The last time I thought much about Maslow’s hierarchy of needs was in a high school psychology class years ago. Abraham Maslow’s hierarchy presents
six ascending layers of need that all humans feel, and was originally developed
by the American psychologist in a paper that he published in 1943.

My thoughts about needs hierarchies were rekindled during a recent call that IBM Research held with industry analysts. The topic was some of
the latest work being done on the IBM Watson computer, big data analytics,
healthcare, and emergent cognitive computing technologies.

IBM Watson and a new hierarchy of healthcare needs

Watson is perhaps most widely known for its impressive performance against human challengers in the TV game show Jeopardy,
but in industries like healthcare, Watson’s role is collaborative; it joins
forces with human efforts to arrive at healthcare diagnoses.

In the analyst call, Michael Karasick, IBM VP of
Innovations, described a new type of healthcare hierarchy of needs when he
spoke of Watson’s evolution.

  • At the ground level, this hierarchy began with assistance
    that the computer would be able to give medical practitioners in their
    diagnosis and treatment of patients.
  • A second, higher tier of needs then
    progressed to understanding, where Watson mapped certain patterns and conditions
    pertaining to the patient, based upon what the practitioner input.
  • The third,
    next highest tier, consisted of decisions on potential diagnoses, which were based
    upon the earlier inputs from practitioners and the mapping of conditions and
    patterns.
  • The fourth and highest tier of need, known as
    discovery, is where the analytics peruse a vast body of papers and other subject
    matter expert inputs related to research on the condition being studied.

IBM Research described how students at the Cleveland Clinic were
using Watson as an assistant and as a vehicle of research understanding while
they worked through complicated use cases of patients to arrive at diagnoses of
diseases and conditions.

Information was fed into Watson, and then Watson formulated pattern mapping into “understandings.” The next step was for the
analytics to come up with a list of high-level factors related to the condition
being studied, and to paint pathways from left to right on a computer screen
between these high-level factors and the most likely diagnoses, which were on
the right-hand side of the screen. Running from left to right, paths connected
high-level factors with likely diagnoses, with the broadest paths indicating
the most likely diagnoses based upon input and analysis. Students could drill down into each high-level factor to look at its contributing
elements more microscopically. Based upon their research and reasoning,
students could either key in agreement or disagreement with the machine’s conclusions. The process enabled collaboration and communication between human and machine in a collective analytics effort to find a satisfactory treatment
approach for a patient.

Further driving this needs hierarchy are institutions like Memorial Sloan Kettering Cancer Center in New York. It was Memorial Sloan Kettering that asked IBM
if it could add a medical discovery need tier to Watson that would build upon the
computer’s ability to take in data, analyze patterns, and perform diagnostics
decisioning. This added discovery layer facilitates comprehensive searches
through hundreds of thousands of pages of medical information on a particular
condition, gleaned from subject matter experts and research papers, and
further sharpens diagnosis and treatment tools.

Going forward, IBM plans to expand on these healthcare analytics
capabilities by giving Watson the ability to “see” and to “empathize,” as well
as to analyze and to diagnose.

This brings me back to the Maslow needs hierarchy, which at
its highest tier of “self actualization” lists traits like problem solving, acceptance
of facts, lack of prejudice, spontaneity, and creativity. If Watson and other
solutions like it can incorporate these qualities in patient care and the
training of medical professionals, it seems we can hope for better outcomes in patient
treatments and cures.

Are you impressed or skeptical about this evolution in Watson’s analytics capabilities? Do you think this could lead to better treatments for patients and even cures? Share your thoughts in the discussion. 

Also read on TechRepublic