ChatGPT, the new artificial intelligence tool, is getting the cold shoulder from educators and the coding community. But authors from McKinsey and Harvard have a more enthusiastic take on the potential of virtual assistants in healthcare. 

In a new report, they estimate that various types of AI, from machine learning (ML) to natural language processing (NLP), could save the healthcare system between $200 billion to $360 billion. It could also improve patient experience and clinician satisfaction while broadening access.

ChatGPT has quickly established a name in healthcare, having passed the U.S. medical licensing exam and notched authorship credits on multiple scientific papers. Universities, researchers and publishers may be debating the place of such AI-based tools. 

But healthcare experts aren’t grappling with the tech so much as salivating over how much it promises to boost productivity among payers, physicians and hospitals. One big chunk of savings could come from lowering administrative costs, estimated to account for a quarter of all health spend. 

AI is well-suited to tackle such tasks, due to their manual and repetitive nature. Private payers, for instance, could leverage AI to improve claims-management processes, like auto-adjudication or prior authorization.

Meanwhile, ML can predict avoidable readmissions, after which care managers can reach out and intervene ahead of time. Per the report, when such a model was used, 70% more of a plan’s members connected with care managers than before. Similarly, follow-up visits rose 40% and all-cause readmissions fell 55%. 

On the flip side of the payer equation, some doctors are enlisting AI to appeal insurance claims. Dr. Cliff Stermer, a Palm Beach, Florida-based rheumatologist, went semi-viral on TikTok in December for sharing a time-saving hack: Using ChatGPT to appeal an insurance company’s denial of coverage for a test he had ordered.

One of Stermer’s patients, who suffers from a rare autoimmune condition called systemic sclerosis, needed an echocardiogram to assess cardiac function. In seconds, the chatbot churned out a persuasive letter appealing the insurer’s refusal of coverage, complete with explanatory references. 

”Amazing stuff. Use this in your daily practice. It will save time and effort. We’re loving it here,” said Stermer in the video.

A second healthcare target for improving productivity revolves around clinical operations. Take the hospital operating room, a critical resource which isn’t always run optimally. Hours are wasted due to poor scheduling techniques, inaccurate estimates of surgical times and tedious processes for freeing up and reassigning unused blocks of time. 

One large regional hospital, per a case study cited in the report, had been losing surgical volumes to other hospitals. Using an AI algorithm, the hospital was able to optimize its OR block scheduler — the system for scheduling open time slots to surgeons — resulting in a 30% expansion in open OR time.

AI is also being adopted for value-based care (VBC) arrangements, where quality and safety outcomes can impact financial performance for physician groups. The report explains how one such group, which was in a VBC arrangement to reduce total cost of care for a chronic disease, aggregated data from multiple sources with the help of AI. 

That resulted in a more refined care model, which showed potential for cutting down on unplanned admissions. The pilot is  currently being rolled out more broadly.

While payers and physician groups are in the scaling-and-adapting phase of using AI within claims management and VBC, the authors point out that not all areas are quite as mature. Hospitals are still piloting the tech in clinical operations. 

Use of AI by physicians for knowledge-based tasks like supporting clinical decision-making and recommending treatment remains in the development stage. By way of example, does anyone remember IBM Watson’s stumbles in healthcare? Or the use of chatbots as virtual patient assistants.

Indeed, the tech will need to overcome major adoption challenges. Those include legacy technology, siloed data and nascent operating models. There are misaligned incentives, industry fragmentation and a need for more talent skilled in data science, per the report.

But based on the AI-driven use cases, the report notes, private payers could save roughly 7% to 9% of their total costs (amounting to $80 billion to $110 billion) within the next five years using the AI tech now available. Among physician groups, the report estimates savings at 3% to 8% of costs ($20 billion to $60 billion), whereas hospitals that tap into data science could net 4% to 11% of costs ($60 billion to $120 billion).

That translates into savings of $200 billion to $360 billion in healthcare, or 5% to 10% of 2019 spending, without sacrificing quality and access. And that’s without sacrificing quality or access. The authors of the report call for ongoing research over the next few years to validate the tech, including randomized controlled trials to prove its impact in clinical areas. 

Some fields may be scrambling to bar their use, but AI applications have already proven their worth in financial services and retail. For the U.S. healthcare system, notorious for being the most expensive in the world while delivering the poorest outcomes, “AI is likely to be part of the solution,” the authors conclude.