The impact of an integrated electronic medication prescribing and dispensing system on prescribing and dispensing errors: a before and after study

Abstract

Background Medication errors are a significant problem in Australian hospitals. This is the first study of the impact of an integrated electronic medication prescribing (e‐prescribing) and dispensing system on medication errors. Aim To evaluate the impact of an integrated e‐prescribing and dispensing system implemented in outpatient clinics and the emergency department on prescribing and dispensing errors. Method Prescribing and dispensing errors were identified over 2 weeks before and after implementation of the integrated system. Errors were identified through original prescription, e‐prescribing and dispensing systems review. Error rates, types, severity and patient waiting times for prescription dispensing were compared. Results The study included 379 patients prescribed 654 medicines in the before group and 375 patients prescribed 635 medicines in the after group. Prescribing and dispensing error rates per patient were reduced by 93% (from 2.98 to 0.2; p < 0.0001) and 98% (from 1.27 to 0.03; p < 0.0001), respectively. The proportions of patients with at least one procedural or clinical prescribing error showed significant reductions of 81% (95% CI 77–85%) and 3.4% (95% CI 0.1–6.7%). The proportions of patients with at least one procedural or clinical dispensing error showed signicant reductions of 61% (94% CI 56–66%) and 2% (95% CI 0.7–3.6%). Fifty system‐related prescribing errors were identified. Mean error severity score was minor in both groups. Median patient waiting time for prescription dispensing was reduced by 52% (from 27 to 13 min; p < 0.0001). Conclusion Implementation of the integrated system significantly reduced prescribing and dispensing errors and patient waiting times for prescription dispensing. Clinicians need to manage new system‐related errors to reduce associated risks.

Prescribing errors and adverse drug reaction documentation before and after implementation of e‐prescribing using the Enterprise Patient Administration System

“…Introduction of the standardised NIMC decreased prescribing errors between 5% and 15% of medication orders per patient . Recent evidence suggests that the positive effect of e‐prescribing may be greater, with decreases in prescribing errors of 50% or greater compared with paper‐based systems . These Australian studies agree with the international data that support the effectiveness of standardised medication charts and e‐prescribing in decreasing the frequency of prescribing errors in hospital …”

“…3,7 Recent evidence suggests that the positive effect of e-prescribing may be greater, with decreases in prescribing errors of 50% or greater compared with paper-based systems. 4,5,8 These Australian studies agree with the international data that support the effectiveness of standardised medication charts and e-prescribing in decreasing the frequency of prescribing errors in hospital. 9,10 The Enterprise Patient Administration System (EPAS) is an electronic health record with e-prescribing functionality that is currently being implemented in South Australian (SA) public hospitals.…”

The impact of health information technology on prescribing errors in hospitals: a systematic review and behaviour change technique analysis

“…Twenty studies focused on adults [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56], ten on a paediatric or neonatal population [57][58][59][60][61][62][63][64][65][66], and five included a mixed population [67][68][69][70][71] (see Table 1). Ten studies were based in the US [37,41,42,48,54,57,58,64,67,70], eight in the UK [39,45,49,50,53,…”

Standardising the Classification of Harm Associated with Medication Errors: The Harm Associated with Medication Error Classification (HAMEC)

“…Different author groups have referred to harm that could be potentially caused by a medication error in various (often interchangeable) and inconsistent ways. These include ‘likely harm’ [61, 62], a ‘potential consequence’ [63, 64], an ‘assumed consequence’ [65], ‘predicted outcome’ [66], ‘potential risk’ [67–69], and/or ‘potential severity’ [70]. Adding to this inconsistency are studies that refer to errors with the potential to harm a patient as either ‘clinically significant’ [61, 71–75], ‘major’ [70, 76–78], ‘serious’ [42] [79, 80], ‘important’ [81], ‘problematic’ [74] and/or ‘high risk’ [82].…”