ObjectivesPotentially inappropriate medication (PIM) occurs frequently and is a well-known risk factor for adverse drug events, but its incidence is underestimated in internal medicine. The objective of this study was to develop an electronic prescription-screening checklist to assist residents and young healthcare professionals in PIM detection.DesignFive-step study involving selection of medical domains, literature review and 17 semistructured interviews, a two-round Delphi survey, a forward/back-translation process and an electronic tool development.Setting22 University and general hospitals from Canada, Belgium, France and Switzerland.Participants40 physicians and 25 clinical pharmacists were involved in the study.Agreement with the checklist statements and their usefulness for healthcare professional training were evaluated using two 6-point Likert scales (ranging from 0 to 5).Primary and secondary outcome measuresAgreement and usefulness ratings were defined as: >65% of the experts giving the statement a rating of 4 or 5, during the first Delphi-round and >75% during the second.Results166 statements were generated during the first two steps. Mean agreement and usefulness ratings were 4.32/5 (95% CI 4.28 to 4.36) and 4.11/5 (4.07 to 4.15), respectively, during the first Delphi-round and 4.53/5 (4.51 to 4.56) and 4.36/5 (4.33 to 4.39) during the second (p<0.001). The final checklist includes 160 statements in 17 medical domains and 56 pathologies. An algorithm of approximately 31 000 lines was developed including comorbidities and medications variables to create the electronic tool.ConclusionPIM-Check is the first electronic prescription-screening checklist designed to detect PIM in internal medicine. It is intended to help young healthcare professionals in their clinical practice to detect PIM, to reduce medication errors and to improve patient safety.
Background Identifying patients at high risk of hospital preventable readmission is an essential step towards selecting those who might benefit from specific transitional interventions. Objective Derive and validate a predictive risk score for potentially avoidable readmission (PAR) based on analysis of readmissions, with a focus on medication. Design/Setting/Participants Retrospective analysis of all hospital admissions to internal medicine wards between 2011 and 2014. Comparison between patients readmitted within 30 days and non-readmitted patients, as identified using a specially designed algorithm. Univariate and multivariate regression analyses of demographic data, clinical diagnoses, laboratory results, and the medication data of patients admitted during the first period (2011–2013), to identify factors associated with PAR. Using these, derive a predictive score with a regression coefficient-based scoring method. Subsequently, validate this score with a second cohort of patients admitted in 2013–2014. Variables were identified at hospital discharge. Results The derivation cohort included 7,317 hospital stays. Multivariate logistic regressions found significant associations with PAR for: [adjusted OR (95% CI)] hospital length of stay > 4 days [1.3 (1.1–1.7)], admission in previous 6 months [2.3 (1.9–2.8)], heart failure [1.3 (1.0–1.7)], chronic ischemic heart disease [1.7 (1.2–2.3)], diabetes with organ damage [2.2 (1.3–3.8)], cancer [1.4 (1.0–1.9)], metastatic carcinoma [1.9 (1.3–3.0)], anemia [1.2 (1.0–1.5)], hypertension [1.3 (1.1–1.7)], arrhythmia [1.3 (1.0–1.6)], hyperkalemia [1.4 (1.0–1.7)], opioid drug prescription [1.3 (1.1–1.6)], and acute myocardial infarction [0.6 (0.4–0.9)]. The PAR-Risk Score, derived from these results, demonstrated fair discriminatory and calibration power (C-statistic = 0.699; Brier Score = 0.069). The results for the validation cohort’s operating characteristics were similar (C-statistic = 0.687; Brier Score = 0.064). Conclusion This study identified routinely-available factors that were significantly associated with PAR. A predictive score was derived and internally validated.
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