6,899 research outputs found

    The Use and Misuse of Biomedical Data: Is Bigger Really Better?”

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    Very large biomedical research databases, containing electronic health records (HER) and genomic data from millions of patients, have been heralded recently for their potential to accelerate scientific discovery and produce dramatic improvements in medical treatments. Research enabled by these databases may also lead to profound changes in law, regulation, social policy, and even litigation strategies. Yet, is “big data” necessarily better data? This paper makes an original contribution to the legal literature by focusing on what can go wrong in the process of biomedical database research and what precautions are necessary to avoid critical mistakes. We address three main reasons for a cautious approach to such research and to relying on its outcomes for purposes of public policy or litigation. First, the data contained in databases is surprisingly likely to be incorrect or incomplete. Second, systematic biases, arising from both the nature of the data and the preconceptions of investigators, are serious threats to the validity of biomedical database research, especially in answering causal questions. Third, data mining of biomedical databases makes it easier for individuals with political, social, or economic agendas to generate ostensibly scientific but misleading research findings for the purpose of manipulating public opinion and swaying policy makers. In short, this paper sheds much-needed light on the problems of credulous and uninformed uses of biomedical databases. An understanding of the pitfalls of big data analysis is of critical importance to anyone who will rely on or dispute its outcomes, including lawyers, policy makers, and the public at large. The article also recommends technical, methodological, and educational interventions to combat the dangers of database errors and abuses

    Enhancing Drug Overdose Mortality Surveillance through Natural Language Processing and Machine Learning

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    Epidemiological surveillance is key to monitoring and assessing the health of populations. Drug overdose surveillance has become an increasingly important part of public health practice as overdose morbidity and mortality has increased due in large part to the opioid crisis. Monitoring drug overdose mortality relies on death certificate data, which has several limitations including timeliness and the coding structure used to identify specific substances that caused death. These limitations stem from the need to analyze the free-text cause-of-death sections of the death certificate that are completed by the medical certifier during death investigation. Other fields, including clinical sciences, have utilized natural language processing (NLP) methods to gain insight from free-text data, but thus far, adoption of NLP methods in epidemiological surveillance has been limited. Through a narrative review of NLP methods currently used in public health surveillance and the integration of two NLP tasks, classification and named entity recognition, this dissertation enhances the capabilities of public health practitioners and researchers to perform drug overdose mortality surveillance. This dissertation advances both surveillance science and public health practice by integrating methods from bioinformatics into the surveillance pipeline which provides more timely and increased quality overdose mortality surveillance, which is essential to guiding effective public health response to the continuing drug overdose epidemic

    Promoting Healthcare Innovation on the Demand Side

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    Innovation policy often focuses on the incentives of firms that sell new products. But optimal use of healthcare products also requires good information about the likely effects of products in different patients, and it is hard to provide the right incentives for producers to develop and disclose information that could limit future sales. Regulation partially fills this gap by requiring sellers to conduct clinical trials and report adverse events. But it is inherently problematic to rely on producers to supply negative information about their own products. Healthcare payers, however, can profit from avoiding inappropriate use of costly technologies. Recent technological advances enable insurers to innovate by analyzing their data about healthcare provision and outcomes. The federal government seeks to promote this sort of innovation through a series of initiatives; some picture insurers as passive data repositories, while others provide opportunities for insurers to innovate more directly. In this paper, we examine the role of health insurers in developing new knowledge about the provision of quality healthcare—what we call “demand-side innovation.” We address the contours of this underexplored area of innovation and describe the behavior of participating firms. We examine the legal rules surrounding privacy and their effects on this research, and consider the effect of market structures and intellectual property rules on incentives for demand-side innovation. Throughout, we highlight the multi-pronged way that government facilitates payer innovation, apart from the traditional tools of innovation policy

    Usability analysis of contending electronic health record systems

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    In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

    Privacy and Security Concerns Associated with MHealth Technologies: A Social Media Mining Perspective

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    mHealth technologies seek to improve personal wellness; however, there are stillsignificant privacy and security challenges. With social networking sites serving as lens through which public sentiments and perspectives can be easily accessed, little has been done to investigate the privacy and security concerns of users, associated with mHealth technologies, through social media mining. Therefore, this study investigated various privacy and security concerns conveyed by social media users, in relation to the use of mHealth wearable technologies, using text mining and grounded theory. In addition, the study examined the general sentiments toward mHealth privacy and security related issues, while unearthing how the various issues have evolved over time. Our target social media platform for data collection was the microblogging platform Twitter, which was accessed through Brandwatch providing access to the “Twitter firehose” to extract English tweets. Triangulation was conducted on a representative sample to confirm the results of the Latent Dirichlet Allocation (LDA) Topic Modeling using manual coding through ATLAS.ti. By using the grounded theory analysis methodology, we developed the D-MIT Emergent Theoretical Model which explains that the concerns of users can be categorized as relating to data management, data invasion, or technical safety issues. This model claims that issues affecting data management of mHealth users through the misuse of their data by entities such as wearable companies and other third-party applications, negatively impact their adoption of these devices. Also, concerns of data invasion via real-time data, security breaches, and data surveillance inhibit the adoption of mHealth wearables, which is further impacted by technical safety issues. Further, when users perceived that they do not have full control over their wearables or patient applications, then their acceptance of these mHealth technologies is diminished. While a lack of data and privacy protection policies contribute negatively to users’ adoption of these devices, it also plays a pivotal role in the data management issues presented in this emergent model. Therefore, the importance of having robust legal and policy frameworks that can support mHealth users is desired. Theoretically, the results support the literature on user acceptance of mHealth wearables. These findings were compared with extant literature, and confirmations found across several studies. Further, the results show that over time, mHealth users are still concerned about areas such as security breaches, real-time data invasion, surveillance, and how companies use the data collected from these devices. The findings reveal that more than 75% of the posts analyzed were categorized as depicting anger, fear, or demonstrating levels of disgust. Additionally, 70% of the posts exhibited negative sentiments, whereas 26% were positive, which indicates that users are ambivalent concerning privacy and security, notwithstanding mentions of privacy or security issues in their posts

    Using Big Data Analytics and Statistical Methods for Improving Drug Safety

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    This dissertation includes three studies, all focusing on utilizing Big Data and statistical methods for improving one of the most important aspects of health care, namely drug safety. In these studies we develop data analytics methodologies to inspect, clean, and model data with the aim of fulfilling the three main goals of drug safety; detection, understanding, and prediction of adverse drug effects.In the first study, we develop a methodology by combining both analytics and statistical methods with the aim of detecting associations between drugs and adverse events through historical patients' records. Particularly we show applicability of the developed methodology by focusing on investigating potential confounding role of common diabetes drugs on developing acute renal failure in diabetic patients. While traditional methods of signal detection mostly consider one drug and one adverse event at a time for investigation, our proposed methodology takes into account the effect of drug-drug interactions by identifying groups of drugs frequently prescribed together.In the second study, two independent methodologies are developed to investigate the role of prescription sequence factor on the likelihood of developing adverse events. In fact, this study focuses on using data analytics for understanding drug-event associations. Our analyses on the historical medication records of a group of diabetic patients using the proposed approaches revealed that the sequence in which the drugs are prescribed, and administered, significantly do matter in the development of adverse events associated with those drugs.The third study uses a chronological approach to develop a network of approved drugs and their known adverse events. It then utilizes a set of network metrics, both similarity- and centrality-based, to build and train machine learning predictive models and predict the likely adverse events for the newly discovered drugs before their approval and introduction to the market. For this purpose, data of known drug-event associations from a large biomedical publication database (i.e., PubMed) is employed to construct the network. The results indicate significant improvements in terms of accuracy of prediction of drug-evet associations compared with similar approaches

    Medical Big Data and Big Data Quality Problems

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    Medical big data has generated much excitement in recent years and for good reason. It can be an invaluable resource for researchers in general and insurers in particular. This Article, however, argues that users of medical big data must proceed with caution and recognize the data’s considerable limitations and shortcomings. These can consist of data errors, missing information, lack of standardization, record fragmentation, software problems, and other flaws. The Article analyzes a variety of data quality problems. It also formulates recommendations to address these deficiencies, including data audits, workforce and technical solutions, and regulatory approache

    The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

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    Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field
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