Search results

The purpose of the paper is to inquire into how decisions about public fiscal responsibility for pharmaceutical spending are made and justified when there is a lack of coherent, unambiguous and undisputed evidence about the material characteristics of pharmaceutical use.

This is an exploratory, single‐organisation case study of a Swedish governmental agency tasked with deciding whether prescription pharmaceuticals are included or excluded from the public pharmaceutical benefits scheme. A comparison is made of two intra‐organisational decision‐making processes based on interviews, document studies and participant observations undertaken over a period of two years.

The study shows that providing foundation for making decisions involves attempts to remove ambiguity among multiple knowledge claims about pharmaceuticals' characteristics. Three means of removing ambiguity are outlined. In addition, a fourth means of dealing with ambiguity is identified, when efforts to achieve coherence among multiple sources of knowledge fail. In this case ambiguity about pharmaceuticals' characteristics may be delegated to the individual medical professional to decide about treatment for specific patients.

The limited empirical material provides no statistical generalisability of the findings. However, the study has theoretical implications for understanding decision‐making processes in health care institutions.

The paper provides a detailed empirical account of a newly created health care assessment organisation similar to those created in other countries to tackle problems of resource allocation.

The purpose of this paper is to assess National Medical Care Survey data quality.

Data completeness and representativeness were computed for all observations while other data quality measures were assessed using a 10 per cent sample from the National Medical Care Survey database; i.e., 12,569 primary care records from 189 public and private practices were included in the analysis.

Data field completion ranged from 69 to 100 per cent. Error rates for data transfer from paper to web-based application varied between 0.5 and 6.1 per cent. Error rates arising from diagnosis and clinical process coding were higher than medication coding. Data fields that involved free text entry were more prone to errors than those involving selection from menus. The authors found that completeness, accuracy, coding reliability and representativeness were generally good, while data timeliness needs to be improved.

Only data entered into a web-based application were examined. Data omissions and errors in the original questionnaires were not covered.

Results from this study provided informative and practicable approaches to improve primary health care data completeness and accuracy especially in developing nations where resources are limited.

Primary care data quality studies in developing nations are limited. Understanding errors and missing data enables researchers and health service administrators to prevent quality-related problems in primary care data.

Automatic anatomical therapeutic chemical (ATC) classification is progressing at a rapid pace because of its potential in drug development. Predicting an unknown compound's therapeutic and chemical characteristics in terms of how it affects multiple organs and physiological systems makes automatic ATC classification a vital yet challenging multilabel problem. The aim of this paper is to experimentally derive an ensemble of different feature descriptors and classifiers for ATC classification that outperforms the state-of-the-art.

The proposed method is an ensemble generated by the fusion of neural networks (i.e. a tabular model and long short-term memory networks (LSTM)) and multilabel classifiers based on multiple linear regression (hMuLab). All classifiers are trained on three sets of descriptors. Features extracted from the trained LSTMs are also fed into hMuLab. Evaluations of ensembles are compared on a benchmark data set of 3883 ATC-coded pharmaceuticals taken from KEGG, a publicly available drug databank.

Experiments demonstrate the power of the authors’ best ensemble, EnsATC, which is shown to outperform the best methods reported in the literature, including the state-of-the-art developed by the fast.ai research group. The MATLAB source code of the authors’ system is freely available to the public at https://github.com/LorisNanni/Neural-networks-for-anatomical-therapeutic-chemical-ATC-classification.

This study demonstrates the power of extracting LSTM features and combining them with ATC descriptors in ensembles for ATC classification.

The purpose of this paper is to set out a methodology for characterising the complexity of air traffic control (ATC) sectors based on individual operations. This machine learning methodology also learns from the data on which the model is based.

The methodology comprises three steps. Firstly, a statistical analysis of individual operations is carried out using elementary or initial variables, and these are combined using machine learning. Secondly, based on the initial statistical analysis and using machine learning techniques, the impact of air traffic flows on an ATC sector are determined. The last step is to calculate the complexity of the ATC sector based on the impact of its air traffic flows.

The results obtained are logical from an operational point of view and are easy to interpret. The classification of ATC sectors based on complexity is quite accurate.

The methodology is in its preliminary phase and has been tested with very little data. Further refinement is required.

The methodology can be of significant value to ATC in that when applied to real cases, ATC will be able to anticipate the complexity of the airspace and optimise its resources accordingly.

The purpose of this paper is to evaluate gender-related differences in the use of drugs for chronic diseases in undocumented migrants.

The authors analyzed the databases of two charitable organizations providing medical help and medicines to undocumented migrants. Data were available for 9,822 patients in the period 2014–2016. The authors grouped medicines according to the Anatomical Therapeutic Chemical (ATC) classification. We considered the ATC group as an indicator of a group of diseases.

Both males and females needed medicines for chronic diseases in a comparable manner. When we analyzed the age distribution, The authors noticed that males showed a tendency to begin to be affected at an earlier age; however, this cumulative difference was not statistically significant. But when we looked at six groups of drugs separately (cardiovascular, respiratory, anti-thrombotic, neurologic, psychiatric, anti-diabetic), the authors found that females always needed drugs for chronic diseases at a later age, always with a significant difference (p<0.0001 for cardiovascular, antithrombotic, antidiabetic and psychoactive drugs; p<0.002 for neurologic products; p<0.04 for drugs used in chronic respiratory conditions).

This is a retrospective study based on the analysis of existing databases, but the peculiar features of this population (undocumented migrants) do not allow at the moment controlled studies.

Our observations could be important when planning public health strategies, especially in the field of prevention.

This is the first report of gender differences in the use of medicines for chronic diseases within a large sample of undocumented migrants.

With the significant growth in electronic education materials such as syllabus documents and lecture notes, available on the internet and intranets, there is a need for robust central repositories of such materials to allow both educators and learners to conveniently share, search and access them. The purpose of this paper is to report on the work to develop a national repository for course syllabi in Ireland.

The paper describes a prototype syllabus repository system for higher education in Ireland, which has been developed by utilising a number of information extraction and document classification techniques, including a new fully unsupervised document classification method that uses a web search engine for automatic collection of training set for the classification algorithm.

Preliminary experimental results for evaluating the performance of the system and its various units, particularly the information extractor and the classifier, are presented and discussed.

In this paper, three major obstacles associated with creating a large‐scale syllabus repository are identified, and a comprehensive review of published research work related to addressing these problems is provided. Two different types of syllabus documents are identified and describe a rule‐based information extraction system capable of extracting structured information from unstructured syllabus documents is described. Finally, the importance of classifying resources in a syllabus digital library is highlighted, a number of standard education classification schemes are introduced, and the unsupervised automated document classification system, which classifies syllabus documents based on an extended version of the International Standard Classification of Education, is described.

Aim ‐ To describe prescription drug use in a nationwide prison population and compare it with contemporary drug use in other relevant populations as well as in a prison population 25 years ago. Method ‐ Detailed medication sheets from 37 prisons, covering 90% of the Norwegian prison population, were collected. All drugs were transcribed into the Anatomical Therapeutic Chemical classification system, and Defined Daily Doses/100 inmates were calculated. Data enabling relevant comparisons were gathered from reliable sources. Results ‐ Among the 2,617 inmates investigated, 48% used no medication. Sixteen percent used both psychotropic and somatic drugs, 17% psychotropic drugs only, and 19% somatic drugs only. Strong correlations between drug use and gender, age, and prison characteristics were demonstrated. Psychotropic drug use was higher than in the general population but lower than in a psychiatric hospital unit. Somatic drug use was lower than in the general population, except non‐steroid antiinflammatory agents, antihistamines, and drugs to alleviate obstructive airway diseases. Psychotropic drug use, particularly antidepressant use, was higher than in prisons 25 years ago. Conclusion ‐ High levels of use of all psychotropic and a number of somatic drugs were demonstrated in this nationwide prison population. Treatment studies are needed in order to ascertain whether the high levels of use of particularly psychotropic medications are indeed improving the mental health of prisoners.

The Indian pharmaceutical industry accounts for 8% of global production and exports medicines to over 200 countries. Multinational enterprises (MNEs) enter the Indian market either directly through the establishment of subsidiaries or indirectly through licensing arrangements. However, evidence on MNE’s contribution toward development in India in terms of capability enhancement and linkages or through other spillover effects is limited. The purpose of this research was to generate evidence on (a) contribution of MNEs in the pharmaceutical market in India, (b) nature and impact of foreign direct investment (FDI) inflows in the Indian pharmaceutical sector, (c) contribution of MNEs in R&D and innovation in India, and (d) MNE’s contribution toward introducing new chemical entities (NCEs) and new biological entities (NBEs) in India through a mixed method research design. We conducted an in-depth quantitative analysis on multiple data sets and qualitative interviews of various stakeholders to generate a holistic understanding on the aforementioned research objectives. Our findings suggest that from the perspective of capability enhancement and linkages, the contribution of pharmaceutical MNEs in India is limited. We observed that majority of FDI investments are brownfield against desired greenfield investments. In addition, MNEs are investing far less of profit before tax (PBT) compared with Indian firms on research and development. However, MNEs are contributing significantly toward access to certain pharmaceutical segments like vaccines, hormones, and parenterals, which require sophisticated production facilities, advanced technology, and intellectual capital. Further, MNEs role in innovation and introduction of new medicines (new molecular entity [NME] and NBE New Chemical and Biological Entities (NCEs and NBEs)) in India is significant. We propose that creating a conducive policy environment and predictable regulatory environment can facilitate capability enhancement and linkages through MNEs. Some of the potential policy instruments include appropriate implementation of FDI policy and Intellectual Property Rights (IPR) policy to balance trade and public health.

The purpose of this paper is to reduce inappropriate non-steroidal anti-inflammatory prescribing in primary care patients with chronic kidney disease (CKD). Once diagnosed, CKD management involves delaying progression to end stage renal failure and preventing complications. It is well established that non-steroidal anti-inflammatories have a negative effect on kidney function and consequently, all nephrology consensus groups suggest avoiding this drug class in CKD.

The sampling criteria included all practice patients with a known CKD risk factor. This group was refined to include those with an estimated glomerular filtration rate (eGFR)<60 ml/min per 1.73m2 (stage 3 CKD or greater). Phase one analysed how many prescriptions had occurred in this group over the preceding three months. The intervention involved creating an automated alert on at risk patient records if non-steroidal anti-inflammatories were prescribed and discussing the rationale with practice staff. The re-audit phase occurred three months’ post intervention.

The study revealed 728/7,500 (9.7 per cent) patients at risk from CKD and 158 (2.1 per cent) who were subsequently found to have an eGFR<60 ml/min, indicating=stage 3 CKD. In phase one, 10.2 per cent of at risk patients had received a non-steroidal anti-inflammatory prescription in the preceding three months. Additionally, 6.2 per cent had received non-steroidal anti-inflammatories on repeat prescription. Phase two post intervention revealed a significant 75 per cent reduction in the total non-steroidal anti-inflammatories prescribed and a 90 per cent reduction in repeat non-steroidal anti-inflammatory prescriptions in those with CKD.

The study significantly reduced non-steroidal anti-inflammatory prescription in those with CKD in primary care settings. It also created a CKD register within the practice and an enduring medication alert system for individuals that risk nephrotoxic non-steroidal anti-inflammatory prescription. It established a safe, reliable and efficient process for reducing morbidity and mortality, improving quality of life and limiting the CKD associated health burden.