HIMSS18 Annual Conference
Wynn Las Vegas
Mar. 5, 2018

Jeffrey Axt

Project Manager, Researcher
Hospital for Special Care

Jeff Axt, DM, MBA, currently works for Hospital for Special Care in New Britain, CT (, in the Information Technology Department where he has been a project manager and systems analyst for over 25 years. He was also an adjunct instructor at the University of Hartford College of Education, Nursing and Health Professions, co-creating one of the first nursing-focused healthcare informatics courses in Connecticut.

He has been involved in clinical research using AI technology for over a decade, working in partnership with clinicians at the hospital as well as other institutions. This includes studies using artificial neural networks for spinal cord injury prognosis and mild traumatic brain injury diagnosis, as well as working with a long-time associate to create a clinical algorithm development center at the hospital. He recently completed his doctoral studies with a dissertation titled, “Artificial Neural Networks: A systematic review of their efficacy as an innovative resource for healthcare practice managers.” He is committed to developing the best methodology for bringing AI technologies into clinical practice, and doing so in ways that the healthcare community engages and embraces, yet making that integration practical enough for software vendors to invest in.

March 5, 2018
3:05pm - 3:30pm

Research literature indicates that human limitations, such as cognitive bias, can interfere with clinical decision-making and that artificial intelligence has the potential to improve the process.

The research also indicates that AI technologies may be viewed as a disruptive innovation to workflow and clinical practice. This mean that to implement AI effectively often requires a cultural change and strategy to promote clinician engagement.

This session will discuss an approach to implementing AI at the point of care that does just that.

Key discussion points:

  • Knowing where opportunities for AI technologies fit into clinical practice workflow.
  • Understanding the risks of mismanaging AI innovation integration.
  • Understanding how the integration of AI technology can optimize clinical practice, reducing both risk and error.

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