AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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AMIA Annu Symp Proc · Jan 2017
Big data in healthcare - the promises, challenges and opportunities from a research perspective: A case study with a model database.
Recent advances in data collection during routine health care in the form of Electronic Health Records (EHR), medical device data (e.g., infusion pump informatics, physiological monitoring data, and insurance claims data, among others, as well as biological and experimental data, have created tremendous opportunities for biological discoveries for clinical application. However, even with all the advancement in technologies and their promises for discoveries, very few research findings have been translated to clinical knowledge, or more importantly, to clinical practice. In this paper, we identify and present the initial work addressing the relevant challenges in three broad categories: data, accessibility, and translation. These issues are discussed in the context of a widely used detailed database from an intensive care unit, Medical Information Mart for Intensive Care (MIMIC III) database.
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AMIA Annu Symp Proc · Jan 2017
A Simulation Study on Handoffs and Cross-coverage: Results of an Error Analysis.
Handoffs and cross-coverage are necessary for maintaining the continuity of patient care, yet both are potential sources of error, and may threaten patient safety and care. Handoffs are the transfer of patient information and accountability from one provider to another. Cross-coverage is the management of patients, of whom physicians who have little or no prior knowledge of, during nightshifts. ⋯ We collected data from thirty physicians from an academic medical center as they signed out six patients after responding to nurse calls. An error analysis of the sign-out data revealed 42 errors overall, with 28 omissions and 14 "erroneous data" errors. We then propose ways to prevent these errors through modification of the electronic medical record and support tools, and through higher awareness of human factors.
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AMIA Annu Symp Proc · Jan 2017
A Cross-Sectional Study of Prominent US Mobile Health Applications: Evaluating the Current Landscape.
Mobile health (mHealth) could offer unprecedented opportunity to provide medical support closer to the users. We have selected some relevant criteria to describe 100 apps from Google Play store and Apple's App Store's top suggestions in medical category. These characteristics were compared based on the paid or free nature of the apps, the target users: consumers or healthcare professionals, and the platform: Android or iOS. ⋯ Our study shows that even in top rated mHealth apps, a high proportion lacks some basic criteria regarding the quality of the apps including the presence of a privacy policy, describing content sources, participation of the target users in the app development, etc. Paid apps did not ensure better quality compared to free apps. The current mHealth market is not mature enough to be used widely and recommended by healthcare professionals.
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AMIA Annu Symp Proc · Jan 2017
Comparative StudyClinical Named Entity Recognition Using Deep Learning Models.
Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. ⋯ The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER.
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AMIA Annu Symp Proc · Jan 2017
Quantifying the Impact of Trainee Providers on Outpatient Clinic Workflow using Secondary EHR Data.
Providers today face productivity challenges including increased patient loads, increased clerical burdens from new government regulations and workflow impacts of electronic health records (EHR). Given these factors, methods to study and improve clinical workflow continue to grow in importance. ⋯ The purpose of this study is to demonstrate that secondary EHR data can be used to quantify that impact, with potentially important results for clinic efficiency and provider reimbursement models. Key findings from this study are that (1) Secondary EHR data can be used to reflect in clinic trainee activity, (2) presence of trainees, particularly in high-volume clinic sessions, is associated with longer session lengths, and (3) The timing of trainee appointments within clinic sessions impacts the session length.