1,673 research outputs found

    Could Data Broker Information Threaten Physician Prescribing and Professional Behavior?

    Get PDF
    Privacy is threatened by the extent of data collected and sold by consumer data brokers. Physicians, as individual consumers, leave a ‘data trail’ in the offline (e.g. through traditional shopping) and online worlds (e.g. through online purchases and use of social media). Such data could easily and legally be used without a physician’s knowledge or consent to influence prescribing practices or other physician professional behavior. We sought to determine the extent to which such consumer data was available on a sample of more than 3,000 physicians, healthcare faculty and healthcare system staff at one university’s health units. Using just work email addresses for these employees we cheaply and quickly obtained external data on nearly two thirds of employees on demographic characteristics (e.g. income, top 10% national wealth, children at home, married), purchases (e.g. baby products, cooking, sports), behavior (e.g. charitable donor, discount shopper) and interests (e.g. automotive, health and wellness). Consumer data brokers have valuable, cost-effective and detailed information on many healthcare professionals, including data that could be used to segment, target, detail and generally market to physicians in ways that seem under‐appreciated. We call for greater attention to this potential aspect of physician-industry relationships

    Women’s experiences of wearing therapeutic footwear in three European countries

    Get PDF
    Background: Therapeutic footwear is recommended for those people with severe foot problems associated with rheumatoid arthritis (RA). However, it is known that many do not wear them. Although previous European studies have recommended service and footwear design improvements, it is not known if services have improved or if this footwear meets the personal needs of people with RA. As an earlier study found that this footwear has more impact on women than males, this study explores women’s experiences of the process of being provided with it and wearing it. No previous work has compared women’s experiences of this footwear in different countries, therefore this study aimed to explore the potential differences between the UK, the Netherlands and Spain. Method: Women with RA and experience of wearing therapeutic footwear were purposively recruited. Ten women with RA were interviewed in each of the three countries. An interpretive phenomenological approach (IPA) was adopted during data collection and analysis. Conversational style interviews were used to collect the data. Results: Six themes were identified: feet being visibly different because of RA; the referring practitioners’ approach to the patient; the dispensing practitioners’ approach to the patient; the footwear being visible as different to others; footwear influencing social participation; and the women’s wishes for improved footwear services. Despite their nationality, these women revealed that therapeutic footwear invokes emotions of sadness, shame and anger and that it is often the final and symbolic marker of the effects of RA on self perception and their changed lives. This results in severe restriction of important activities, particularly those involving social participation. However, where a patient focussed approach was used, particularly by the practitioners in Spain and the Netherlands, the acceptance of this footwear was much more evident and there was less wastage as a result of the footwear being prescribed and then not worn. In the UK, the women were more likely to passively accept the footwear with the only choice being to reject it once it had been provided. All the women were vocal about what would improve their experiences and this centred on the consultation with both the referring practitioner and the practitioner that provides the footwear. Conclusion: This unique study, carried out in three countries has revealed emotive and personal accounts of what it is like to have an item of clothing replaced with an ‘intervention’. The participant’s experience of their consultations with practitioners has revealed the tension between the practitioners’ requirements and the women’s ‘social’ needs. Practitioners need greater understanding of the social and emotional consequences of using therapeutic footwear as an intervention

    Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression

    Full text link
    Advancements in machine learning algorithms have had a beneficial impact on representation learning, classification, and prediction models built using electronic health record (EHR) data. Effort has been put both on increasing models' overall performance as well as improving their interpretability, particularly regarding the decision-making process. In this study, we present a temporal deep learning model to perform bidirectional representation learning on EHR sequences with a transformer architecture to predict future diagnosis of depression. This model is able to aggregate five heterogenous and high-dimensional data sources from the EHR and process them in a temporal manner for chronic disease prediction at various prediction windows. We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence. The model generated the highest increases of precision-recall area under the curve (PRAUC) from 0.70 to 0.76 in depression prediction compared to the best baseline model. Furthermore, the self-attention weights in each sequence quantitatively demonstrated the inner relationship between various codes, which improved the model's interpretability. These results demonstrate the model's ability to utilize heterogeneous EHR data to predict depression while achieving high accuracy and interpretability, which may facilitate constructing clinical decision support systems in the future for chronic disease screening and early detection.Comment: in IEEE Journal of Biomedical and Health Informatics (2021
    corecore