Improving drug prescription in general practice using a novel quality improvement model

Introduction Quality improvement (QI) clusters have been established in many countries to improve healthcare using the Breakthrough Series’ collaboration model. We investigated the effect of a novel QI approach based on this model of performed medication reviews and drug prescription in a Norwegian municipality. Methods All 27 General Practitioners (GPs) in a mid-size Norwegian municipality were invited to join the intervention, consisting of three peer group meetings during a period of 7–8 months. Participants learned practical QI skills by planning and following up QI projects within drug prescription practice. Evaluation forms were used to assess participants’ self-rated improvement, reported medication review reimbursement codes (MRRCs) were used as a process measure, and defined daily doses (DDDs) of potentially inappropriate drugs (PIDs) dispensed to patients aged 65 years or older were used as outcome measures. Results Of the invited GPs, 25 completed the intervention. Of these, 76% self-reported improved QI skills and 67% reported improved drug prescription practices. Statistical process control revealed a non-random increase in the number of MRRCs lasting at least 7 months after intervention end. Compared with national average data, we found a significant reduction in dispensed DDDs in the intervention municipality for benzodiazepine derivates, benzodiazepine-related drugs, drugs for urinary frequency and incontinence and non-steroid anti-inflammatory and antirheumatic medications. Conclusion Intervention increased the frequency of medication reviews, resulting in fewer potentially inappropriate prescriptions. Moreover, there was self-reported improvement in QI skills in general, which may affect other practice areas as well. Intervention required relatively little absence from clinical practice compared with more traditional QI interventions and could, therefore, be easier to implement. KEY POINT The current study investigated to what extent a novel model based on the Breakthrough Series’ collaborative model affects GP improvement skills in general practice and changes their drug prescription. KEY FINDINGS Most participants reported better improvement skills and improved prescription practice. The number of dispensed potentially inappropriate drugs decreased significantly in the intervention municipality compared with the national average. The model seemed to lead to sustained changes after the end of the intervention.publishedVersio

Neural networks for sentiment analysis in AsterixDB

As data is generated at an ever increasing rate, and social media are getting larger and more comprehensive than ever, the availability of these data and the possibility to analyze them, is growing as well. The capability of storing large masses of data has become trivialized over the years, and massive amount of information is stored every second. There is competitiveness in being able to extract meaningful information from data that were previously thought of as insignificant. Sentiment analysis is methods of retrieving an authors attitude towards the topic discussed. Having knowledge about the sentiment of the masses can be of significant market value, especially in i.e. knowing customers happiness with a product or service provided. Other useful areas can be mining twitter and follow opinions about the latest trends to develop new market strategies. Together with the late success of deep learning, it poses great interest to observe how these practices can be combined to analyze big data. Previously the technique for processing natural language has been to create vocabularies of arbitrary order to use for further processing, but by introducing multi-dimensional vectors we can store semantic and syntactic information about words in a vector-space. These vectors has proven incredibly good for natural language processing, and more so as input format to deep learning algorithms as neural networks. In this study several neural network models will be evaluated up against traditional classification algorithms, measuring accuracy, on sentiment classification of twitter messages. Each model will be tested for prediction speed on big data using AsterixDB, a big data management system support ingestion of streaming data. The results from this study gives the best accuracy score 84,02%, and the fastest networks can handle an average of about 10 000 tweets per second

Legemiddelgjennomgang – viktig tiltak for bedre behandling

Fagartikkel som belyser legemiddelgjennomganger sin betydning for god legemiddelbehandling.Legemiddelskader kan i stor grad forebygges med enkle tiltak. Et slikt tiltak er legemiddelgjennomgang, og nå har Legemiddelverket laget en sjekkliste for legemiddelgjennomgang. Vi oppfordrer leger, særlig fastleger, til aktiv bruk av denne metoden for å forebygge legemiddelskader