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Navigating the Healthcare AI Landscape: Insights from the HIMSS AI in Healthcare Forum 2023

Table of Content
Healthcare AI Adoption

In healthcare, artificial intelligence (AI) stands out as a transformative force. Hospitals and health systems worldwide are grappling with the decision to adopt AI and machine learning technologies. To shed light on this complex journey, Tom Hallisey, digital health strategy lead at the Healthcare Association of New York, is set to moderate a panel at the 2023 HIMSS AI in Healthcare Forum. The session, titled “A Strategic Guide to Incorporating AI into Your Healthcare Roadmap,” promises a comprehensive exploration of AI adoption best practices.

Defining the Problem: The First Critical Step

In a sneak preview of the upcoming session, Hallisey emphasizes the importance of identifying a specific, measurable goal at the outset of the AI journey. The key question to ask during the beginning phase is, “What problem are we trying to solve?” While AI tools offer a myriad of possibilities, success hinges on a clear objective. Hallisey cautions against the allure of implementing cutting-edge tools without a defined purpose, as such projects are more likely to falter.

Build, Buy, or Tweak: The AI-Specific Dilemma

Once a pilot idea is established, organizations face the crucial decision of whether to build, buy, or tweak an existing AI tool. This choice depends on internal capabilities, privacy and security concerns, scalability, and data/bias considerations. Hallisey underscores the necessity of a careful, measured approach to align the chosen tool with the intended value and organizational capabilities. Projects driven by the desire to showcase new AI capabilities, rather than addressing a specific need, are identified as high-risk ventures.

Maximizing Impact: The Role of Committees and Diversity

To ensure AI investments yield maximum impact, Hallisey recommends the formation of a committee tasked with collecting and prioritizing ideas, guiding resource selections, and reviewing pilot results. The inclusion of a diverse group within this committee is crucial, as it brings varied perspectives that align with the multifaceted challenges of healthcare. Involving business units and end users in the decision-making process ensures that the technology addresses real-world problems and gains essential buy-in for success.

Long-Term Success: A Continuous Measurement Approach

AI tools in healthcare often face challenges in achieving long-term success due to their newness and evolving nature. Hallisey acknowledges the difficulty but stresses the need for continuous measurement and evaluation. Plans must be in place to monitor the results of each AI tool and intervention continually. Recognizing that what works in one context may not be applicable in another, and that AI tools may become obsolete over time, underscores the importance of ongoing assessment.

Navigating Change: Adapting to Shifting Data Landscapes

As AI tools, particularly large language models and clinical algorithms, continue to evolve, healthcare organizations must adapt to shifting data landscapes. Demographic changes, updates in data, and evolving interventions can impact the effectiveness of AI solutions. Hallisey suggests that plans should be flexible and capable of adapting to these changes. He references recent White House executive orders and proposed rules from the ONC as attempts to address concerns related to AI regulations in healthcare.

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