By Gurjeet Singh

Artificial intelligence (AI) and machine learning have become catchphrases for the latest generation of vaporware. Almost any intelligent application or analytic software can now be classified as “AI”, especially if it can be applied to large data sets which, by definition, are what health care runs on. But in health care, the term AI refers not only to technology, but also to a specific approach that must follow certain rules to have any real impact on clinical care.

Take population health management, which is being increasingly adopted by health care organizations so that they can succeed under value-based reimbursement. To maintain and improve the health of their patients, health care organizations need a way to understand and harness the huge amounts of data that could potentially be applied to achieving these goals. Artificial intelligence systems can derive actionable insights from large, complex data sets at the scale required by health care enterprises. AI can also uncover subtle predictive trends that traditional analytics platforms may miss. But it can do this only if it is deployed in the right way.

For starters, an AI solution must aggregate and normalize the financial and clinical data from health care information systems, along with claims data from payers, in some kind of cloud-based infrastructure such as the Hadoop framework. The AI software must be live in an organization’s clinical and business processes, and must be able to process data in near-real time to be of value in clinical and financial decisions.

Beyond that, there are five components that an AI platform needs in order to deliver the results that health care organizations seek. These can be summarized under the headings of Discover, Predict, Justify, Act and Learn, as follows.

Discover
An AI platform must be capable of performing unsupervised learning. Unsupervised learning is critical because, in large and complex data sets such as those in health care, the odds of asking the right question of your data is effectively zero. AI needs to discover all of the patterns or relationships that exist in the data, independent of human input.

For example, a health care organization might employ AI to automatically discover groups of patients who share certain kinds of characteristics. These groups, e.g.: low-income, opioid-addicted, obese patients who live alone and have two or more chronic diseases, might be targeted with personalized interventions and care paths. AI can identify these kinds of subgroups without being told what to look for. This can dramatically increase a doctor’s ability to craft care plans for people in this subpopulation. It can also help health care organizations design customized campaigns to address the medical and social needs of these patients.

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