Impact of a commercial order entry system on prescribing errors amenable to computerised decision support in the hospital setting: a prospective pre-post study

Abstract

Background: In this UK study, we investigated the impact of computerised physician order entry (CPOE) and clinical decision support (CDS) implementation on the rate of 78 high-risk prescribing errors amenable to CDS.

Methods: We conducted a preintervention/postintervention study in three acute hospitals in England. A predefined list of prescribing errors was incorporated into an audit tool. At each site, approximately 4000 prescriptions were reviewed both pre-CPOE and 6 months post-CPOE implementation. The number of opportunities for error and the number of errors that occurred were collated. Error rates were then calculated and compared between periods, as well as by the level of CDS.

Results: The prescriptions of 1244 patients were audited pre-CPOE and 1178 post-CPOE implementation. A total of 28 526 prescriptions were reviewed, with 21 138 opportunities for error identified based on 78 defined errors. Across the three sites, for those prescriptions where opportunities for error were identified, the error rate was found to reduce significantly post-CPOE implementation, from 5.0% to 4.0% (P<0.001). CDS implementation by error type was found to differ significantly between sites, ranging from 0% to 88% across clinical contraindication, dose/frequency, drug interactions and other error types (P<0.001). Overall, 43/78 (55%) of the errors had some degree of CDS implemented in at least one of the hospitals.

Conclusions: Implementation of CPOE with CDS was associated with clinically important reductions in the rate of high-risk prescribing errors. Given the pre-post design, these findings however need to be interpreted with caution. The occurrence of errors was found to be highly dependent on the level of restriction of CDS presented to the prescriber, with the effect that different configurations of the same CPOE system can produce very different results.

Keywords: decision support, clinical; decision support, computerized; hospital medicine; medication safety.

Figure 1

iMPACT data capture tool: prescribing errors. Drugs and drug classes are categorised for ease of use. iMPACT, Investigating Medication Prescribing Accuracy for Critical error Types.

Figure 2

Capturing drug and drug class data on iMPACT. Clicking on a drug or drug class reveals a series of questions about the prescription and/or the patient’s medical and drug history. iMPACT, Investigating Medication Prescribing Accuracy for Critical error Types.

Figure 3

Implementation of any level of CDS by hospital site. P values are from Fisher’s exact test, and bold P values are significant at P<0.05. CDS, clinical decision support.

Figure 4

Implementation of passive and interruptive CDS at the hospital sites. * Passive CDS at Site 3 = 4%. CDS, clinical decision support.