author. Email: melita.vreede@gmail.com

Australian Health Review 43(3) 276-283 https://doi.org/10.1071/AH17119
Submitted: 9 May 2017  Accepted: 22 January 2018   Published: 14 May 2018

Journal Compilation © AHHA 2019 Open Access CC BY-NC-ND

Abstract

Objective The aim of the present study was to identify and quantify medication errors reportedly related to electronic medication management systems (eMMS) and those considered likely to occur more frequently with eMMS. This included developing a new classification system relevant to eMMS errors.

Methods Eight Victorian hospitals with eMMS participated in a retrospective audit of reported medication incidents from their incident reporting databases between May and July 2014. Site-appointed project officers submitted deidentified incidents they deemed new or likely to occur more frequently due to eMMS, together with the Incident Severity Rating (ISR). The authors reviewed and classified incidents.

Results There were 5826 medication-related incidents reported. In total, 93 (47 prescribing errors, 46 administration errors) were identified as new or potentially related to eMMS. Only one ISR 2 (moderate) and no ISR 1 (severe or death) errors were reported, so harm to patients in this 3-month period was minimal. The most commonly reported error types were ‘human factors’ and ‘unfamiliarity or training’ (70%) and ‘cross-encounter or hybrid system errors’ (22%).

Conclusions Although the results suggest that the errors reported were of low severity, organisations must remain vigilant to the risk of new errors and avoid the assumption that eMMS is the panacea to all medication error issues.

What is known about the topic? eMMS have been shown to reduce some types of medication errors, but it has been reported that some new medication errors have been identified and some are likely to occur more frequently with eMMS. There are few published Australian studies that have reported on medication error types that are likely to occur more frequently with eMMS in more than one organisation and that include administration and prescribing errors.

What does this paper add? This paper includes a new simple classification system for eMMS that is useful and outlines the most commonly reported incident types and can inform organisations and vendors on possible eMMS improvements. The paper suggests a new classification system for eMMS medication errors.

What are the implications for practitioners? The results of the present study will highlight to organisations the need for ongoing review of system design, refinement of workflow issues, staff education and training and reporting and monitoring of errors.


References

[1]  Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U. The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. J Am Med Inform Assoc 2008; 15 585–600.
The effect of electronic prescribing on medication errors and adverse drug events: a systematic review.Crossref | GoogleScholarGoogle Scholar |

[2]  Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003; 163 1409–1416.
Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review.Crossref | GoogleScholarGoogle Scholar |

[3]  Reckmann MH, Westbrook JI, Koh Y, Lo C, Day RO. Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review. J Am Med Inform Assoc 2009; 16 613–23.
Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review.Crossref | GoogleScholarGoogle Scholar |

[4]  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 1159–67.
The safety of electronic prescribing: manifestations, mechanisms, and rates of system-related errors associated with two commercial systems in hospitals.Crossref | GoogleScholarGoogle Scholar |

[5]  Meeks DW, Smith MW, Taylor L, Sittig DF, Scott JM, Singh H. An analysis of electronic health record-related patient safety concerns. J Am Med Inform Assoc 2014; 21 1053–9.
An analysis of electronic health record-related patient safety concerns.Crossref | GoogleScholarGoogle Scholar |

[6]  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; 13 547–56.
Types of unintended consequences related to computerized provider order entry.Crossref | GoogleScholarGoogle Scholar |

[7]  Committee on Patient Safety and Health Information Technology. Health IT and patient safety. Building safer systems for better care. Institute of Medicine Report. 2012. Available at: http://www.nap.edu/read/13269/chapter/1 [verified 15 April 2018].

[8]  Electronic Medication Management Systems. A guide to safe implementation, 2nd edn. 2012. Available at: http://www.safetyandquality.gov.au/publications/electronic-medication-management-systems-a-guide-to-safe-implementation/ [verified 15 April 2018].

[9]  Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. J Am Med Inform Assoc 2012; 19 121–7.
Automation bias: a systematic review of frequency, effect mediators, and mitigators.Crossref | GoogleScholarGoogle Scholar |

[10]  Coiera E. Technology, cognition and error. BMJ Qual Saf 2015; 24 417–22.
Technology, cognition and error.Crossref | GoogleScholarGoogle Scholar |

[11]  Westbrook JI, Reckmann M, Li L, Runciman WB, Burke R, Lo C, Baysari MT, Braithwaite J, Day RO. Effects of two commercial electronic prescribing systems on prescribing error rates in hospital in-patients: a before and after study. PLoS Med 2012; 9 e1001164
Effects of two commercial electronic prescribing systems on prescribing error rates in hospital in-patients: a before and after study.Crossref | GoogleScholarGoogle Scholar |

[12]  Westbrook JI, Li L, Lehnbom EC, Baysari MT, Braithwaite J, Burke R, Conn C, Day RO. What are incident reports telling us? A comparative study at two Australian hospitals of medication errors identified at audit, detected by staff and reported to an incident system. Int J Qual Health Care 2015; 27 1–9.
What are incident reports telling us? A comparative study at two Australian hospitals of medication errors identified at audit, detected by staff and reported to an incident system.Crossref | GoogleScholarGoogle Scholar |

[13]  Van de Vreede MA, de Clifford JM, McGrath A. Staff experience and perceptions of the safety and risks of electronic medication management systems in Victorian public hospitals. J Pharm Pract Res 2018; 48 18–25.
Staff experience and perceptions of the safety and risks of electronic medication management systems in Victorian public hospitals.Crossref | GoogleScholarGoogle Scholar |

[14]  Schiff GD, Amato MG, Eguale T, Boehne JJ, Wright A, Koppel R, Rasdidee H, Elson RB, Whitney DL, Thach T-T, Bates DW, Seger AC. Computerised physician order entry-related medication errors: analysis of reported errors and vulnerability testing of current systems. BMJ Qual Saf 2015; 24 264–71.
Computerised physician order entry-related medication errors: analysis of reported errors and vulnerability testing of current systems.Crossref | GoogleScholarGoogle Scholar |

[15]  Mozaffar H, Cresswell KM, Williams R, Bates DW, Sheikh A. Exploring the roots of unintended safety threats associated with the introduction of hospital ePrescribing systems and candidate avoidance or mitigation strategies: a qualitative study. BMJ Qual Saf 2017; 26 722–33.
Exploring the roots of unintended safety threats associated with the introduction of hospital ePrescribing systems and candidate avoidance or mitigation strategies: a qualitative study.Crossref | GoogleScholarGoogle Scholar |

[16]  Carayon P, Schoofs Hundt A, Karsh B-T, Gurses AP, Alvarado CJ, Smith M, Flatley Brennan P. Work system design for patient safety; the SEIPS model. Qual Saf Health Care 2006; 15 i50–8.
Work system design for patient safety; the SEIPS model.Crossref | GoogleScholarGoogle Scholar |