Aims To demonstrate an epidemiological method to assess predictors of prescribing errors. Methods A retrospective case-control study, comparing prescriptions with and without errors. Results Only prescriber and drug characteristics were associated with errors. Prescriber characteristics were medical specialty (e.g. orthopaedics: OR: 3.4, 95% CI 2.1, 5.4) and prescriber status (e.g. verbal orders transcribed by nursing staff: OR: 2.5, 95% CI 1.8, 3.6). Drug characteristics were dosage form (e.g. inhalation devices: OR: 4.1, 95% CI 2.6, 6.6), therapeutic area (e.g. gastrointestinal tract: OR: 1.7, 95% CI 1.2, 2.4) and continuation of preadmission treatment (Yes: OR: 1.7, 95% CI 1.3, 2.3). Conclusions Other hospitals could use our epidemiological framework to identify their own error predictors. Our ®ndings suggest a focus on speci®c prescribers, dosage forms and therapeutic areas. We also found that prescriptions originating from general practitioners involved errors and therefore, these should be checked when patients are hospitalized.

From this study it can be concluded that prevention of prescribing errors by hospital pharmacy staff results in higher benefits than the costs related to the net time investment.

Other hospitals could use our epidemiological framework to identify their own indicators for non-concurrent prescribing. Our findings suggest tailor-made enforcement of PTG adherence for certain prescribers while conversely, adaptation of the PTGs will be required for some infectious diseases.

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Rational prescribing of anti‐infectives is necessary for reasons of effectiveness, safety, resistance, and costs [1]. The aim was to assess indicators for anti‐infective prescribing not concurrent with regional pharmacotherapeutic treatment guidelines (PTGs) on infectious diseases. The study was carried out in departments of clinical pharmacology in four general teaching hospitals in the Friesland Province, the Netherlands. A retrospective explorative cohort study based on hospital‐wide anti‐infective prescription data of a 2 month cross‐sectional study period (n=1037). Data were univariably and subsequently, multivariably analysed using conditional stepwise logistic regression analysis. Absolute risks (AR), relative risks (RR) and odds ratios (OR) with 95% confidence intervals (95% CI) were estimated for patient, therapy, drug, and prescriber variables considered to be potential indicators for non‐concurrent prescribing (P<0.05). At least 40% of all prescriptions were not concurrent with the PTGs. Overall, non‐concurrence mainly existed of non‐indicated prescribing of (particular) anti‐infectives (24.3%) and prescribing of non‐first choice anti‐infectives (55.2%). Non‐concurrent durations of treatment and dosing issues accounted for 17.2% and 16.2% respectively. Non‐concurrence was associated with empiric therapy (OR 3.4, 95% CI 2.3, 5.1), with certain diagnoses, such as skin and soft tissue (OR 2.9, 95% CI 1.3, 6.5), urinary (OR 2.2, 95% CI 1.2, 4.0), and osteoarthrological infections (OR 5.4, 95% CI 11.6, 18.7), and with prescriptions involving topical dosage forms (RR 2.1, 95% CI 1.6, 2.5), cephalosporins (OR 2.1, 95% CI 1.3, 3.5), macrolides and lincosamides (OR 5.9, 95% CI 2.7, 12.0), and quinolones (OR 2.2, 95% CI 1.2, 4.1). There was also an association with prescribing geriatricians (RR 2.3, 95% CI 1.5, 3.6), surgeons (OR 2.0, 95% CI 1.4, 3.2), pulmonologists (RR 1.6, 96% CI 1.3, 2.1), and urologists (RR 2.0, 95% CI 1.1, 3.07) and, in general, assistant clinicians (RR 1.8, 95% CI 1.2, 3.8). We have provided an epidemiological framework that each hospital can use to evaluate anti‐infective prescribing and assess its own predictors. Our study has been able to confirm expert opinions on supposed predictors [2, 3]. The nature and frequency of our hospitals' non‐concurrence was such that tailor‐made enforcement of PTG adherence will be required for some drug groups whereas for other drug groups the contents of the PTGs will be adapted.