Artificial Intelligence to Strain Health IT Infrastructure

November 09, 2016 - The healthcare industry continues to seek technological advancements to improve the quality of patient care, and artificial intelligence (AI) and machine learning are poised to become a significant part of health IT infrastructure.

As healthcare organizations continue to adapt and improve their electronic health records (EHRs) and interoperability initiatives by applying effective data analytics solutions, technology experts are developing ways for healthcare organizations to apply their data to AI and machine learning.

“Experts forecast that rapid progress in the field of specialized artificial intelligence will continue,” wrote US Chief Technology Officer Megan Smith and John P. Holdren, Assistant to the President for Science and Director, Office of Science and Technology Policy.

“One area of great optimism about AI and machine learning is their potential to improve people’s lives by helping to solve some of the world’s greatest challenges and inefficiencies,” they continued. “Public and private sector investments in basic and applied R&D on AI have already begun reaping major benefits to the public in fields as diverse as healthcare, transportation, the environment, criminal justice, and economic inclusion.”

AI programs are already widely used across many enterprise industries as well as by consumers. Voice command applications (e.g., Apple’s Siri) are widely used for multiple personal and professional purposes, giving the user an advantage by saving time manually entering information into a device. Siri emulates a human interaction and over time gains intelligence by recognizing user patterns.

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The healthcare industry aims to develop and deploy AI solutions that emulate human performance by learning and drawing conclusions based on data patterns. AI implementations will eventually be able to understand and communicate complex data that would be indecipherable or difficult for humans to understand.

Gartner describes AI as applications including autonomous vehicles, automatic speech recognition and generation, and detecting novel concepts and abstractions. Detecting concepts and abstractions is useful for detecting potential new risks and aiding humans to quickly understand very large bodies of ever changing information.

AI applications use machine learning to sort through the data collected by an organization to learn patterns and construct algorithms. For healthcare organizations currently implementing or considering predictive analytics, machine learning can assist doctors and clinicians in detecting cancer and other chronic diseases early based on patterns taken from scans of previous patients. The subtle patterns that likely indicate the beginnings of a chronic illness can use previous positive and negative identifications to adjust the criteria for flagging future images from patients who have not yet received a diagnosis.

Predictive analytics continues to make an impact in patient care quality and patients are coming to expect improved diagnosis and are more willing to contribute data using wearable technology.

“Predictive analytics have already helped providers tackle difficult patient identification and risk stratification activities related to a number of chronic and acute conditions,” says HealthITAnalytics.com. “The rapid proliferation of IoT tools and devices, including wearable fitness trackers, mHealth apps, Bluetooth scales, pill boxes, glucose monitors, and blood pressure cuffs, are allowing patients an unprecedented degree of flexibility when it comes to managing their own health.”

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Artificial intelligence functions on collected data: The more data AI solutions have access to, the more successful its implementation will be. AI solutions with a wide range of data are capable of making more connections. AI solutions become increasingly accurate in detecting, recognizing, and flagging concerning healthcare issues early, the more

The healthcare AI movement is largely connected with the implementation of the Internet of Things (IoT). Patients can potentially contribute health data to the network via wearable or other personal devices contribute to AI machine learning, giving the AI solution more data to reference.

In order for the AI solution to benefit from data collected by mobile, IoT wearable, and other connected medical devices, the data needs to be integrated into the patient’s record in a format compatible with the organization's EHR technology for a data analytics solution to process and AI solution to evaluate it.

The massive amounts of data and the increase in connected devices calls for a serious evaluation and plan for how an organization’s IT infrastructure can support the increases in activity.

Increased network security for IoT devices and advanced network security for organizations sending and receiving data with other organizations is necessary to ensure that the increase in devices accessing the network will be protected from malware.

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Implementing a network defense such as a network access solution (NAC) allows organizations to set clearance protocols for all devices accessing the healthcare network. Organizations currently deploying mobile devices likely already have a virtual private network (VPN) in place, but assessing the VPN and ensuring that it is secure enough to allow data to safely move among healthcare organizations sharing data. Wireless networks may also need to be updated to accommodate heavy traffic from increased connections.

Storage is another concern for AI implementation. Organizations will need to have a least a hybrid cloud environment to store data to increased data demands. On-premise servers are costly and limited to a finite amount of storage space. The AI solutions needs to have access to cloud data constantly to implement machine learning.

Healthcare organizations are still several years away from fully realizing and benefiting from artificial intelligence. Official guidelines and regulations are still needed to ensure protected health data remains secure and AI solutions are thoroughly tested before implementation.

Many healthcare organizations are looking to the future for ways to effectively use collected health data. As technology continues to advance, healthcare organizations are motivated to embrace new technologies that could significantly improve the quality of patient care.

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