SBIR/STTR Research Award

Applying NLP to Free Text as an EHR Data Capture Method to Improve EHR Usability

Award identifier 9df3dfd31235d1e532c1dfa8260b71040507dcbeadf38931228b8590091f2403Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$150,000

Program and phase

SBIR · Phase I

Award year

2012

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Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
ZYDOC MEDICAL TRANSCRIPTION, LLC
Proposal award date
Not reported
Contract end date
Not reported
UEI
LFAXQ2ZT9RU8
Solicitation number
PA11-096
Topic code
NLM

Research abstract

DESCRIPTION (provided by applicant): This proposal aims to ensure the ability of NLP-Standalone-or-Hybrid Documentation, a method of EHR data capture involving Natural Language Processing and possibly also standard EHR data capture, to improve the usability of EHR by reducing documentation time, increasing documentation quality, and increasing clinician satisfaction. Problem to be Addressed. Limited usability of the Electronic Health Record ( EHR ) and lack of standardized terminology impedes EHR adoption and optimal use, and therefore hinders realization of a universally interoperable and evidence-based reportable health care system. Large amounts of time required for documentation, low clinician satisfaction, and incomplete documentation are problemsplaguing EHR. Innovation. Current research has demonstrated that NLP may be used for EHR data capture. ZyDoc is furthering the state of research by assessing the capability of NLP-Standalone-or-Hybrid Documentation to improve EHR usability along several criteria. Long Term Goal. By enabling interoperability and improving EHR usability, through improving clinician satisfaction, improving documentation quality, and reducing data capture time, MediSapien will encourage widespread EHR adoption and optimal usewith structured data. Phase I Summary. The purpose of the first Specific Aim of this grant proposal is to ensure that NLP- Standalone-or-Hybrid Documentation is capable of improving clinician satisfaction, efficiency, and documentation quality, relative tostandard EHR data capture methods. The purpose of the second Specific Aim is to improve the accuracy of MediSapien's coding. These Specific Aims will ensure the technical feasibility of NLP-Standalone-or-Hybrid Documentation and MediSapien for improving EHR usability. Phase II Objectives. In Phase II, ZyDoc will complete product development, beta test MediSapien at two hospitals, and measure the product's impact on clinical outcomes or documentation results. Commercial Opportunity. ZyDoc will offer MediSapien as a modular component by partnering with vendors that combine MediSapien in their own solutions, enabling their clients to meet EHR meaningful use standards. PUBLIC HEALTH RELEVANCE: Limited usability of the Electronic Health Record ( EHR )and lack of standardized terminology impedes EHR adoption and meaningful use, and therefore hinders realization of a universally interoperable and evidence- based reportable health care system. This proposal aims to prove that EHR usability can be increased by applying NLP and other technologies to convert dictated and transcribed unstructured text to structured data and inserting it into the EHR. Achievement of this result will encourage optimal EHR use with searchable, structured data that will enable interoperability.

Technology keywords

Not reported

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Agency intelligence

Department of Health and Human Services

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