WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Drug–drug interactions (DDIs) may lead to often preventable adverse drug events and health damage. • In Dutch community pharmacies approximately 6% of all prescriptions generate a DDI alert. • Hospitalized patients may be especially at risk, as they are more severely ill and multiple medications may be prescribed simultaneously; however, only limited data are available on the frequency and nature of DDIs during hospitalization. WHAT THIS STUDY ADDS • In a Dutch university hospital 10% of all prescriptions generated a DDI alert; overall 25% of patients encountered at least one potential DDI. • Besides the risk of decreased effectiveness (25% of the DDIs), the most frequently occurring potential clinical consequence of the DDIs was an increased risk of side‐effects, such as an increased bleeding risk (22% of DDIs), hypotension (15%) and nephrotoxicity (13%). • Almost half of the DDIs could be managed by monitoring laboratory values. AIM Drug–drug interactions (DDIs) may lead to often preventable adverse drug events and health damage. Especially within hospitals, this might be an important factor, as patients are severely ill and multiple medications may be prescribed simultaneously. The objective of this study was to measure the frequency and nature of DDI alerts in a Dutch university hospital. METHODS All patients hospitalized in the University Medical Centre Utrecht in 2006 who were prescribed at least one medication were included. The frequency of DDIs was calculated as: (i) the percentage of patients experiencing at least one DDI, and (ii) the percentage of prescriptions generating a DDI alert. Based on the national professional guideline, DDIs were classified into categories of potential clinical outcome, management advice, clinical relevance (A–F) and available evidence (0–4). RESULTS Of the 21 277 admissions included, 5909 (27.8%) encountered at least one DDI. Overall, the prescribing physician received a DDI alert in 9.6% of all prescriptions. The most frequently occurring potential clinical consequence of the DDIs was an increased risk of side‐effects such as increased bleeding risk (22.0%), hypotension (14.9%), nephrotoxicity (12.6%) and electrolyte disturbances (10.5%). Almost half (48.6%) of the DDIs could be managed by monitoring laboratory values. CONCLUSIONS Computerized DDI alerts may be a useful tool to prevent adverse drug events within hospitals, but they may also result in ‘alert fatigue’. The specificity of alerts could significantly improve by the use of more sophisticated clinical decision support systems taking into account, for example, laboratory values.

Transfer of automated laboratory data collected during routine clinical care from the laboratory information system into a database format that enables linkage to other administrative (e.g., patient characteristics) or clinical (e.g., medication, diagnoses, procedures) data provides a valuable tool for clinical epidemiological research. It allows the investigation of biochemical characteristics of diseases, therapeutic effects and diagnostic and/or prognostic markers for disease with easy access and at relatively low cost. To this end, the Utrecht Patient Oriented Database (UPOD), an infrastructure of relational databases comprising data on patient characteristics, laboratory test results, medication orders, hospital discharge diagnoses and medical procedures for all patients treated at the University Medical Centre Utrecht since January 2004, was established. Current research within UPOD is focused on the innovative linkage of laboratory and medication data, which, for example, makes it possible to assess the quality of pharmacotherapy in clinical practice, to investigate interference between laboratory tests and drugs, to study the risk of adverse drug reactions, and to develop diagnostic and prognostic markers or algorithms for adverse drug reactions. Although recently established, we believe that UPOD broadens the opportunities for clinical pharmacoepidemiological research and can