IBM's Watson supercomputer is using big data analytics to address healthcare's hierarchy of four needs.
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.
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