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 problems plaguing 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 use with 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 to standard 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.
Public Health Relevance Statement: 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.
NIH Spending Category: Clinical Research; Networking and Information Technology R&D
Project Terms: Achievement; Address; Adoption; Algorithms; Applications Grants; base; Client; Clinical; clinical care; Code; commercial application; Computer Assisted; Data; Documentation; Electronic Health Record; Ensure; evidence base; expectation; Genetic Transcription; Goals; Health; Healthcare Systems; Hospitals; Hybrids; ICD-10-CM; ICD-9-CM; improved; innovation; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); interoperability; Logical Observation Identifiers Names and Codes; Maps; Measures; Medical Informatics; medical specialties; meetings; Methods; Mus; Natural Language Processing; novel; Outcome; Output; Patients; Phase; Physicians; Plague; Process; product development; prospective; Provider; Records; Relative (related person); Research; research study; Risk; satisfaction; Solutions; Speech; Structure; System; Systematized Nomenclature of Medicine; Teaching Hospitals; Technology; Terminology; Testing; Text; Time; usability; Vendor
