Improving hospital drug safety - identification of medication errors and subsequent development, implementation and outcome evaluation of alert algorithms for their targeted prevention

David Franklin Niedrig
Any drug prescription requires careful weighting of risks vs. benefits. Failure to do so or to ignore known contraindications, recommended dose-adjustments and other precautions represents a medication error that may result in adverse drug events, i.e. harm to the patient. Clinical decision support systems can routinely detect potential medication errors and issue automated alerts for their prevention. However, current systems typically focus on high sensitivity at the price of low specificity
more » ... of low specificity regarding relevance of their alerts. In clinical practice this results in an excessive number of alerts to prescribers with subsequent alert fatigue and indiscriminate alert overriding, i.e. even important warnings are ignored. So medication errors continue to be a theoretically avoidable yet persistent burden for healthcare systems and patients. Therefore this thesis pursued the following objectives: I) Systematic quantification of potential medication errors in a real-life hospital setting. II) Validation of the clinical relevance of selected potential medication errors and associated adverse events. III) Development, implementation and outcome assessment not only of highly sensitive but also of highly specific alert algorithms for the prevention of clinically relevant medication errors. I) Two studies were performed in order to systematically quantify potential medication errors: A local pharmacoepidemiological database including 6.6 million drug administrations during approximately 82000 hospitalizations was successfully developed based on raw data extracted from the electronic medical records system of a tertiary care hospital. After its validation, highly efficient algorithms were developed that identified potential medication errors. They allowed the retrospective assessment of a considerable number of contraindicated and/or critical prescriptions. Sensitivity and specificity regarding clinical relevance of these potential medication errors was enhanced by the use of additional patient-specific laboratory data and repeated clinical validation procedures. With the help of a newly developed interface with ID PHARMA CHECK® -a commercially available clinical decision support system -several ten thousand potential drug interactions, contraindications and dosing errors were identified and assigned to formal severity categories. 48 distinct contraindicated drug interactions were considered as clinically relevant and suitable for display of highly specific alerts within a clinical information systems; 32 alert algorithms required retrieval and implementation of current patient-specific information such as laboratory results in order to reach high specificity. The resulting algorithms were subsequently programmed for routine use with the clinical decision support system. II) Three sub-studies were conducted within the pharmacoepidemiological database and addressed specific safety concerns of pharmacotherapy in clinical practice: The first of these studies identified 1136 hospitalizations with exposure to second-generation antipsychotics. Blood pressure, blood glucose, lipids and body mass index should be routinely monitored in those patients, however they were found to be documented in 97.7, 75.7, 24.6 and 77.4 % of hospitalizations, respectively. 63.4, 70.8 and 37.1% of the patients with hyperglycemia, dyslipidemia and hypertension, respectively, had no pharmacotherapy for these conditions. Among patients exposed to second-generation antipsychotics and concomitant use of drugs featuring a high risk for potentially severe adverse drug events, one case with associated neutropenia and four cases with abnormal QTc-interval were detected. Specific monitoring for such adverse drug events was not performed in 89.8% of patients with related high-risk drug combinations. The second sub-study analyzed the use of benzodiazepines (including "Z-drugs") that were 3.6.2. Remarks 90 3.6.3. Background 91 3.6.4. Objectives 91 3.6.5. Methods 91 3.6.6. Results 92 3.6.7. Conclusion 95 3.7. Development, Implementation and Outcome Analysis of Semi-Automated
doi:10.5167/uzh-124894 fatcat:v3acmwcsxvgm3ca2ziur7sn3ce