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

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.

Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains.

BACKGROUND: Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. OBJECTIVES: We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. METHODS: Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). RESULTS: Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. CONCLUSIONS: Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors.

A case study in generic drug use: should there be risk adjustment in incentive payments for the use of generic medications?

BACKGROUND: Encouraging generic drug use has reduced health care costs for payers and consumers, but the availability of therapeutically interchangeable medications or generic medications of choice is not equal across disease states. The extent to which systems of care are able to substitute with generics is not well understood.  OBJECTIVES: To (a) define and measure the maximum generic rate (MGR) of currently prescribed drugs within an academic medical group in and (b) illustrate differences across drugs associated with selected underlying diseases.   METHODS: Prescription claims data were examined from an academic medical group in Chicago, Illinois. Based on pharmacologic and therapeutic criteria, drugs were classified into 2 categories-potentially substitutable and not potentially substitutable-based on whether the drugs are branded forms of the same chemical entities that are available as generics or are therapeutically interchangeable with other medications that have different chemical compositions but the same mechanisms of action and potential efficacy. A medication was considered potentially substitutable if it (a) did not have a narrow therapeutic index as defined by the FDA; (b) did not belong to 1 of 6 protected classes of drugs in the Medicare D provisions; (c) was substitutable with a generic medication containing the same chemical entity; or (d) was therapeutically interchangeable with a therapeutically equivalent medication. MGR was defined as the percentage of prescriptions that could potentially be prescribed in generic form. This rate was examined overall and across drugs known to be associated with illustrative diseases including hypertension, diabetes mellitus, and obstructive lung diseases.   RESULTS: The MGR ranged from 100% for drugs used in hypertension to 26.7% for drugs used in obstructive lung diseases. The MGR was 83.6%.  CONCLUSIONS: Payers wishing to promote generic substitution should incorporate the potential for substitution of clinically appropriate generic medications as part of incentives for generic utilization to avoid unintended consequences of using a fixed target rate. A practical methodology for determining an MGR is offered.