Reasons for computerised provider order entry (CPOE)-based inpatient medication ordering errors: an observational study of voided orders

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Abstract

Objective

Medication voiding is a computerised provider order entry (CPOE)-based discontinuation mechanism that allows clinicians to identify erroneous medication orders. We investigated the accuracy of voiding as an indicator of clinician identification and interception of a medication ordering error, and investigated reasons and root contributors for medication ordering errors.

Method

Using voided orders identified with a void alert, we conducted interviews with ordering and voiding clinicians, followed by patient chart reviews. A structured coding framework was used to qualitatively analyse the reasons for medication ordering errors. We also compared clinician-CPOE-selected (at time of voiding), clinician-reported (interview) and chart review-based reasons for voiding.

Results

We conducted follow-up interviews on 101 voided orders. The positive predictive value (PPV) of voided orders that were medication ordering errors was 93.1% (95% CI 88.1% to 98.1%, n=94). Using chart review-based reasons as the gold standard, we found that clinician-CPOE-selected reasons were less reflective (PPV=70.2%, 95% CI 61.0% to 79.4%) than clinician-reported (interview) (PPV=86.1%, 95%CI 78.2% to 94.1%) reasons for medication ordering errors. Duplicate (n=44) and improperly composed (n=41) ordering errors were common, often caused by predefined order sets and data entry issues. A striking finding was the use of intentional violations as a mechanism to notify and seek ordering assistance from pharmacy service. Nearly half of the medication ordering errors were voided by pharmacists.

Discussion

We demonstrated that voided orders effectively captured medication ordering errors. The mismatch between clinician-CPOE-selected and the chart review-based reasons for error emphasises the need for developing standardised operational descriptions for medication ordering errors. Such standardisation can help in accurately identifying, tracking, managing and sharing erroneous orders and their root contributors between healthcare institutions, and with patient safety organisations.

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