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Automated Problem and Allergy Lists Enrichment Based on High Accuracy Information Extraction from the Electronic Health Record

基于电子健康记录中高精度信息提取的自动化问题和过敏列表丰富

基本信息

批准号：
9138574
负责人：
STEPHANE MEYSTRE
金额：
$ 68.49万
依托单位：
CLINACUITY,INC.
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-01 至 2018-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9138574
关键词：
Adverse drug event Cancer Patient Caring Cessation of life Cities Clinical Code Communications Media Complex Development Disease Electronic Health Record Electronics Ensure Environment Excision Goals Health Insurance Portability and Accountability Act Health Personnel Healthcare Hospitals Huntsman Cancer Institute at the University of Utah Hybrids Hypersensitivity Imagery Incentives Injury Inpatients Institutes Laboratories Malignant Neoplasms Manuals Marketing Medical Medical Errors Medication Errors Methods Natural Language Processing Outpatients Output Patients Performance Pharmaceutical Preparations Phase Process Reference Standards Reporting Risk Sampling Secure Site Sodium Chloride Speed Structure System Test Result Testing Text Time Training United States Centers for Medicare and Medicaid Services Universities Utah Work base commercial application commercialization computerized physician order entry design improved payment prevent processing speed prototype public health relevance software development standard measure usability web services

项目摘要

DESCRIPTION (provided by applicant): Medical errors are recognized as the cause of numerous deaths, and even if some are difficult to avoid, many are preventable. Computerized physician order-entry systems with decision support have been proposed to reduce this risk of medication errors, but these systems rely on structured and coded information in the electronic health record (EHR). Unfortunately, a substantial proportion of the information available in the EHR is only mentioned in narrative clinical documents. Electronic lists of problems and allergies are available in most EHRs, but they require manual management by their users, to add new problems, modify existing ones, and the removal of the ones that are irrelevant. Consequently, these electronic lists are often incomplete, inaccurate, and out of date. Clinacuity, Inc. proposed a new system to automatically extract structured and coded medical problems and allergies from clinical narrative text in the EHR of patients suffering from cancer, and established its feasibility. To advance this new system from a prototype to an accurate, adaptable, and robust system, integrated into the commercial EHR system used in our implementation and testing site (Huntsman Cancer Institute and University of Utah Hospital, Salt Lake City, Utah), and ready for commercialization efforts, we will work on the following aims: 1) enhance the NLP system performance, scalability, and quality, 2) develop an advanced visualization interface for local adaptation of the NLP system, and 3) integrate the NLP system with a commercial EHR system. A large and varied reference standard for training and testing the information extraction application will also be developed, a reference standard including a random sample of de-identified clinical narratives from patients treated at the Huntsman Cancer Institute and at the University of Utah Hospital (Salt Lake City, Utah), with problems and allergies annotated by domain experts. Commercial application: The system Clinacuity proposes will not only help healthcare providers maintain complete and timely lists of problems and allergies, providing them with an efficient overview of a patient, but also help healthcare organizations attain meaningful use requirements. The proposed system has potential commercial applications in inpatient and outpatient settings, increasing the efficiency of busy healthcare providers by saving time, and aiding healthcare organizations in demonstrating "meaningful use" and obtaining Centers for Medicare & Medicaid Services incentive payments. Clinacuity will further extend the commercial potential of the system and its output, using modular design principles allowing utilization of each module independently, and enhancing its local adaptability for easier deployment.