If you’ve investigated conversational Artificial Intelligence (AI) and the many ways it’s impacting commerce, you’re probably aware that a major chasm exists between chatbots and true cognitive AI. We’ve written at length about this subject in order to educate enterprise leaders as they move ahead with their investment strategies. In short: chatbots are highly scripted and rely on little-to-no AI, whereas cognitive AI thinks and reacts the way a human would, while adhering to business processes and industry regulations.

Nowhere is the distinction more important than in the healthcare industry. Patients, doctors, nurses and anyone else who might interact with a cognitive agent need to be sure that the technology is delivering reliable information and services. Inaccurate treatment schedules, outdated patient information, incorrect test results — all of these errors could have a major impact on patients’ lives.

Why Cognitive AI Works Best

When doctors, patients or nurses converse with a cognitive AI system, they’re having a conversation within the confines of a greater context. A patient’s medical history, information about a hospital or provider facility, insurance data, etc. — all of this can be pulled from a variety of systems and data sources. A doctor, for example, can ask the AI system for specific patient care plans through a cognitive agent that has backend integrations with current and historical patient information. Even better, the AI system can ensure that any new patient treatments, such as adding a new medication regime, are consistent with accepted practices and avoid any adverse patient effects like an allergic reaction from the combination of two treatments.

Consider a cognitive AI system’s potential impact on the pre- and post-surgery periods. Managing a patient checklist is a complex process that requires personalization and strict adherence to doctor and provider recommendations and standards. Medications, risk factors, whether to shower on the day of surgery, pre-op fasting and preparation — this is just some of the information the AI system has to manage.

Where Simple Chatbots Fail

Now contrast cognitive AI’s capability with those of a chatbot in those scenarios. A chatbot is incapable of storing and relaying historical information or highly personalized data. They’re capable of returning scripted answers scanned from FAQ pages or even a company database, but without context, an ability to switch contexts, or an ability to read intent and display empathy. A chatbot doesn’t have the capacity to offer specific or personalized recommendations that are relevant to a patient’s care plan in real-time.

A pre- and post-surgery conversation with a chatbot would follow specific steps outlined in a hospital’s manual or a provider’s website. What happens, though, if a doctor makes a small change to the protocol based on the patient’s specific condition? A chatbot, as only a pre-scripted service not integrated to real-time data, would not be able to provide this level of personalization without human intervention. Also, what happens if a patient has a specific question about a deviation they accidentally made to their pre-op plan? A chatbot is not equipped to answer a question out of its purview, whereas an AI system can make a recommendation based on its own historical and hospital-provided information.

Given the complexity of healthcare that requires the juggling of multiple provider, patient and workforce needs, a simple scripted chatbot is outmatched when compared to the impact a cognitive AI system can have across the industry.

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