Why artificial intelligence will be crucial in value-based care

As healthcare organizations collect more data, AI can help winnow down the vast stores of information to help clinicians make faster, more accurate decisions.

Automation has been relieving the strain on human hands, backs and knees for generations. But until recently, those whose jobs required high-level cognitive skills have been able to rest assured that no machine or program could possibly replace their ability to make nuanced decisions based on the evaluation of complicated, sometimes conflicting, data.

That was before artificial intelligence (AI) rose to the fore. It appears possible—if not probable—that advanced algorithms will one day replace “mid-level” brainpower as well. It begs the question many have already asked: could robots someday replace highly deductive roles such as doctors and nurses?

It’s a sobering question, but not the most critical one facing our industry. The more thoughtful question is: will the healthcare ecosystem, vendors and solution providers included, be able to survive without AI? Don’t worry, no one’s suggesting a dystopian future in which AI has taken over and humans have become subservient. But we, as an industry, need to find resolutions to challenges that are difficult, if not presently impossible, to solve without the aid of the AI-driven solutions we’ve come to rely on.

These questions range from everyday issues of practice management to vital questions of patients’ health. For example:
  • How much will the treatment cost?
  • How much and how fast will I get paid?
  • Which treatment option is best for this patient: medication or surgery?
  • How likely are patients to follow my advice?
  • Where and/or when should I schedule this surgery?
  • How long until this patient will be able to return to his/her normal routine?

While some of these have always been questions for practitioners, the healthcare industry has a unique new dependence on AI to answer even the “simplest” ones. All kinds of discernable data are now readily available—from personal health information (PHI) to financials to protocols—and are always on standby to provide guidance. For the high-level problem solver, innovation has never been so close to the surface of their workflow.

Healthcare providers are beginning to embrace the shift from service to value-based care, and eager to see how outcomes can work for them, both clinically and financially.

Additionally, the demographics of healthcare practitioners are changing, too—computer- and tech-savvy clinicians, who received their medical education and training in the 1990s and 2000s, are now entering leadership positions from which they can influence change. These practitioners are the catalysts for ushering in even more automation.

In other words, a greater supply of data beckons an even greater demand for more data. However, this demand isn’t simply for massive data-dumps of ambiguous information. What healthcare providers and administrators demand are the critical data they need, and little more, in an easily consumable form.

This is where AI shines—with computing power and tactical decision-making to condense and filter indiscernible information into actionable evidence for decision-making.

Perhaps the greatest potential for AI (or “smart solutions,” as you’ll soon be accustomed to hearing) lies with the optimization of clinical protocols. By tapping in to the bottomless pool of evidence-based results, AI will be increasingly necessary to proactively and dynamically manage patient outcomes. This, in turn, will optimize the treatment experience, and the greater continuity of care will promote both healthier patients and healthier practices as the industry moves toward a value-based environment.

Ideally, AI will help spur patient engagement to new levels of compliance, enabling provider organizations to glean even more relevant information.

Lower costs, better margins and healthier, more engaged patients—can the industry really afford to not take advantage of smart solutions?

The healthcare industry is already at the early stages of leveraging interpretive intelligence into daily clinical workflows. Machine learning, along with AI, will only become a more integral part of the healthcare ecosystem. Vast swathes of critical data will become available when clinicians have the tools to abstract the data embedded in their routine workflows.

Ultimately, AI’s role in value-based care will result in optimized patient outcomes at a lower cost, unfathomable new protocol development, and, just maybe, a few unemployed science fiction writers.

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