Screening references is a time‐consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web‐based software system that combines text‐mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract‐level decisions. The number of references that needed to be screened to identify 95% of the abstract‐level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large‐scale study using technology‐assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings.
ObjectiveThis study aimed to examine the prevalence and determinants of benzodiazepine prescription among older adults in Switzerland, and analyse association with hospitalisation and costs.DesignRetrospective analysis of claims data.SettingThe study was conducted in nine cantons in Switzerland.ParticipantsOlder adults aged 65 years and older enrolled with a large Swiss health insurance company participated in the study.Primary and secondary outcome measuresThe primary outcome was prevalence of benzodiazepine prescription. The secondary outcomes were (1) determinants of any benzodiazepine prescription; (2) the association between any prescription and the probability of hospitalisation for trauma and (3) the association between any prescription and total healthcare expenditures.ResultsOverall, 69 005 individuals were included in the study. Approximately 20% of participants had at least one benzodiazepine prescription in 2017. Prescription prevalence increased with age (65–69: 15.9%; 70–74: 18.4%; 75–80: 22.5%; >80: 25.8%) and was higher in women (25.1%) compared with men (14.6%). Enrollees with the highest deductible of Swiss Francs (CHF) 2500 were 70% less likely to receive a prescription than enrollees with the lowest deductible of CHF 300 (adjusted OR=0.29, 95% CI 0.24 to 0.35).Individuals with at least one prescription had a higher probability of hospitalisation for trauma (OR=1.31, 95% CI 1. 20 to 1.1.44), and 70% higher health care expenditures (β=0.72, 95% CI 0. 67 to 0.77). Enrollees in canton Valais were three times more likely to receive a prescription compared to enrollees from canton Aargau (OR=2.84, 95% 2.51 to 3.21).ConclusionsThe proportion of older adults with at least one benzodiazepine prescription is high, as found in the data of one large Swiss health insurance company. These enrollees are more likely to be hospitalised for trauma and have higher healthcare expenditures. Important differences in prescription prevalence across cantons were observed, suggesting potential overuse. Further research is needed to understand the drivers of variation, prescription patterns across providers, and trends over time.
Objective and settingPrimary prevention, comprising patient-oriented and environmental interventions, is considered to be one of the best ways to reduce violence in the emergency department (ED). We assessed the impact of a comprehensive prevention programme aimed at preventing incivility and verbal violence against healthcare professionals working in the ophthalmology ED (OED) of a university hospital.InterventionThe programme was designed to address long waiting times and lack of information. It combined a computerised triage algorithm linked to a waiting room patient call system, signage to assist patients to navigate in the OED, educational messages broadcast in the waiting room, presence of a mediator and video surveillance.ParticipantsAll patients admitted to the OED and those accompanying them.DesignSingle-centre prospective interrupted time-series study conducted over 18 months.Primary outcomeViolent acts self-reported by healthcare workers committed by patients or those accompanying them against healthcare workers.Secondary outcomesWaiting time and length of stay.ResultsThere were a total of 22 107 admissions, including 272 (1.4%) with at least one act of violence reported by the healthcare workers. Almost all acts of violence were incivility or verbal harassment. The rate of violence significantly decreased from the pre-intervention to the intervention period (24.8, 95% CI 20.0 to 29.5, to 9.5, 95% CI 8.0 to 10.9, acts per 1000 admissions, p<0.001). An immediate 53% decrease in the violence rate (incidence rate ratio=0.47, 95% CI 0.27 to 0.82, p=0.0121) was observed in the first month of the intervention period, after implementation of the triage algorithm.ConclusionA comprehensive prevention programme targeting patients and environment can reduce self-reported incivility and verbal violence against healthcare workers in an OED.Trial registration numberNCT02015884
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