BackgroundMedicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services, there is a need to increase efficiency while maintaining patient safety.ObjectiveTo develop a prediction tool, the Medicines Optimisation Assessment Tool (MOAT), to target patients most in need of pharmacists’ input in hospital.MethodsPatients from adult medical wards at two UK hospitals were prospectively included into this cohort study. Data on medication-related problems (MRPs) were collected by pharmacists at the study sites as part of their routine daily clinical assessments. Data on potential risk factors, such as number of comorbidities and use of ‘high-risk’ medicines, were collected retrospectively. Multivariable logistic regression modelling was used to determine the relationship between risk factors and the study outcome: preventable MRPs that were at least moderate in severity. The model was internally validated and a simplified electronic scoring system developed.ResultsAmong 1503 eligible admissions, 610 (40.6%) experienced the study outcome. Eighteen risk factors were preselected for MOAT development, with 11 variables retained in the final model. The MOAT demonstrated fair predictive performance (concordance index 0.66) and good calibration. Two clinically relevant decision thresholds (ie, the minimum predicted risk probabilities to justify pharmacists’ input) were selected, with sensitivities of 90% and 66% (specificity 30% and 61%); these equate to positive predictive values of 47% and 54%, respectively. Decision curve analysis suggests that the MOAT has potential value in clinical practice in guiding decision-making.ConclusionThe MOAT has potential to predict those patients most at risk of moderate or severe preventable MRPs, experienced by 41% of admissions. External validation is now required to establish predictive accuracy in a new group of patients.

IntroductionMedicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services there is a need to increase efficiency while maintaining patient safety. The aim of this study is to develop a prognostic model, the Medicines Optimisation Assessment Tool (MOAT), which can be used to target patients most in need of pharmacists' input while in hospital.Methods and analysisThe MOAT will be developed following recommendations of the Prognosis Research Strategy partnership. Using a cohort study we will prospectively include 1500 adult patients from the medical wards of two UK hospitals. Data on medication-related problems (MRPs) experienced by study patients will be collected by pharmacists at the study sites as part of their routine daily clinical assessment of patients. Data on potential risk factors such as polypharmacy, renal impairment and the use of 'high risk' medicines will be collected retrospectively from the information departments at the study sites, laboratory reporting systems and patient medical records. Multivariable logistic regression models will then be used to determine the relationship between potential risk factors and the study outcome of preventable MRPs that are at least moderate in severity. Bootstrapping will be used to adjust the MOAT for optimism, and predictive performance will be assessed using calibration and discrimination. A simplified scoring system will also be developed, which will be assessed for sensitivity and specificity.Ethics and disseminationThis study has been approved by the Proportionate Review Service Sub-Committee of the National Health Service Research Ethics Committee Wales REC 7 (16/WA/0016) and the Health Research Authority (project ID 197298). We plan to disseminate the results via presentations at relevant patient/public, professional, academic and scientific meetings and conferences, and will submit findings for publication in peer-reviewed journals.Trial registration numberNCT02582463.

The aim of this prospective observational study was to establish associations between the use of high-risk medicine groups and the study outcome: occurrence of at least 1 moderate or severe preventable medication-related problem. Data on medication-related problems, high-risk medicines, and other potential risk factors were collected from adults on medical wards in 2 UK hospitals. Logistic regression modelling was used to determine relationships between high-risk medicines and the study outcome. Among 1503 eligible admissions, 6 high-risk medicine groups were associated with the study outcome on univariable analysis; multivariable analysis found only systemic antimicrobials and epilepsy medicines to be independently associated with the outcome (adjusted odds ratio 1.44, 95% confidence interval 1.08-1.92 and adjusted odds ratio 1.61, 95% confidence interval 1.16-2.25 respectively).Identification of high-risk medicine groups has potential to permit targeting of patients at highest risk of avoidable medication-related harm, but multivariable analysis suggests risk is likely to be multifactorial.

Objective Hospital pharmacy is undergoing a period of rapid change, with pharmacists needing to focus where they add most value. Our aim was to identify where pharmacists have potential for greatest impact by analysing data on clinically relevant medication‐related problems (MRPs). Methods We included consecutive admissions from adult medical wards at two UK hospitals between April and November 2016. MRPs were identified by pharmacists at the study sites as part of their routine daily patient assessments, validated and assessed for preventability and severity. Descriptive analyses were performed on clinically relevant (moderate or severe preventable) MRPs to establish the stage of inpatient stay where identified and their types/categories (overall and by stage of inpatient stay). Key findings Among 1503 eligible admissions, 2614 validated MRPs were identified, of which 1153 were moderate or severe, and preventable. Over 70% of these clinically relevant MRPs were identified during/before the first ward‐based pharmacy review of patients. The most frequent MRP subcategory was ‘indication not treated/missing therapy’, accounting for 46% of clinically relevant MRPs. Dose selection issues were the next most common, accounting for 24%. The subcategory ‘indication not treated/missing therapy’ was identified more frequently at admission and discharge (53% and 45% of MRPs, respectively) compared with during the inpatient stay (14%), P < 0.001. Conclusions This research suggests patients are at greatest need of pharmacist input in terms of identification/resolution of clinically relevant MRPs during early stages of inpatient stay; however, clinically relevant MRPs continue to occur throughout their stay, suggesting need for ongoing pharmacy review.