AI-Based Automation Framework For Healthcare

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:

  • Task is the smallest possible unit of work
    performed on behalf of an activity.
  • Activity is a collection of related tasks to be
    completed to achieve the objective.
  • Process is a series of related activities that
    produces a specific output.
  • System (or an ecosystem) is a set of connected
    processes.

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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