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The CEAM Approach to Cognitive Machine Adoption


Every new technology comes with an adoption gap: that space between technological capability and human adaptation. This disconnect handicaps the inherent power of new tools and our ability to effectively and quickly realize value. The gap widens for emerging technologies like cognitive science. With cognitive machines, we have tremendous potential that, when applied, can positively impact the health and lives of millions; but, the newness and sophistication of the solutions can act as speed bumps to adoption.

We worked with our existing clients to identify the critical attributes that helped to drive adoption and value realization from the Cognitive Clinical Success Machine. Four themes emerged:

  • Make it Compelling
  • Make it Easy to consume
  • Make it Actionable
  • Make it Meaningful

Based on this feedback, we developed the CEAM approach. The idea is to build off the inherent ability of the machine to reduce a caregiver’s cognitive load and enable value even at the earliest stages of clinician engagement.

Make it Compelling: One of the best ways to combat skepticism is to deliver compelling proof that a machine or solution will work. For healthcare, we measure what “works” by the number of people that we can effectively help. During implementation, our Success Activation Team collaborates with project owners and sponsors to develop a comparison that succinctly communicates the capabilities of the Cognitive Clinical Success Machine and the potential impact that it can have on a patient population. These numbers provide the proof-points needed to drive momentum and adoption and establish a backdrop for the machine’s potential positive impact.

Make it Easy to Consume: Cognitive machines (at least the good ones) deliver outputs that are simple and elegant. Think about Google. When you search for something, what you get back is “just a list.” But the power within that list—the machine’s ability to know exactly what you want, on exactly what page, in nanoseconds—is what jettisoned Google to the dominant position within search. The Cognitive Clinical Success Machine works in much the same way. It delivers the smartest, most advanced list of at-risk patients and recommended actions. And these outputs can be inserted into just about any interface: a data visualization tool, patient management software, Electronic Health Record system. What the machine renders is easy to incorporate, easy to understand, and easy to communicate.

Make it Actionable: Knowing the who helps you target your resources; knowing the what helps to narrow your scope of interventions; knowing the why pin points effective actions that will best reduce risk and improve health. Edye Cleary, CQO at Health First, call this the “why behind.” By knowing who is at risk, what they are at risk of, and why this risk exists has helped her team save close to a million in direct costs and reduce readmissions across Health First’s patient population. It is knowing the “why behind” that has made the Cognitive Clinical Success Machine so effective across so many diverse patient populations.

Make it Meaningful: The Cognitive Clinical Success Machine goes beyond the patient and the current clinical encounter to account for the exogenous factors that contribute to 60% of a person’s risk. The engine that powers the machine delivers an ultra-high definition view into each individual patient. And this view enables meaningful, effective action to address a full spectrum of illnesses and adverse events. Clinicians can see points of risk, like pressure ulcers, up to broad, longitudinal risk like cardiac health. This ability to render an ultra-high definition patient delivers clearer, richer, and more detailed views into what is likely to happen to a patient and what can be done to lower risk. Moreover, the machine brings to light the patients who you never would have thought were at risk so that you can better impact and improve health across every segment of your population.