AI in healthcare: The unevenly distributed future is here

There is a rapidly growing body of use cases for AI technologies in healthcare. Mainstream adoption may be a bit further along.

artificial intelligence / machine learning
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AI. Cognitive. RPA. Autonomics. Machine learning. Deep learning.

All these terms fly around in IT organizations today as CIOs, battling marketplace uncertainties and cost pressures, look for ways to enhance enterprise performance. As with most technology trends, the hype tends to overhang reality by a significant margin in the early stages of adoption, much in line with Gartner’s hype cycle theory.

Early this year, I wrote a piece that discussed how emerging technologies such as artificial intelligence (AI) and blockchain will drive precision medicine this year. Halfway into the year, the signs are that the use of AI technologies has definitely picked up momentum.

A recent study by consulting firm Accenture provides us some interesting data points. Artificial Intelligence or AI in healthcare is expected to grow more than 10x in the next five years, to around $ 6.6 billion, at a compounded rate of over 40%. AI represents a $150 billion savings opportunity for healthcare, across a wide range of applications: robot-assisted surgery, clinical diagnosis and treatment options, and operational efficiencies, to name a few. In my firm’s work with healthcare technology firms and enterprises, there is definitely a palpable excitement about the growing demand for AI in healthcare. Before unpacking what that means, it may be worthwhile defining some of the terms that are used interchangeably and synonymously with AI.

At the operating levels, autonomics and robotic process automation (RPA) refer to software that runs on pre-determined rules and eliminates the need for human intervention (a good example is fetching benefit eligibility information in a health plan or managing routine IT infrastructure operations). In many cases, these tools – sometimes referred to as “bots” – learn from patterns of requests and remediate/update their algorithms to respond in a more intelligent fashion over time. At higher levels of application, cognitive and AI systems aim to “mimic” humans in terms of reasoning and judgment based on techniques such as neural networks and Bayesian models that help these technologies come close to making decisions in a human-like manner. However, as IBM CEO Ginni Rometty points out, these techniques are more about augmenting human intelligence today, not replacing it (man and machine, not man vs. machine).

There is no doubt that these emerging technologies can transform healthcare. There is a rapidly growing body of use cases and successful applications of AI in operational and clinical areas. Here are a few examples of how AI technologies are currently being applied in the healthcare and life sciences sectors.

Health plans: There is considerable traction today applying RPA tools and AI technologies for improving productivity and efficiencies in health plans. By codifying workflow rules and enabling self-learning through ontological patterns and databases, these technologies are being used in areas such as provider data management, claim approvals and exception management, fraud detection, and customer service operations.

Health systems: AI and automation tools have found wide applications in a range of functions including revenue cycle operations, diagnosis and treatment, and population health management initiatives. IBM’s Watson Health engine, for example, has made significant strides in applying cognitive and AI technologies in the field of oncology and diabetic retinopathy, allowing the search and analysis of vast amounts of data and knowledge to provide clinicians with inputs for targeted intervention options.

Life sciences: Pharma companies have started successfully applying AI tools in clinical trial phases of new drugs by automatically generating content required for regulatory submissions and reviews. On the other side of the equation, these tools are being applied in pharmacovigilance for case intake and reporting on the adverse effects of drugs. There is increasing interest in the use of AI for improving efficiencies in supply chain operations. 

Across all of these segments, there are several commonly used applications, an example of which is the use of AI technologies for IT infrastructure operations in detecting and remediating network errors and application failures. Another example is the use of AI in patient engagement programs, especially for managing chronic conditions such as diabetes through automated alerts and interventions based on analysis of real-time data gathered through intelligent devices and wearables.

As the use of AI technologies gains momentum, more use cases will surely emerge. As healthcare transitions from a fee-for-service to a value-based care era, the need for advanced technologies for everything from precision medicine to increased operational efficiencies and improved patient engagement will drive the adoption rates for these technologies. Many of these initial projects are in pilot phases, and in the broader context, there is a relatively small number of healthcare enterprises that are investing in these technologies and programs. That is par for the course for new technologies in any field. Mainstream adoption may be a bit further away, and in the current environment of policy uncertainty, many of the smaller enterprises are likely to be in wait and watch mode, choosing to stay with business as usual till there is some clarity.

To paraphrase the sci-fi writer William Gibson, the future is already here, only it is unevenly distributed. This may be the most accurate summary of AI in healthcare at this time.       

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