AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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AMIA Annu Symp Proc · Jan 2014
Characterization of a handoff documentation tool through usage log data.
Handoffs are a critical component of coordinated patient care; however, poor handoffs have been associated with near misses and adverse events. To address this, national agencies have recommended standardizing handoffs, for example through the use of handoff documentation tools. Recent research suggests that handoff tools, typically designed for physicians, are often used by non-physician providers as information sources. ⋯ This further reiterates the view of electronic handoff tools as facilitators of team communication and coordination. However, the study also showed considerable variability in the frequency of updates between different units and across different patients. Further research is required to understand what factors drive such diversity in the use of electronic handoff tool and whether this diversity can be used to make inferences about patients' conditions.
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AMIA Annu Symp Proc · Jan 2014
An evaluation of a natural language processing tool for identifying and encoding allergy information in emergency department clinical notes.
Emergency department (ED) visits due to allergic reactions are common. Allergy information is often recorded in free-text provider notes; however, this domain has not yet been widely studied by the natural language processing (NLP) community. We developed an allergy module built on the MTERMS NLP system to identify and encode food, drug, and environmental allergies and allergic reactions. ⋯ We developed an annotation schema and annotated 400 ED notes that served as a gold standard for comparison to MTERMS output. MTERMS achieved an F-measure of 87.6% for the detection of allergen names and no known allergies, 90% for identifying true reactions in each allergy statement where true allergens were also identified, and 69% for linking reactions to their allergen. These preliminary results demonstrate the feasibility using NLP to extract and encode allergy information from clinical notes.
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Being a hospital patient can be isolating and anxiety-inducing. We conducted two experiments to better understand clinician and patient perceptions about giving patients access to their medical records during hospital encounters. The first experiment, a survey of physicians, nurses, and other care providers (N=53), showed that most respondents were comfortable with the idea of providing patients with their clinical information. ⋯ In the second experiment, we provided eight hospital patients with a daily copy of their full medical record-including physician notes and diagnostic test results. From semi-structured interviews with seven of these patients, we found that they perceived the information as highly useful even if they did not fully understand complex medical terms. Our results suggest that increased patient information sharing in the inpatient setting is beneficial and desirable to patients, and generally acceptable to clinicians.
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Mobile Health (mHealth) applications lie outside of regulatory protection such as HIPAA, which requires a baseline of privacy and security protections appropriate to sensitive medical data. However, mHealth apps, particularly those in the app stores for iOS and Android, are increasingly handling sensitive data for both professionals and patients. This paper presents a series of three studies of the mHealth apps in Google Play that show that mHealth apps make widespread use of unsecured Internet communications and third party servers. Both of these practices would be considered problematic under HIPAA, suggesting that increased use of mHealth apps could lead to less secure treatment of health data unless mHealth vendors make improvements in the way they communicate and store data.
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AMIA Annu Symp Proc · Jan 2014
Development, Implementation and Use of Electronic Surveillance for Ventilator-Associated Events (VAE) in Adults.
Mechanical ventilation provides an important, life-saving therapy for severely ill patients, but ventilated patients are at an increased risk for complications, poor outcomes, and death during hospitalization.1 The timely measurement of negative outcomes is important in order to identify potential issues and to minimize the risk to patients. The Centers for Disease Control and Prevention (CDC) created an algorithm for identifying Ventilator-Associated Events (VAE) in adult patients for reporting to the National Healthcare Safety Network (NHSN). Currently, the primarily manual surveillance tools require a significant amount of time from hospital infection prevention (IP) staff to apply and interpret. This paper describes the implementation of an electronic VAE tool using an internal clinical data repository and an internally developed electronic surveillance system that resulted in a reduction of labor efforts involved in identifying VAE at Barnes Jewish Hospital (BJH).