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James M. Maisel, M.D.

James M. Maisel, M.D.
Chairman, ZyDoc

Website | @zydoc_marketing

ZyDoc Chairman James M. Maisel, M.D. has unique medical informatics experience as CEO of ZyDoc and Retina Group of NY. Since training as the Esther Dyson Foundation vitreoretinal fellow at NY Presbyterian Cornell, he has served thousands of ophthalmology patients, and diabetic patients and their health care providers. He is an educator for patients and for diabetic educators and professionals, and a pharmaceutical medical advisor.

Formerly CEO of HOST, Dr. Maisel led ZyDoc in developing informatics technologies including a prototype EMR purchased by the DOD, speech recognition, e-transcription, and NLP. He is currently Principal Investigator for an NIH grant-funded study, “Applying NLP to Free Text as an EHR Data Capture Method to Improve EHR Usability,” involving ZyDoc’s NLP-powered MediSapien™ knowledge management platform. The grant funding was awarded to ZyDoc through a Phase I SBIR grant of the National Library of Medicine of the National Institutes of Health. In mid-2012, in competition against MModal, Nuance, and others, ZyDoc was awarded a contract by the State of Alaska Department of Health and Social Services Division of Behavioral Health / Alaska Psychiatric Institute (API) to provide an enterprise-wide voice recognition, transcription and dictation system including MediSapien reporting. Under Dr. Maisel’s leadership, ZyDoc has also won numerous awards from the medical informatics, speech recognition and computing industries, including: LISA Awards for Business Productivity, Transcription Workflow, EMR and Transcription; Tablet PC Healthcare Productivity Award; SpeechTECHNOLOGY Innovators Award; AMA Technology Summit Nominee; and Medical Records Institute 3rd National Medical Transcription Business Recognition Program.

Sessions

Our NIH-funded study hypothesizes that a new method of EHR data capture using Natural Language Processing (“NLP”) would improve EHR usability and documentation quality. With test subjects from various specialties, we compared the NLP-Based method against the standard EHR data capture method. Learn the surprising results of our study. Read more.

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