Innovation on the Front Lines: Change Management for Successful Digital Health Implementation
By Evan D. Collins MD, MBA, Orthopaedic Surgeon and Chief of the Hand & Upper Extremity Center, Houston Methodist As
Read MoreBy Evan D. Collins MD, MBA, Orthopaedic Surgeon and Chief of the Hand & Upper Extremity Center, Houston Methodist As
Read MoreBy Chris Hunt, AVP Perioperative Services, MultiCare Health System Many, if not all, operating room (OR) committees look at block
Read MoreBy Roberto Torres, Jr., Director, Clinical Technology and Biomedical Engineering, Stanford Medicine Children’s Health One of the most challenging areas
Read MoreIn the current healthcare ecosystem of providers, payors, industry, and (last but not least!) patients, fragmented data creates tremendous friction,
Read MoreBy Dr. Alexis Burnett, VP of Surgical & Procedural Services, Medical City Healthcare The post-pandemic staffing crisis, combined with high
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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