19,164 research outputs found

    Multimodal Machine Learning for Automated ICD Coding

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    This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

    The Promise of Health Information Technology: Ensuring that Florida's Children Benefit

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    Substantial policy interest in supporting the adoption of Health Information Technology (HIT) by the public and private sectors over the last 5 -- 7 years, was spurred in particular by the release of multiple Institute of Medicine reports documenting the widespread occurrence of medical errors and poor quality of care (Institute of Medicine, 1999 & 2001). However, efforts to focus on issues unique to children's health have been left out of many of initiatives. The purpose of this report is to identify strategies that can be taken by public and private entities to promote the use of HIT among providers who serve children in Florida

    Transforming Healthcare Quality through Information Tehnology

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    Information and information exchange are crucial to the delivery of care on all levels of the health care delivery system—the patient, the care team, the health care organization, and the encompassing political-economic environment. To diagnose and treat individual patients effectively, individual care providers and care teams must have access to at least three major types of clinical information—the patient’s health record, the rapidly changing medical-evidence base, and provider orders guiding the process of patient care. In this frame, Information Technology can help healthcare organizations improve the quality of care that they provide, improve patient safety, improve cost-effectiveness, accelerate the translation of research findings into practice, improve care for the medically underserved, increase consumer involvement, improve accuracy and privacy, and increase their ability to monitor health nationally. Consequently, in the present article are presented some implementations of Information and Communication Technologies in the Health Care field.Healthcare; Quality; Information and Communication Technologies

    Widespread Adoption of Information Technology in Primary Care Physician Offices in Denmark: A Case Study

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    Describes the use of electronic medical records, standardized clinical communications, and patient identification numbers by Denmark's primary care physicians; a nonprofit organization's role in implementation and certification; and elements of success

    The Electronic Health Record Scorecard: A Measure of Utilization and Communication Skills

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    As the adoption rate of electronic health records (EHRs) in the United States continues to grow, both providers and patients will need to adapt to the reality of a third actor being present during the visit encounter. The purpose of this project is to provide insight on “best” practice patterns for effective communication and efficient use of the EHR in the clinical practice setting. Through the development of a comprehensive scorecard, this project assessed current status of EHR use and communication skills among health care providers in various clinical practice settings. Anticipated benefits of this project are increased comfortability in interfacing with the EHR and increased satisfaction on the part of the provider as well as the patient. Serving as a benchmark, this assessment has the potential to help guide future health information technology development, training, and education for both students and health care providers

    Health informatics domain knowledge analysis: An information technology perspective

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    Health Informatics is an intersection of information technology, several disciplines of medicine and health care. It sits at the common frontiers of health care services including patient centric, processes driven and procedural centric care. From the information technology perspective it can be viewed as computer application in medical and/or health processes for delivering better health care solutions. In spite of the exaggerated hype, this field is having a major impact in health care solutions, in particular health care deliveries, decision making, medical devices and allied health care industries. It also affords enormous research opportunities for new methodological development. Despite the obvious connections between Medical Informatics, Nursing Informatics and Health Informatics, most of the methodologies and approaches used in Health Informatics have so far originated from health system management, care aspects and medical diagnostic. This paper explores reasoning for domain knowledge analysis that would establish Health Informatics as a domain and recognised as an intellectual discipline in its own right

    Electronic Information Sharing to Improve Post-Acute Care Transitions

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    Hospitals frequently transfer patients to skilled nursing facilities (SNFs) for post-acute care; information sharing between these settings is critical to ensure safe and effective transitions. Recent policy and payer initiatives have encouraged hospitals and SNFs to work together towards improving these care transitions, and associated patient outcomes such as avoidable re-hospitalizations. Exchanging information electronically, through health information exchange (HIE), can help facilitate information transfer, and has shown benefits to patient care in other contexts. But, it is unclear whether this evidence translates to the post-acute care context given the vulnerability of this patient population and complexities specific to coordination between acute and post-acute care settings. Chapter One estimates the national prevalence of hospital’s engagement in HIE with post-acute providers, and explores potential factors prompting this investment. 56% of hospitals report some level of HIE with post-acute care providers. This investment appears strategically to be more incidental than intentional; hospitals’ overall level of sophistication and investment in electronic health records and HIE strongly predicts whether HIE is occurring in the post-acute transition context. However, we see some evidence of association between participation in delivery and payment reforms and hospital use of HIE with post-acute providers. This suggests that HIE may increasingly be considered part of a comprehensive strategy to improve coordination between hospitals and post-acute care providers, though may lack the necessary customization to achieve meaningful value in this context. Chapter Two utilizes a difference-in-differences approach to assess HIE impact on patient outcomes in the post-acute context, exploiting one focal hospital’s selective implementation of HIE with just three partnering local SNFs. I find no measurable effect of HIE implementation on patient likelihood of re-hospitalization, relative to patients discharged to SNFs without HIE. However, log files that capture when and how these SNF providers use available HIE technology reveal significant variation in usage patterns. HIE was more often utilized following discharge situations where transitional care workflows may not be particularly robust, such as discharge from the ED or observation rather than an inpatient unit. However, the system was less likely to be used for more complex patients, and for patients discharged on the weekend – when SNFs operate at reduced staffing and may not have the bandwidth to leverage available technology. When we connect variation in usage patterns to likelihood of readmission, realizing patient care benefits depended on the timing (relative to patient transfer) and intensity (depth of information retrieved) of use. Chapter Three employs qualitative methods – semi-structured interviews with the focal hospital and five proximate SNFs – to better understand hospital-to-SNF transitions, and perceived opportunities and challenges in using HIE functionality to address information gaps. We capture five specific dimensions of information discontinuity; utilizing IT to address these issues is hindered by lack of process optimization from a sociotechnical perspective. Some SNFs lacked workflows to connect those with HIE access to the staff seeking information. Further, all facilities struggled with physician-centric transition processes that restricted availability of critical nursing and social work documentation, and promoted organizational changes that strengthened physician-to-physician handoff while unintentionally weakening inter-organizational transitional care processes. HIE has the potential to address information discontinuity that compromises post-acute transitions of care. These findings facilitate targeted efforts to help hospitals and SNFs pursue HIE in ways that are most likely to result in improved care quality and patient outcomes.PHDHealth Services Organization & PolicyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146031/1/dacross_1.pd

    Towards a New Science of a Clinical Data Intelligence

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    In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201
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