The proportion of defined daily doses of CredibleMeds ® drugs among the top 3000 prescribed medicines increased 4.6fold from 2013 (4.6%) to 2019 (20.9%) in Germany, largely due to newly listed drugs in CredibleMeds ® . Major discrepancies between German SmPCs/US Prescribing Information and CredibleMeds ® exist and pose a risk for medication safety.

Contraindications (CIs) in Summaries of Product Characteristics (SmPCs)/Prescribing Information (PI) that lack clarity may pose a risk to medication safety and increase the risk for adverse drug reactions. We assessed and compared SmPCs/PI from three major drug markets regarding comprehensibility from the prescriber perspective, as well as usability in clinical decision support systems. 158 drugs met the following inclusion criteria: marketed in Germany (DE), United Kingdom (UK) and United States (US) and belonged to the 100 most recently FDA approved and/or 100 most frequently prescribed drugs in either country. In the 474 (3 × 158) SmPCs/PI all expressions for absolute CIs were identified, divided into 3999 stand-alone terms and evaluated according to ‘clarity’ and ‘codability’. The average number of absolute CIs per drug differed drastically between the three markets (DE: 11.7, UK: 9.0, US: 4.6). Expressions were frequently unclear (DE: 27.2% (95% CI 25.2–29.2%), UK: 28.5% (26.2–30.9%), US: 22.6% (19.7–25.8%)). Moreover, 60.9% (58.6–63.1%), 63.6% (61.0–66.0%), and 64.7% (61.2–68.1%) of the expressions were not codable in DE, UK, and US, respectively. Taken together, in three major drug markets, statements regarding CIs in SmPCs/PI substantially differ in frequency and frequently lack clarity and codability which poses an unnecessary obstacle to medication safety.

Drug-related problems (DRPs), i.e., adverse drug reactions (ADRs) and medication errors (MEs), constitute a serious threat to the patient’s safety. DRPs are often insufficiently captured by clinical routine documentation, and thus, they frequently remain unaddressed. The aim of this study was to assess the coverage and usability of the new 11th revision of the WHO International Classification of Diseases (ICD-11) to document DRPs. We refined the ‘Quality and Safety Algorithm’ from the ICD-11 Reference Guide and used it for DRP reporting to code 100 different anonymized DRPs (50 ADRs and 50 MEs) in a German hospital. The ICD-11 three-part model consisting of harm, cause, and mode was used whenever they were applicable. Of 50 ADRs, 15 (30.0%), such as drug-induced osteoporosis, were fully classifiable and codable by the ICD-11, whereas 35 (70.0%), such as drug-induced hypokalaemia, could not be fully classified due to sanctioning rules preventing the postcoordination (i.e., a combination of specific codes, such as drug and diagnosis). However, coding without the loss of information was possible in the 35 of these 35 (100.0%) ADR cases when we were deviating from the cluster code order of the Reference Guide. In all 50 MEs, the mode could be encoded, but for none of the MEs, postcoordination, i.e., the assignment of the ME to a specific drug, was allowed. In conclusion, the ICD-11 three-part model enables us to acquire more detailed documentation of DRPs than the previous ICD versions did. However, the codability, documentation, and reporting of DRPs could be significantly improved by simple modifications of the current ICD-11 sanctioning rules and by the addition of new ICD-11 codes.

Aims Prescribing information should follow a defined structure to help prescribers easily find required information. Often information appears in different sections of Summaries of Product Characteristics (SmPCs) in an inconsistent way. Still unknown is how this inconsistency affects absolute contraindications and how it can be improved. Thisstudy aimed to evaluate the structure of absolute contraindications in SmPCs based on absolute drug–drug contraindications (DDCI) in the section ‘contraindications’ and references to sections ‘special warnings and precautions for use’ (here as ‘warnings’) and ‘interaction with other medicinal products and other forms of interaction’ (here as ‘interactions’). Methods SmPCs of 693 commonly prescribed drugs were analysed regarding absolute DDCI in ‘contraindications’ sections. References to sections on ‘warnings’ and ‘interactions’ were evaluated to characterize information provided about DDCI. Results Of 693 analysed SmPCs, 138 (19.9%) contained ≥1 absolute DDCI. Of 178 SmPCs that referred to sections on ‘warnings’ or ‘interactions’, 131 (73.6%) did not contain further information on absolute DDCI, whereas 47 (26.4%) did. Such additional information was found in sections on ‘interactions’ and ‘warnings’ in 41 (87.2%) and 9 (19.1%) SmPCs, respectively. Conclusions Information regarding absolute DDCI was found not only in sections on ‘contraindications’ but also in sections on ‘warnings’ and ‘interactions’. Information was not given with consistently straightforward phrasing and structure and so can leave uncertainty for prescribers. To improve drug safety, clear definitions and wording for absolute and relative contraindications should be provided, ideally in tables.

Aims Automated checks for medication‐related problems have become a cornerstone of medication safety. In many clinical settings medication checks remain confined to drug–drug interactions because only medication data are available in an adequately coded form, leaving possible contraindicated drug–disease combinations unaccounted for. Therefore, we devised algorithms that identify frequently contraindicated diagnoses based on medication patterns related to these diagnoses. Methods We identified drugs that are associated with diseases constituting common contraindications based on their exclusive use for these conditions (such as allopurinol for gout or salbutamol for bronchial obstruction). Expert‐based and machine learning algorithms were developed to identify diagnoses based on highly specific medication patterns. The applicability, sensitivity and specificity of the approach were assessed by using an anonymized real‐life sample of medication and diagnosis data excerpts from 3506 discharge records of geriatric patients. Results Depending on the algorithm, the desired focus (i.e., sensitivity vs. specificity) and the disease, we were able to identify the diagnoses gout, epilepsy, coronary artery disease, congestive heart failure and bronchial obstruction with a specificity of 44.0–99.8% (95% CI 41.7–100.0%) and a sensitivity of 3.8–83.1% (95% CI 1.0–86.1%). Using only medication data, we were able to identify 123 (51.3%) of 240 contraindications identified by experts with access to medication data and diagnoses. Conclusion This study provides a proof of principle that some key diagnosis‐related contraindications can be identified based on a patient's medication data alone, while others cannot be identified. This approach offers new opportunities to analyse drug–disease contraindications in community pharmacy or clinical routine data.