By: Navdeep Alam, Chief Technology Officer, Abacus Insights

For a decade or more, healthcare leaders across the industry have been teased with grand promises of artificial intelligence (AI) and machine learning (ML) and the role these technologies will play in the future of medicine. From automating the creation of digital clinical documentation and advanced clinical analytics to delivering real-time clinical guidance and amplifying patient engagement, AI/ML have been discussed for years as the catalysts we need to realize a more intelligent healthcare future.

Much like children over the course of a long road trip, every now and then healthcare leaders beg the question: Are we there yet?

On the surface, this question makes sense. In social media, for example, AI is used to dictate content, to steer users in their thinking, to guide advertising — because social media companies know what their users read, what they vote for, what they like and consume.

But the road to a similarly intelligent healthcare industry is long, and we have just recently departed. Some tech firms have been able to implement AI/ML tools successfully on a limited scale. Amazon’s Comprehend Medical, for example, uses machine learning and NLP to extract health data from doctors’ notes, clinical trial reports, and patient health records. However, unlike other industries, we have yet to see any widely impactful application of AI/ML in healthcare.

That’s because healthcare data is much, much more fragmented. Our interactions with the healthcare system — our provider visits, pharmacy interactions, lab work — are logged as individual, time-separated events. That information is sensitive, unstructured, and often non-interoperable, making it extraordinarily difficult to visualize the healthcare journey for any given patient.

The lethargic progress of AI/ML in healthcare is due to this: healthcare has a data problem. Without a truly complete and correct story of the patient’s journey, told with longitudinal data reaching across all facets of the healthcare experience, AI/ML cannot reach its full potential.

Quality data is the foundation

Any and every accurate and effective AI/ML model is trained on quality data — and a lot of it. The key word here is “quality” — AI/ML not only depends on vast amounts of data but complete and correct data. While healthcare, which is brimming with data, may seem ripe for AI/ML innovation, ambitious and forward-looking AI projects from tech giants like IBM and Google quickly learn that healthcare has a serious data problem.

Most healthcare data is unstructured, coming from different sources and in different formats, and is often incomplete and/or duplicative. It is also extremely sensitive and highly regulated. This is why, for example, so few AI solutions in medicine have been given the greenlight by medical device regulators: at the end of the day, the efficacy of these tools is hampered by the quality of the data they’re built upon and the depth of consent administrators are able to garner.

Ideally, for health plans, AI can one day be used not only to inform patients that they are sick today but predict their healthcare journey down the road by detecting the patterns and preconditions leading patients toward chronic conditions like diabetes or diseases like Alzheimer’s. When can AI/ML truly be realized to let us know what will happen tomorrow so we can act today? There’s a reason why we haven’t seen this level of innovation. It all comes down to data. Ultimately, you cannot train AI to be intelligent about the healthcare outcomes of a patient if the model does not have complete and correct data on that patient and millions more

With healthcare data in its current state as it exists within most health plans — rarely complete and often incorrect — AI models cannot be used in any medically meaningful manner.

This is why so many overly ambitious AI initiatives in healthcare ultimately fail: Could AI solutions eventually be applied to tell patients how to improve their health today, so they don’t get sick tomorrow? Maybe, but certainly not right now. In order to realize the promise of AI in healthcare, we must start by rethinking data management and data engineering.

Realizing the promise of AI in healthcare

Currently, AI’s role in healthcare is much more nascent than many may realize, but its current application — mostly for automating mundane tasks — is an important step toward realizing the big AI dreams many of us in the healthcare industry hold.

We have seen AI/ML work toward improving data quality, which is a necessary step forward. Instead of humans trying to figure out if two data sources are the same, AI/ML can be applied to automate that task to produce a single source of truth, to structure unstructured data, and ingest and enrich doctors’ notes. These are the seemingly small steps we need to take to materialize downstream, ambitious systems.

The future of healthcare begins with quality data. This is a prerequisite for AI/ML, but also for many of the other grand visions we have for the future of healthcare.

Without quality data, success in value-based care models will continue to be difficult to achieve. For the U.S. healthcare system to incentivize higher-quality, cost-effective care, payers and providers need high-quality data that can help them find the signal in the noise — information that can be used to properly risk-adjust, guide clinical decision-making, reduce unnecessary care while promoting proactive preventive care, and ultimately be leveraged to improve patient outcomes while reducing costs.

And, as healthcare companies make inroads in data quality, making data much more complete, correct, and longitudinally rich, precision medicine can be used to activate from the “wisdom of the crowd,” drawing from the insights of many to guide the care of any given patient.

The promise of AI in healthcare is very real, but given the current, fragmented state of data management across the industry, we are still miles away from applying it in ways that create transformational change. As it stands, the true promise of painting a holistic data picture of a given patient or member is yet to be realized.

As an industry, we understand that the promise of AI/ML relies heavily on long-term data management strategy, and we’re in the early stages of implementation. To move forward requires the continual application of advanced data engineering concepts and an undying willingness to work with rich and complex data. Once we can paint a longitudinal picture of the patient journey that pulls information from every corner of the healthcare system, then we can begin using AI/ML in ways that make decisively positive impacts on the healthcare experience.

So, “are we there yet?” In reality, we’ve just left.