Some experts believe that evaluating vendors ‘ AI systems within an AI governance model is of crucial importance in the healthcare industry. Vendor-provided equipment may be healthy, effective and honest to provide the best results for all patients. An effective method for identifying and evaluating risks associated with AI systems in medical use should be a part of a management structure. In each class, the danger is categorized according to this approach, and a scoring system is used to determine risk. A health system can make more educated choices about the tools to use, and the technology implementation teams can create hazard mitigation plans that are suitable for the equipment or workflows by highlighting the areas of risk in these categories: Accuracy, transparency, fairness, equity, safety, and privacy. According to Glenn Wasson, UVA Health’s superintendent of analytics, creating a strong governance framework that can interact with vendor products will help healthcare organizations harness the advantages of industrial AI systems while reducing risk and maintaining public trust. He holds a degree in computer technology. Dear AI Vendors: This Is What We Need, a HIMSS25 education program will be addressing this topic. Wasson is in charge of the way the health system gathers and discusses data for patient treatment and research. His responsibilities include data operations, analytics, data science and data visualization. He is responsible for everything in this position, from identifying patient risk modeling to figuring out hospital ranking algorithms. He is particularly interested in how to create problem-solving cultures. We interviewed Wasson for a preview of his HIMSS25 session. Q. What are the differences between AI tools and previous software that hospitals and health systems need to be aware of? A. AI is transforming every aspect of the modern healthcare industry, opening the door to potential improvements in everything from diagnosis and treatment planning to resource management and billing, to drug discovery and research. However, AI systems also carry novel risks that weren’t present in previous software generations. To effectively govern the AI within their enterprise, organizations must understand these risks, as well as their potential effects and mitigations. It’s a difficult question to answer about understanding the risks and benefits of AI systems. Rarely do provider organizations have the resources, training, or talent to analyze vendor code. Instead, this session addresses a dialogue between vendor and provider organizations that can identify potential risk sources. This dialogue will require greater transparency regarding data, algorithms, and workflows, as well as help vendors gain confidence in their tools as a result. Q. How will you be focusing on AI in your HIMSS25 session? A. Artificial intelligence is the subject of this session, which comes in a variety of forms, with generative AI being one of the most recent. AI’s ability to analyze evidence based on prior human experience in the healthcare sector makes it useful in a variety of scenarios. These include diagnosis prediction and treatment selection, personalized medicine, staff scheduling, bed allocation, supply chain operation, remote monitoring, improvements in billing and coding, cost reductions through efficiency, and many more. In this session, we want to talk about the various AI use cases and the decision-making processes that allow AI and humans to collaborate. We’ll discuss how having a sense of the consequences of those decisions and the risks associated with those decisions can help ensure that an AI system will provide the support required in a safe and effective manner. Q. What is one of the various takeaways you anticipate HIMSS25 attendees will bring home with them so they can apply there when they return to their organizations? A. Attendees will leave with an appreciation of the novel aspects of AI system evaluation that were previously unwelcomed in traditional technology evaluation. Through a dialogue ( a series of questions ) between providers and vendors to understand risks and mitigations, we will offer a framework for AI system evaluation. The questions will consider different sources of risk – data sets, workflows, etc. – and will call for both quantitative and qualitative responses. Therefore, this discussion is not intended to be merely a discussion between AI professionals, such as data scientists or statisticians; instead, it should include experts and operators who are familiar with the deployment environment and workflows. Finally, we will discuss how the dynamic learning style of today’s advanced AI requires that this dialogue continue. Wasson’s session,” Dear AI Vendors: This Is What We Need”, is scheduled for Tuesday, March 4, from 10: 15-11: 15 a. m. at HIMSS25 in Las Vegas. Follow Bill’s HIT coverage on Linked In: Bill Siwicki
Email him: bsiwicki@himss .org
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