Washington, DC (PRWEB) June 01, 2014
ZyDoc, a NY-based medical transcription and informatics company has unveiled release 2.0 of its powerful NLP-driven MediSapien application at Health Datapalooza, June 1-3, 2014 in Washington, DC. MediSapien, developed by ZyDoc, uses natural language processing (NLP) and surrounding patent-pending technologies to extract medical concepts and ICD-10 codes from unstructured text.
MediSapien offers a dictation-based alternative to capturing patient health data and automates the process of converting text to medical concepts, structured codes and modifiers for use in EHRs, reporting, billing, and analytics. The MediSapien app is web-based, browser-independent, secure and enterprise-ready. It works on any device such as a computer, iPad, or smart phone.
“MediSapien enables physicians to literally ‘talk to their EHR’ to document encounters and automatically generate structured data,” states James Maisel, MD, ZyDoc Chairman. “With MediSapien, text-based clinical documentation is structured and outputted as ICD-10, SNOMED-CT, RxNorm, LOINC, and other coding terminologies, allowing the data to be leveraged for multiple uses including sophisticated query-based reporting, revenue cycle management, and health information exchange between providers for care coordination. As an analytics tool, MediSapien provides structured data for evidence-based care support, at-risk patient identification, practice management, and pharmaceutical or other research studies. MediSapien also solves a myriad of data submission challenges for accountable care, HITECH, and specialized health registry reporting.”
In a recent NIH-funded research study, using MediSapien to generate EHR data from dictation was shown to be an average of 2.5 times faster than using a keyboard and mouse.
“Because MediSapien “understands” natural language, it is not only a faster method of EHR data insertion than conventional typing,” continues Dr. Maisel, “but it also unlocks important trapped meaning that might otherwise remain hidden as unsearchable, unstructured text. With the richer, more granular level of detail that is often included in a doctor’s nuanced observations, the patient health story is more complete, potentially influencing adjustments in treatment to make a measurable difference in outcome.”
Health Datapalooza attendees are invited to stop by ZyDoc’s Booth 18 at the conference for more information about utilizing MediSapien to convert text to medical concepts, structured codes and modifiers. ZyDoc Chairman James Maisel, MD will be on hand to offer a brief demonstration of the new features of MediSapien 2.0. He may also be reached at 516-238-3837 or jmaisel(at)zydoc(dot)com. Appointments for post-show in-depth presentations may be scheduled with Kevin Ross, ZyDoc VP Sales, at 516-509-7309 or kevin.ross(at)zydoc(dot)com.
ZyDoc will also exhibit at the American Association of Orthopaedic Executives (AAOE) 2014, May 31-June 3, in Washington, DC. For a complete list of upcoming events where ZyDoc will be exhibiting or offering presentations, please visit http://www.zydoc.com.
About ZyDoc and MediSapien
ZyDoc, based in Islandia, NY, was founded in 1993 to develop medical informatics technologies. ZyDoc has developed award-winning e-transcription infrastructure and speech recognition technology. Augmenting its transcription business, ZyDoc launched MediSapien, an NLP-powered web-based platform that converts unstructured text to fully coded structured data for EHRs, PACS, RIS, analytics, and reporting. The MediSapien Knowledge Management Platform is powered by disruptive patent-pending proprietary technologies, and NLP technology from Health Fidelity. For clinician end-users, MediSapien can be utilized in conjunction with existing or planned EHR installations, and can facilitate compliance with Meaningful Use mandates. ZyDoc is a certified Philips Reseller and a VMWare Professional Solution Provider Partner.
Research reported in this press release was supported by the National Library of Medicine of the National Institutes of Health under Award Number R43LM011165. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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YouTube: “Study Overview: NLP-Enabled Documentation Improves EHR Usability”