Chicago, IL (PRWEB) December 01, 2013
ZyDoc, an Islandia, NY-based medical informatics company, will present SpeechDoc™ featuring MediSapien structured reporting linked to Philips SpeechExec Enterprise at the 2013 Radiological Society of North America (RSNA) Annual Meeting, Booth 6711. ZyDoc Chairman James M. Maisel, MD will also demonstrate Philips SpeechExec Enterprise Mobile at the RSNA Mobile Connect event.
A leader in web-based medical transcription and related technologies for over a decade, ZyDoc has announced the new SpeechDoc™ Documentation Platform, featuring Philips SpeechExec Enterprise (SEE) voice productivity solutions and MediSapien™ structured reporting with a radiology-optimized interface for PACS, RIS, and EHRs. Users can dictate and obtain structured reporting with analytic capabilities.
The Philips SpeechExec Enterprise in concert with ZyDoc’s MediSapien SpeechDoc Platform features the SpeechMike Air with slide controls, mobile recorders and smartphone dictation with speech recognition, automated structured reporting, EHR insertion, and HL-7 interfaces to PACS, RIS and EHR systems. Dictations are securely sent to the Philips SpeechExec Enterprise documentation system for speech recognition utilizing customized ZyDoc radiology language models and enhanced applications. NLP and other patent-pending technologies perform structured reporting and analytic reporting. These integrated voice workflow solutions for clinical documentation add powerful functionality from Philips to the ZyDoc transcription platform.
Expanded features now available to ZyDoc transcription clients and Philips Channel Partners include:
According to James M. Maisel, MD, ZyDoc Chairman, “Our NIH grant-funded study proved that dictation is 61% more efficient than conventional EHR data entry. Our NLP solution is preferred by doctors and generates structured data required by new standards. Radiologists can continue to dictate using the Philips SpeechExec Enterprise voice productivity solutions with ZyDoc enhancements. MediSapien accepts transcription and leverages legacy data with automatic extraction of structured ICD-10, SNOMED, LOINC and RxNorm data for insertion into EHRs stylized for analytic reporting.” Dr. Maisel further states, “ZyDoc allows radiologists to transition to EHR with no change in work habits, obtaining the structured data demanded by industry.”
Dr. Maisel will also present two brief scientific talks on conversion of unstructured text into structured data, ICD-10 readiness and analytics for radiology in the RSNA 2013 Educational Sessions, Radiological Informatics Series on Tuesday, December 3 at 9:15 AM in Room S502AB, South Building, Level 5. A frequent guest speaker on innovation and emerging technology in medical knowledge management, including speech recognition, natural language processing, and data mining, he most recently presented research on “Using Unstructured Data Sources for Analytics in a Retinal Practice” at the 2013 American Society of Retina Specialists (ASRS) in Toronto; and “Analytics for ACO Patient Management” at the AHIMA 85th Annual Convention Innovation Pavilion. A full schedule and details of these presentations are available at 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 solutions. 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 reseller of the Philips SpeechExec Enterprise Solution, and a Speech Processing Solutions partner.
Research reported in this publication 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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