AI-Based Automation Framework For Healthcare

By Cynthia Burghard, research director, IDC Health Insights.

Cynthia Burghard

Artificial intelligence (AI) has two faces in healthcare. One face sings the praises of AI as the tonic that will enable healthcare to deliver better clinical outcomes at a lower cost and the second face is full of skepticism and raises barriers to adoption at every turn. It is heartening to see that a third face is emerging, the thoughtful and appropriate use of AI to predict adverse health events; to identify and stratify patients in need of health, social, and human services; and the application of AI in the automation of tasks, activities, and processes.

To understand the likely evolution of AI-based automation, it’s important to evaluate the interaction of humans and machines across these five levels. At each level of automation, the following questions must be asked and answered:

  1. Who produces insights? – Does the human or the machine (AI) analyze data and deliver insights from such analysis? Does the human or the machine describe what something is, how it trends, why something is happening, and what might happen next?
  2. Who decides and how? – Once all relevant analysis has been conducted, does the human or the machine make the decision based on the derived insights?
  3. Who acts based on the decision? – Finally, a decision should lead to an action by either a human or a machine? The action can be in the digital or physical environment.

Based on the responses to these questions, IDC has identified the following five levels of AI-based automation:

  1. Human Led – At the first level, it is the human who analyzes the data using limited technology, such as tools for only descriptive analytics; it is the human who makes the decision based on the analysis (or experience); and it is the human who acts based on the decision.
  2. Human Led, Machine Supported – At the second level, the human continues to lead data analysis, decision making, and action steps but is now more reliant on the machine across these steps.
  3. Machine-led, Human Supported – At the third level, it is the machine that is using a wide range of analytic and AI techniques to conduct the analysis and produce insights. These insights are reviewed by humans. The human still makes the decision based on machine’s recommendations, and it is the human who acts based on the decision. However, at this level, the machine acts to provide oversight over human decision making and execution.
  4. Machine Led, Human Governed – At the fourth level, the machine analyzes data and produces insights without the need for human review. At this level, the machine decides based on the analysis of all available data and a framework of human-developed governance policies and procedures. At this stage, it is also the machine that acts based on the decision under the governance of humans.
  5. Machine Led – At the fifth level, the world has likely achieved general AI. At this stage, there is a full AI-based automation without the need for human involvement. At this level, we need to think of machines that set their own goals and understand all mathematical, economic, legal, and other external constraints. Most AI academics and experts in labs of commercial enterprises predict this level of AI to arrive no sooner than in about 50 years.

In recent years, one of the shortcomings in the commercial sphere of AI has been the misrepresentation of the scope of possible automation. Too often, we hear claims of AI systems automating end-to-end processes and predictions of massive labor losses, this does a disservice to organizations trying to plan for the appropriate level of investment in AI. There is a need for a pragmatic framework that decision makers across industries can use to assess opportunities and risks of AI-based automation. The levels of AI-based automation must also be viewed in the context of the scope of automation. We define this scope where:

IDC’s AI automation framework was developed to help wade through the hyperbole associated with AI.  Our goal is to help provide a planning tool and key piece of vendor evaluations processes to fully understand the role AI is playing in software and guide strategic decision making.

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