NLP from an end-user perspective
By Hamed Abbaszadegan, MD, MBA, FACP, FAMIA, Program Director, Clinical Informatics Fellowship Program, University of Arizona College of Medicine-Phoenix The
Read moreArtificial Intelligence (AI) is getting increasingly advanced at doing what people do, but more efficiently, more rapidly, and at a much lower cost. AI is transforming health care, molding the business models of life sciences and healthcare organizations as access to data is becoming increasingly democratized. From hospitals to clinical research, and drug discovery, AI applications are transforming the healthcare sector, reducing cost and improving patient care.
By Hamed Abbaszadegan, MD, MBA, FACP, FAMIA, Program Director, Clinical Informatics Fellowship Program, University of Arizona College of Medicine-Phoenix The
Read moreBy Justin Gnau, MHSA, RHIA, CIO, St. Luke’s Health – Texas Division In the health care field, our knowledge and
Read moreBy Richard S. Temple, VP/CIO, Deborah Heart and Lung Center I come to you today as the Vice President and
Read moreBy Tony Ambrozie, SVP & Chief Digital and Information Officer, Baptist Health South Florida One of the most significant changes
Read moreBy Christopher Hutchins, VP, Chief Data & Analytics Officer, Northwell Health Today we are experiencing unprecedented growth in the generation
Read moreBy Brendan Watkins, Chief Analytics Officer, Stanford Children’s Health Tips:1) Leverage the excitement of data exploration and visualization technology rollout.2)
Read moreBy Andy Draper PhD, CIO & Dr. Mark Radlauer, CMIO at HCA Healthcare Continental Division On June 30, 2020, IBM
Read moreBy Marti Strand, VP Revenue Cycle, St Joseph’s Candler The number one problem in the revenue cycle is that we
Read moreBy Robert Rowley, MD Family Medicine Physician & CMO at Hayward Family Care The emerging COVID-19 pandemic has become a
Read moreWithout a foundation of consistency in the collection, a consistency in definition, and consistency in metrics, artificial intelligence within healthcare is a free-floating mass of inconsistent teaching material. No strength in computing or programming can overcome bad data. On the road to meeting the promise of AI in healthcare to improve population health, we must collectively work to ensure that our teaching data is clear, defined, and with visible outcome metrics, whether quality, efficiency, or even costs.
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