Healthcare organizations exploit AI solutions to support better patient results in many areas, including for screening and early detection of diseases. Until now, the FDA has approved over 1000 algorithms for healthcare, many of which are associated with early detection of diseases.1
To obtain the greatest value from these AI tools, including algorithms that can identify patients at risk of some cancers and other chronic diseases, health care organizations need trouble -free data integration and robust cyber security. Such interoperability, however, remains hard for organizations fighting to include AI solutions in increasingly sophisticated IT systems. “The liquidity of data becomes so important,” said Okan Ekinci, a cardiologist and global head of digital technology and CMIO at Roche Information Solutions. “Why? Because we need to make sure that medical algorithms based on artificial intelligence can access the appropriate data to all patients at the right time.”
Since health care becomes more decentralized in the US, interoperability is crucial for aggregating data from various sources to ensure a longitudinal look at the medical history of patients and possible risk factors. “The health care of the future consists in connected integrated systems … [providing] Hyper-Person and Precise Care, “added Sunil Dadlani, Executive Vice President, Information and Digital Director and Cyber security director at Atlantic Health System. “Data, interoperability and cyber security are the basic key challenges that we need to solve” to accept and scale these systems, including AI screening algorithms.
Organizations can successfully implement screening solutions related to AI, combining neat frames with strategic data integration. Altamed Health Services, qualified federally independent community center in southern California, demonstrated the effectiveness of this approach, using artificial integration to integrate the data of claims in 13 health plans and improving results in its value -based care model. According to Raymond Lowe, the senior vice president of the organization and information director, this reduced unnecessary tests by 30%. “In the current financial climate of decreasing revenues … We must be very well thought out, how effectively we use our tools,” he noted.
Datlani noticed that the successful implementation of AI algorithms requires a solid management process, including interfunctional leadership representing various service lines, such as legal, compliance, financial and technology. Organizations must also understand the demographic data of patient population and anticipated health needs. “You must have a good view on the community that you serve, how there will be a change in terms of a mix of matters related to your demographic change,” he explained.
Bearing in mind this demographic data, Lowe said that the analytics can support leaders demonstrate potential benefits about the health of the population. “Look what high -quality drivers are and connect it with the areas [of care] They are very expensive, “he advised, emphasizing the need to adapt the implementation of AI with dazzling business goals and value -based care goals. Focusing on four times AIM can support in healthcare systems in the most effective justification for the implementation of such tools to escalate patients’ results.
According to Ekinci, Roche supports the efforts of healthcare institutions to implement and support AI solutions by developing platforms that relate to cyber security and data privacy. These platforms may combine multimodal data to support clinical decision making, but require human supervision in their design and exploit. “We must make sure that people are involved [in developing medical algorithms]Summed up Ekinci. “Our goal is definitely to create a possible knowledge that improves care, with four times health care based on the background.”
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- US Food & Drug Administration. On December 20, 2024, medical devices with artificial intelligence and machine learning (AI/ML). https://www.fda.gov/medical-devices/software-medical-device-samd/artficial-intelligence-and-machine-learning-aiml-enabled-edical-devices.