The Role of AI in Value-Based Healthcare: Part One

November 26, 2018

We address this issue in a series of three posts on our blog, this is part one. Parts two and three coming soon! 

How does the United States spend more on healthcare per person, and yet have the most preventable deaths when compared to similar countries? No one will argue that this is a fundamental problem with healthcare in this country. The U.S. is held up as a beacon of innovation in many industries and spaces, including technology, and yet little of this forward thinking has made significant impact on the healthcare sector. So is innovative technology even the answer?

First, we have to examine how we could possibly have prevented those deaths. The very definition of “preventable” implies those lives could have been saved if someone did something different somewhere along the way. Is the fault with human judgement? While medical error does happen with alarming frequency, we propose that the problem is not with the judgement capacity of medical personnel, who are almost always highly trained, motivated, and competent. Rather the problem is due in part to the information on which interventions are made. Relying on noisy, fragmented, and delayed data leads to errors and lower-quality decision making, no matter how smart you are.

This is where the promise of “big data” and predictive analytics comes into play – machines can do a fast and efficient job of pulling relevant data together, finding patterns, and showing us what’s coming. This gives us human beings a better opportunity to change outcomes. With the explosive growth in affordable computing power and the near-constant hype cycle in the press, it’s interesting to ask: why don’t we see this technology being adopted faster?

The economic problem

The unfortunate truth is that many dysfunctions in healthcare have nothing to do with technology. One core problem lies in misaligned economic incentives. A transactional approach to healthcare is a terrible idea when the patient doesn’t pay for services (the insurance does); the insurance doesn’t receive the benefit of the treatment (the patient does); and the doctors don’t know the costs of the diagnostics and treatments they’re ordering. To make things worse, these “fee-for-service” payment models reward doctors and facilities for volume, not outcomes. The incentives are broken, and attempts to address quality issues by tying reimbursement rates to quality metrics are just a bandaid.

The data problem

Another major problem is that analytics is only as good as the data on which it operates. In healthcare, data is siloed across a myriad of legacy systems that don’t talk to each other, and this creates real harm for patients. For example, repeat visits to the emergency room are surprisingly common, sometimes even to different ERs. Why is this happening? It’s possible for patients to get the follow up care they need in order to avoid trips to the emergency room, but the data is so fragmented across settings of care that no one doctor can see enough of the picture to connect the dots and identify the gap in care. The onus, therefore, falls on the patient to manage their own journey through this complex web. Even their own primary care physician often won’t know about the ER visits. Proactive care to prevent many of these incidents is absolutely possible with proper data sharing and follow up, but who is driving this transition? Some states are linking records so patients can be tracked from one facility to another, but it’s still relatively uncommon. Basically, individuals are often unable to get preventative care because siloed data doesn’t allow members of their care team to see beyond their own setting of care for a comprehensive view of the patient.

But all is not doom and gloom—there is genuine hope in the form of value-based payment models and data-driven workflows. After more than 60 years of efforts, we’re starting to see the market tip towards a more efficient, sustainable direction, and early results are promising. Challenges remain, but can be overcome as we’ll discuss next.

Next installment of this article series is coming soon and covers where we go next.

Adam C. Lichtl, PhD, CEO and Chief Data Scientist

Adam is passionate about dispelling the myths and hype surrounding artificial intelligence in the healthcare space.

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