Evaluating the impact of a polypharmacy Action Learning Sets tool on healthcare practitioners' confidence, perceptions and experiences of stopping inappropriate medicines.

BACKGROUND: Issues of medication adherence, multimorbidity, increased hospitalisation risk and negative impact upon quality of life have led to the management of polypharmacy becoming a national priority. Clinical guidelines advise a patient-centred approach, involving shared decision-making and multidisciplinary team working. However, there have been limited educational initiatives to improve healthcare practitioners' management of polypharmacy and stopping inappropriate medicines. This study aimed to evaluate the impact of a polypharmacy Action Learning Sets (ALS) tool across five areas: i. healthcare practitioners' confidence and perceptions of stopping medicines; ii. knowledge and information sources around stopping medicines; iii. perception of patients and stopping medicines; iv. perception of colleagues and stopping medicines and v. perception of the role of institutional factors in stopping medicines. METHODS: The ALS tool was delivered to a multi-disciplinary group of healthcare practitioners: GPs [n = 24] and pharmacy professionals [n = 9]. A pre-post survey with 28 closed statements across five domains relating to the study aims [n = 32] and a post evaluation feedback survey with 4 open-ended questions [n = 33] were completed. Paired pre-post ALS responses [n = 32] were analysed using the Wilcoxon signed-rank test. Qualitative responses were analysed using a simplified version of the constant comparative method. RESULTS: The ALS tool showed significant improvement in 14 of 28 statements in the pre-post survey across the five domains. Qualitative themes (QT) from the post evaluation feedback survey include: i. awareness and management of polypharmacy; ii. opportunity to share experiences; iii. usefulness of ALS as a learning tool and iv. equipping with tools and information. Synthesised themes (ST) from analysis of pre-post survey data and post evaluation feedback survey data include: i. awareness, confidence and management of inappropriate polypharmacy, ii. equipping with knowledge, information, tools and resources and iii. decision-making and discussion about stopping medicines with colleagues in different settings. CONCLUSIONS: This evaluation contributes to developing understanding of the role of educational initiatives in improving inappropriate polypharmacy, demonstrating the effectiveness of the ALS tool in improving healthcare practitioners' awareness, confidence and perceptions in stopping inappropriate medicines. Further evaluation is required to examine impact of the ALS tool in different localities as well as longer-term impact.

Identifying on admission patients likely to develop acute kidney injury in hospital.

BACKGROUND: The incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI. METHODS: Electronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation. Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC). RESULTS: Accuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64-0.77); FLS II (AUC 0.77, 95% CI: 0.69-0.85) and MLR II (AUC 0.74, 95% CI: 0.65-0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92-0.98). CONCLUSIONS: FLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models.