Leveraging interpretive intelligence in clinical workflows
Automation has been making human workers superfluous for centuries, but until recently, those whose jobs required high-level cognitive skills have been able to rest easy, confident that no one machine could possibly replace them when it came to making nuanced decisions based on the evaluation of complicated, at times conflicting data.
However, that was before Artificial Intelligence (AI) came along, jumping straight out of science fiction and into our daily lives. It now seems possible, even probable that machines will replace mid-level knowledge workers, and the question arises: could someday robots replace doctors and nurses?
It’s a vexing question, but not the most critical one facing our industry. A more compelling question is: Will the healthcare ecosystem—the vendors and the solution providers—be able to survive without AI? Doctors and healthcare administrators will increasingly demand answers to this question. As an industry, we must find innovative solutions to these challenges, that are difficult, if not impossible, to solve without the aid of AI-driven solutions.
These questions range from practical 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?
• Where and/or when should I schedule this surgery?
• How long until this patient will be able to return to his/her normal routine?
Some of these, of course, are the perennial questions that have always faced healthcare practitioners. Nevertheless, the recent changes in technology have made innovative solutions possible in a way never before imaginable. All kinds of data are now readily available in easily consumable forms—from personal health information (PHI) to financials to protocols—and storing and managing this data is getting cheaper every day.
Additionally, healthcare providers are beginning to embrace the shift from service, to value-based care, and seeing how it can work for them, clinically and financially. Finally, the healthcare practitioners are changing: computer- and technology-savvy clinicians, who received their medical education and training in the 1990s and 2000s, Generation X and Y, are now entering leadership positions where they can influence change.
In other words, with a greater supply of data, comes a greater demand for it. However, this demand isn’t simply for massive data-dumps of undefined information. What’s necessary is for healthcare providers and administrators to have the critical data they need, and only the data they need, when and where and in an easy to consume form. This is where AI can help make tactical decisions about amalgamating and filtering data.
There’s immense potential for AI (or “smart solutions”) to optimize clinical protocols by drawing on a huge pool of evidence-based results. As we move toward a value-based environment, AI will be increasingly necessary to proactively, and dynamically manage patient outcomes. This, in turn, will optimize the treatment experience, leading to greater patient engagement—and this greater continuity of care will promote both healthier patients and healthier practices. Clinicians will also gain insights into how to manage risks, which leads to lower costs and better margins.
Will robots replace healthcare providers? Unlikely. However, we can start to leverage interpretive intelligence in daily clinical workflow. Machine learning, along with AI, will become an integral part of the healthcare ecosystem because the vast resources of critical data will only be available when clinicians have tools to abstract the data embedded in their daily workflows that result in better patient care at a lower cost.