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Background: Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable., Aim: To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients., Methods: A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA)., Results: A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4., Conclusion: The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Medication harm is experienced by up to 30% of hospitalised patients, of which 7% experience severe harm. Pharmacist review can mitigate this harm. However, in increasingly busy hospitals, with high patient throughput, and scarce resources, there is a need to prioritise patients. Current methods are cumbersome, include many risk factors and are not evaluated in the clinical setting., Objectives: To determine key criteria used by hospital pharmacists and investigate perspectives related to patient prioritisation for potential medication harm in the hospital setting., Methods: This study used two methods; focus groups and a cross-sectional survey of Australian hospital pharmacists. Focus groups were used to identify criteria and perspectives related to prioritisation and were analysed thematically. Criteria from focus groups, and a systematic review, were used to design the survey. The survey was distributed via the Society of Hospital Pharmacists of Australia. The top 10 prioritisation criteria, and associated sub-criteria selected by over 50% of respondents were ranked. Combination of criteria used most frequently on a day-to-day basis were identified., Results: Twenty clinical pharmacists participated in four, one-hour, audio recorded focus groups. Using inductive thematic analysis of transcripts three themes were identified; 1) prioritisation criteria, 2) barriers to, and 3) facilitators of patient prioritisation, with five sub-themes and 26 codes. Pharmacists identified a number of barriers such as a lack of relevant handover information. Organisational demands, such as patient discharge and medications supply also influenced priority and could act as barriers to a pharmacist enacting their prioritisation plan. A total of 231 pharmacists completed the survey. High priority criteria included, renal impairment, use of high-risk medications and therapeutic drug monitoring., Conclusion: Pharmacists described prioritisation as a multifactorial process with a focus on high-risk medications and renal impairment. These findings will inform the development of a predictive risk score for patient prioritisation., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objective: The aim was to better understand how beliefs influence medication adherence in asthma., Methods: All participants were prescribed an inhaled corticosteroid for a diagnosis of asthma. Each participant completed a survey consisting of: Beliefs about Medicines Questionnaire (BMQ), Brief-Illness Perception Questionnaire (B-IPQ) and Multi-dimensional Health Locus of Control Scale (MHLCS). Adherence to inhaled corticosteroids was elicited using the Medication Adherence Report Scale (MARS). Multiple linear regression with interaction effects was used to identify significant predictors of medication adherence and interactions between beliefs., Results: A total of 198 participants completed the survey. The mean(±SD) MARS score was 19.2(±4.5). A multivariable model (adjusted R 2  = 0.39) predicted adherence using: age, asthma hospitalisation, timeline (B-IPQ) subscale, necessity and concern (BMQ) subscales, doctor (MHLCS) subscale and the two interaction effects (concerns [BMQ] moderated by chance [MHLCS] and treatment control [B-IPQ] moderated by understanding [B-IPQ])., Conclusion: The findings of this study contribute to a better understanding of the role of beliefs in medication adherence in asthma. Certain beliefs meaningfully interrelate and change the relationship they have with medication adherence., Practice Implications: If these beliefs are causally related to medication adherence and can be intervened upon, the findings are useful for providing targets to personalise adherence support., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objective: This meta-analysis investigated whether beliefs in the necessity and concerns of medicine and the necessity-concerns differential are correlated with medication adherence on a population level and in different conditions., Methods: An electronic search of Web of Science, EMBASE, PubMed and CINAHL was conducted for manuscripts utilising the Beliefs about Medicines Questionnaire and comparing it to any measure of medication adherence. Studies were pooled using the random-effects model to produce a mean overall effect size correlation. Studies were stratified for condition, adherence measure, power and study design., Results: Ninety-four papers were included in the meta-analysis. The overall effect size(r) for necessity, concerns, and necessity-concerns differential was 0.17, -0.18 and 0.24 respectively and these were all significant (p<0.0001). Effect size for necessity was stronger in asthma and weaker in the cardiovascular group compared to the overall effect size., Conclusion: Necessity and concerns beliefs and the necessity-concerns differential were correlated with medication adherence on a population level and across the majority of included conditions. The effect sizes were mostly small with a magnitude comparable to other predictors of adherence., Practice Implications: This meta-analysis suggests that necessity and concern beliefs about medicines are one important factor to consider when understanding reasons for non-adherence., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)