AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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AMIA Annu Symp Proc · Jan 2018
A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.
Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports. The objective of this study was to determine whether natural language processing (NLP) with machine learning performs better than a traditional keyword model for ARDS identification. Linguistic pre-processing of reports was performed and text features were inputs to machine learning classifiers tuned using 10-fold cross-validation on 80% of the sample size and tested in the remaining 20%. ⋯ The traditional model had an accuracy of 67.3% (95% CI: 58.3-76.3) with a positive predictive value (PPV) of 41.7% (95% CI: 27.7-55.6). The best NLP model had an accuracy of 83.0% (95% CI: 75.9-90.2) with a PPV of 71.4% (95% CI: 52.1-90.8). A computable phenotype for ARDS with NLP may identify more cases than the traditional model.
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AMIA Annu Symp Proc · Jan 2018
Systematic Literature Review of Prescription Drug Monitoring Programs.
Prescription opioid abuse has become a serious national problem. To respond to the opioid epidemic, states have implemented prescription drug monitoring programs (PDMPs) to monitor and reduce opioid abuse. We conducted a systematic literature review to better understand the PDMP impact on reducing opioid abuse, improving prescriber practices, and how EHR integration has impacted PDMP usability. Lessons learned can help guide federal and state-based efforts to better respond to the opioid crisis.
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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.