The AI Marketing Mistake You’re Making in Healthcare

Stanford Medicine predicts that healthcare data will produce 2,314 exabytes by 2020. For context, the volume of big data in healthcare will surpass finance, media, and manufacturing via compound annual growth of 36% through 2025.

Given this huge amount of data – and big goals to improve value-based care – the healthcare sector is bullish on AI. People are living longer and hold more consumer power than ever before. They’re also living with more chronic conditions that increase the total cost of care.

Meanwhile, advancements in tools like chip technologies coupled with huge amounts of data make AI a natural fit for the healthcare market. Google searches for “Artificial Intelligence in healthcare” quadrupled between 2015 and 2018. But two big questions remain: What exactly is AI, and who is the best audience for healthcare AI products?

Marketing hype is to blame for much confusion about what AI is and how it can solve specific problems. As just one example, many healthcare enterprises are trying to implement Robotic Process Automation (RPA) – which is commonly mistaken for AI. In research for Gartner clients, Analyst Anurag Gupta describes the difference this way:

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“RPA tools perform the “if, then, else”-like statements on structured data, usually using a combination of user interface and/or APIs to drive the underlying infrastructure (like servers), mainframes or HTML code. AI, on the other hand, is a technology or system that can emulate human performance. Note that unlike RPA, which uses mostly clear structured data, AI can extract information from unstructured data.”

Unclear articulation of AI’s value is the first part of a two-pronged problem. The second part is marketing AI to the wrong healthcare leaders.

Within healthcare providers, 12% of IT spending sits beyond the formal IT team. In fact, leaders in business and clinical units like oncology hold influential purchasing power.

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AI is most successful when working with humans to do specific tasks, such as using radiology data to find malignant growths. In this case, marketing such healthcare AI to a radiologist should be top-of-mind. They’re the ones most likely to see AI’s influence on their work and advocate for pilot program roll-outs.

You’re hard-pressed to find an industry more keen to use data than healthcare. Research conducted by GetApp found that respondents in the healthcare sector had the highest utilization rate of data: 1 in 4 told us that less than 10% of their data goes unused.

That said, respondents working in healthcare had the lowest levels of confidence in collecting relevant data. This inability to find the data they need prevents them from making big decisions based on data, which impedes AI adoption.

If you’re marketing a healthcare AI product, your first step is to target the right audience. Instead of cold calling the CIO, make sure your pitch outlines the value-based care problems that your product solves. Then, target the head of the department solving said problem. They are best positioned to see AI’s impact and advocate for mass adoption.

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