AI talk is everywhere. Actual usage not so much.
The buzz about how artificial intelligence (AI) is transforming the health industry is now everywhere. So much that it’s now hard to find a health conference, committee, article, report, or blog that doesn’t mention the term “artificial intelligence.” Not surprising, because the potential industry savings are enormous, over $150 billion, according to Frost & Sullivan. About half of those savings are clinical and the other half, financial and operational.
But as alluring as the real-world savings from AI are, the same report calls out a concerning disconnect between intent and usage of AI across every health enterprise – payers, providers, and life sciences. In a recent survey, 83% of health systems indicated they were “adopting” AI, yet only 15-20% of them could say that they were actually using AI to drive real change in their organization.
As our industry teams hold discussions everyday with health leaders across the country on the many ways that AI can help them run, grow and transform their business, we continue to hear the same message. There is a unanimous conviction that they need AI to put the data they have to work and to remain competitive, but most just don’t know where to start.
Clinical AI crowding out financial and operational AI
Here’s my take on why so many health systems are finding it so hard to move from AI talk to AI action.
Most of what we hear from experts and analysts about the potential benefits and savings of AI is almost exclusively focused on clinical applications of AI – like decision support, diagnostics, chronic condition management, medical imaging, drug discovery, and patient engagement. We rarely hear about the financial and operational benefits of AI. Why? Because clinical use cases draw more research dollars and more headlines on how AI is changing the practice of medicine as we know it, which draw more conference attendees and readers. Clinicians, who are often involved in the selection and prioritization of AI use cases, are naturally biased toward clinical use cases rather than financial or operational ones. And, compared to clinical AI use cases, financial and operational AI use cases seem, well, boring and maybe even a bit less noble.
But here’s the problem with this overemphasis on clinical AI. Compared to clinical AI use cases, financial and operational AI use cases are usually faster, simpler, and easier to implement and cost justify than clinical AI use cases. Financial and operational AI use cases also tend to be easier to implement because they usually don’t require redesigning existing clinician workflows. And financial and operational AI can also be noble when they reduce the cost of care–because that’s one of the best ways to make care more available.
To be clear, clinical AI is vitally important – but financial and operational AI are equally important, just understated.
Rebalance your AI portfolio
So what’s the best way to rebalance your AI portfolio with financial and operational AI? Start simple with virtual assistants. Why? Because virtual assistants can unburden your customers and your staff by freeing up time for them to focus on higher value activities or work at the top of their license.
A branded virtual assistant can unburden your customers to navigate your health system or website. A recent Accenture study showed that more than half (52%) of consumers are unable to navigate the health system on their own, which drives nearly $5 billion in avoidable customer service calls. Cincinnati Childrens unburdened the families they serve by quickly building a branded bot, “Caren” with Azure Bot Service and Cognitive Services to help families navigate the hospital, answers their questions, and help entertain their children. Premera Blue Cross unburdened their customers with a branded bot, Premera Scout built with Microsoft Healthcare Bot service to help customers quickly know where to get information on claims, benefits and other Premera services.
An internal virtual assistant can unburden your IT and clinical staff by freeing up more time to solve problems or care for patients by instantly delivering precise information, avoiding search cycles, or offloading clerical tasks. Nurses and physicians constantly tell me they need more time for patients care. For example, nurses today spend over half (51%) of their time in activities that have nothing to do with patient care, and physicians spend nearly a fifth (16%) of their time on non-patient care activities. Merck unburdened their scientists with a voice-enabled chatbot and Microsoft HoloLens to empower their scientists to look up procedures in a holographic operational manual without leaving their workbench or using their hands. Weill Cornell Medicine is unburdening their clinicians by empowering them to ask a bot to find genomic cancer variants and interpretations in their Precision Medicine Knowledgebase on demand.
If you’re stuck in the AI starting gates or your AI application portfolio is in need of rebalancing, here’s a good place to start: Breaking Down AI: 10 Real Applications in Healthcare.