Vanderbilt University has a widely adopted patient portal, MyHealthAtVanderbilt, which provides an infrastructure to deliver information that can empower patient decision making and enhance personalized healthcare. An...
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Vanderbilt University has a widely adopted patient portal, MyHealthAtVanderbilt, which provides an infrastructure to deliver information that can empower patient decision making and enhance personalized healthcare. An interdisciplinary team has developed Flu Tool, a decision-support application targeted to patients with influenza-like illness and designed to be integrated into a patient portal. Flu Tool enables patients to make informed decisions about the level of care they require and guides them to seek timely treatment as appropriate. A pilot version of Flu Tool was deployed for a 9-week period during the 2010-2011 influenza season. During this time, Flu Tool was accessed 4040 times, and 1017 individual patients seen in the institution were diagnosed as having influenza. This early experience with Flu Tool suggests that healthcare consumers are willing to use patient-targeted decision support. The design, implementation, and lessons learned from the pilot release of Flu Tool are described as guidance for institutions implementing decision support through a patient portal infrastructure.
Objective The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities including medical problems, tests, and treatments, as well as their asserted status from ho...
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Objective The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities including medical problems, tests, and treatments, as well as their asserted status from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge. Design The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes. Measurements Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge. Results and discussion Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically
Explicit guidelines are needed to develop safe and effective patient portals. This paper proposes general principles, policies, and procedures for patient portal functionality based on MyHealthAtVanderbilt (MHAV), a r...
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Explicit guidelines are needed to develop safe and effective patient portals. This paper proposes general principles, policies, and procedures for patient portal functionality based on MyHealthAtVanderbilt (MHAV), a robust portal for Vanderbilt University Medical Center. We describe policies and procedures designed to govern popular portal functions, address common user concerns, and support adoption. We present the results of our approach as overall and function-specific usage data. Five years after implementation, MHAV has over 129 800 users;45% have used bi-directional messaging;52% have viewed test results and 45% have viewed other medical record data;30% have accessed health education materials;39% have scheduled appointments;and 29% have managed a medical bill. Our policies and procedures have supported widespread adoption and use of MHAV. We believe other healthcare organizations could employ our general guidelines and lessons learned to facilitate portal implementation and usage.
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