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- Creators: Arizona State University
- Creators: Computer Science and Engineering Program
- Resource Type: Text
Description
Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.
ContributorsRawal, Samarth Chetan (Author) / Baral, Chitta (Thesis director) / Anwar, Saadat (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Clinicians confront formidable challenges with information management and coordination activities. When not properly integrated into clinical workflow, technologies can further burden clinicians’ cognitive resources, which is associated with medical errors and risks to patient safety. An understanding of workflow is necessary to redesign information technologies (IT) that better support clinical processes. This is particularly important in surgical care, which is among the most clinical and resource intensive settings in healthcare, and is associated with a high rate of adverse events. There are a growing number of tools to study workflow; however, few produce the kinds of in-depth analyses needed to understand health IT-mediated workflow. The goals of this research are to: (1) investigate and model workflow and communication processes across technologies and care team members in post-operative hospital care; (2) introduce a mixed-method framework, and (3) demonstrate the framework by examining two health IT-mediated tasks. This research draws on distributed cognition and cognitive engineering theories to develop a micro-analytic strategy in which workflow is broken down into constituent people, artifacts, information, and the interactions between them. It models the interactions that enable information flow across people and artifacts, and identifies dependencies between them. This research found that clinicians manage information in particular ways to facilitate planned and emergent decision-making and coordination processes. Barriers to information flow include frequent information transfers, clinical reasoning absent in documents, conflicting and redundant data across documents and applications, and that clinicians are burdened as information managers. This research also shows there is enormous variation in how clinicians interact with electronic health records (EHRs) to complete routine tasks. Variation is best evidenced by patterns that occur for only one patient case and patterns that contain repeated events. Variation is associated with the users’ experience (EHR and clinical), patient case complexity, and a lack of cognitive support provided by the system to help the user find and synthesize information. The methodology is used to assess how health IT can be improved to better support clinicians’ information management and coordination processes (e.g., context-sensitive design), and to inform how resources can best be allocated for clinician observation and training.
ContributorsFurniss, Stephanie Kohli (Author) / Kaufman, David R. (Thesis advisor) / Grando, M. Adela (Committee member) / Johnson, William G. (Committee member) / Arizona State University (Publisher)
Created2017
Description
This thesis concerns the adoption of health information technology in the medical sector, specifically electronic health records (EHRs). EHRs have been seen as a great benefit to the healthcare system and will improve the quality of patient care. The federal government, has seen the benefit EHRs can offer, has been advocating the use and adoption of EHR for nearly a decade now. They have created policies that guide medical providers on how to implement EHRs. However, this thesis concerns the attitudes medical providers in Phoenix have towards government implementation. By interviewing these individuals and cross-referencing their answers with the literature this thesis wants to discover the pitfalls of federal government policy toward EHR implementation and EHR implementation in general. What this thesis found was that there are pitfalls that the federal government has failed to address including loss of provider productivity, lack of interoperability, and workflow improvement. However, the providers do say there is still a place for government to be involved in the implementation of EHR.
ContributorsKaldawi, Nicholas Emad (Author) / Lewis, Paul (Thesis director) / Cortese, Denis (Committee member) / Jones, Ruth (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2013-05
Description
The traditional model of assessing and treating behavioral health (BH) and physical health (PH) in silos is inadequate for supporting whole-person health and wellness. The integration of BH and PH may result in better care quality, patient-provider experiences, outcomes, and reduced costs. Cross-organizational health data sharing between BH and PH providers is critical to patients with BH conditions (BHCs). In the last few decades, many initiatives -including health information exchange organizations- have facilitated cross-organizational health data sharing. The current challenge is affording meaningful consent and ensuring patient privacy, two of the core requirements for advancing the adoption and use of health information technology (HIT) in the US. The Office of the National Coordinator for HIT (ONC) recommends that patients should be given granular control beyond the “share all” or “share none” approach widely used currently in consent practices. But there is no consensus on the variables relevant to promote granularity in data sharing to honor privacy satisfaction for patients. As a result, existing granular data sharing (GDS) studies use ad-hoc and non-standardized approaches to implement or investigate patient data sharing preferences. Novel informatics methods were proposed and piloted to support patient-driven GDS and to validate the suitability and applicability of such methods in clinical environments. The hypotheses were: H1) the variables recommended by the ONC are relevant to support GDS; H2) there is diversity in medical record sharing preferences of individuals with BHCs; and H3) the most frequently used sensitive data taxonomy captures sensitive data sharing preferences of patients with BHCs. Findings validated the study hypotheses by proposing an innovative standards-based GDS framework, validating the framework with the design and pilot testing of a clinical decision support system with 209 patients with BHCs, validating with patients the adequacy of the most frequently used sensitive data taxonomy, and systematically exploring data privacy views and data sharing perceptions of patients with BHCs. This research built the foundations for a new generation of future data segmentation methods and tools that advances the vision of the ONC of creating standards-based, interoperable models to share sensitive health information in compliance with patients’ data privacy preferences.
ContributorsKarway, George K (Author) / Grando, Adela Maria (Thesis advisor) / Murcko, Anita C (Committee member) / Franczak, Michael (Committee member) / Arizona State University (Publisher)
Created2022