Implementing AI? Heathcare organizations 'must have a specific measurable goal'

Implementing AI? Heathcare organizations 'must have a specific measurable goal'

Tom Hallisey, digital health strategy lead at Columbia Memorial Health, previews his presentation at the HIMSS AI in Healthcare Forum next month, describing rollout best practices and discussing ways to ensure investments are targeted for maximum impact.

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The healthcare industry is in the process of evaluating the suitability of artificial intelligence (AI) for their organizations, with many seeking to expand existing AI and machine learning implementations. An upcoming HIMSS forum will host an educational session on AI adoption in healthcare, targeting healthcare provider organizations. Tom Hallisey, the digital health strategy lead at Columbia Memorial Health, will moderate a panel session titled "A Strategic Guide to Incorporating AI into Your Healthcare Roadmap."

The panelists include Albert Marinez, Chief Analytics Officer at the Cleveland Clinic; Tatyana Fedotova, Director of Global Data, Platforms, and Partnerships at Johnson & Johnson; and Christopher Larkin, Chief Technology Officer at Concord Technologies. The session aims to provide insights into critical questions and decisions at each phase of the AI journey, covering topics such as build versus buy, tool selection, ensuring targeted AI investments, and more.

In a sneak preview, Hallisey emphasizes the importance of identifying the specific problem a healthcare organization aims to solve during the initial phase of an AI journey. He stresses the need for a measured approach, cautioning against projects that aim to find uses for new and exciting AI tools without a clear goal. To ensure AI investments have maximum impact, Hallisey recommends starting a committee to collect and prioritize ideas, guide resource selections, review pilot results, and assist with scaling, with diverse representation.

Regarding long-term success with healthcare AI investments, Hallisey acknowledges the challenges posed by the newness of AI tools and the potential for algorithms to become obsolete as interventions change underlying data. He suggests implementing plans to continually measure the results of each AI tool and intervention, considering factors such as changing demographics and evolving data. The session aims to provide practical insights into navigating the complexities of AI adoption in healthcare.

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