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

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R42CA180190-02A1
Agency Tracking Number: R42CA180190
Amount: $1,452,365.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: NCI
Solicitation Number: PA15-270
Timeline
Solicitation Year: 2015
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-09-23
Award End Date (Contract End Date): 2019-08-31
Small Business Information
307 W 200 S, SUITE 3004, Salt Lake City, UT, 84101-1282
DUNS: 078649023
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 STEPHANE MEYSTRE
 (843) 792-0015
 meystre@musc.edu
Business Contact
 STEPHANE MEYSTRE
Phone: (843) 792-0015
Email: smeystre@clinacuity.com
Research Institution
 UNIVERSITY OF UTAH
 75 SOUTH 2000 EAST
SALT LAKE CITY, UT, 84112-8930
 Nonprofit college or university
Abstract
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and the removal of the ones that are irrelevant Consequently these electronic lists are often incomplete inaccurate and out of date Clinacuity Inc proposed a new system to automatically extract structured and coded medical problems and allergies from clinical narrative text in the EHR of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 enhance the NLP system performance scalability and quality develop an advanced visualization interface for local adaptation of the NLP system and 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 andquot meaningful useandquot and obtaining Centers for Medicare andamp 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 PUBLIC HEALTH RELEVANCE Medical errors cause numerous deaths and even if some are difficult to avoid many could be prevented 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 such as entries in electronic lists of problems and allergies Such lists are available in most electronic health records but they require manual management and are often incomplete inaccurate and out of date On the other hand clinical text reports contain the majority of the patient information including problems and allergies The overall goal of this project is to develop a new system to automatically extract structured and coded medical problems and allergies from clinical narrative text in the electronic health record

* Information listed above is at the time of submission. *

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