Evaluation of a consultant audit and feedback programme to improve the quality of antimicrobial prescribing in acute medical admissions

Objectives This study aims to evaluate the effectiveness and acceptability of a pharmacist-led antimicrobial stewardship intervention, consisting of consultant performance audit and feedback, on antimicrobial prescribing quality. Methods From October 2010 to September 2012, the prescribing performance of medical consultant teams rotating on the acute medical admissions unit was measured against four quality indicators. Measurements were taken at baseline then at quarterly intervals during which time consultants received feedback. Proportion of prescriptions adhering to each indicator was compared with baseline using paired sample z-test (significance level P�<�0.01, Bonferroni corrected). Consultants' views were explored using anonymous questionnaires. Key findings Overall, 2609 antimicrobial prescriptions were reviewed. Improvement from baseline was statistically significant in all follow-up periods for two indicators: �antimicrobials should have a documented indication in the medical notes� and �antimicrobials should adhere to guideline choice or have a justification for deviation�, reaching 6.0% (95% CI 2.5, 9.6) and 8.7% (95% CI 3.7, 13.7), respectively. Adherence to the indicator �antimicrobials should have a documented stop/review prompt� improved significantly in all but the first follow-up period. For the indicator: �antimicrobial assessed by antimicrobial specialists as unnecessary�, improvement was statistically significant in the first (�4.7%, 95% CI �8.0, �1.4) and fourth (�4.2%, 95% CI �7.7%, �0.8%) periods. Service evaluation showed support for the pharmacist-led stewardship activities. Conclusions There were significant and sustained improvements in prescribing quality as a result of the intervention. Consultants' engagement and acceptance of stewardship activities were demonstrated

Patient-Driven Network Selection in multi-RAT Health Systems Using Deep Reinforcement Learning

The recent pandemic along with the rapid increase in the number of patients that require continuous remote monitoring imposes several challenges to support the high quality of services (QoS) in remote health applications. Remote-health (r-health) systems typically demand intense data collection from different locations within a strict time constraint to support sustainable health services. On the contrary, the end-users with mobile devices have limited batteries that need to run for a long time, while continuously acquiring and transmitting health-related information. Thus, this paper proposes an adaptive deep reinforcement learning (DRL) framework for network selection over heteroge-neous r-health systems to enable continuous remote monitoring for patients with chronic diseases. The proposed framework allows for selecting the optimal network(s) that maximizes the accumulative reward of the patients while considering the patients' state. Moreover, it adopts an adaptive compression scheme at the patient level to further optimize the energy consumption, cost, and latency. Our results depict that the proposed framework outperforms the state-of-the-art techniques in terms of battery lifetime and reward maximization.This work was made possible by NPRP grant # NPRP12S-0305-190231 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors

Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

Purpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three nonmutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: More women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance. © 2022 Kostka et al.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]