It’s safe to say that healthcare has come a long way from its low-tech past. Until just a few years ago, paper was still the foundation for most healthcare processes. We spent tens of billions of dollars, much of it government money, to adopt electronic medical records, for better or, in all too many cases, for worse. Interpersonal communications went from face-to-face to email to instant messaging, seemingly in an instant. I can still remember the awe of holding my first Blackberry and the dread of that first pager.
Today, tech is ubiquitous, with applications in almost every nook and cranny of a healthcare organization. In most cases, there has been no strategy to it all, so we have tech that can’t communicate or is so poorly implemented that it isn’t solving the problems it was meant to solve. To help make that technology work for us, and to conquer expense and reimbursement challenges, we’re trying Lean, Six Sigma, CQI, or even TQM, and some of it is helping, though there are often too many new projects to implement all of them, no matter the rapid improvement approach being used.
Another problem is how to finally take advantage of the massive amount of data being generated by all of this tech. Now the flavor of the month is artificial intelligence. It seems like every vendor marketing to healthcare providers claims its technology uses it, and some health systems are buying off-the-shelf software and/or databases to try some of it on their own. One big problem is what “it” is. Is AI merely advanced data analytics, machine learning, predictive modeling, prescriptive solutions, or all of the above? What can we expect? Will it ease transitions to value-based care? What does it mean to use AI to manage revenue cycle in a digital age?
Salud recently celebrated its eighth anniversary. As a company that was born in a digital age, we recognize the need to collect, store, and analyze clinical and financial data that can help us predict outcomes and improve processes. We’ve embraced efficiency gained through robotic processes. And we comfortably present insights to clients using the knowledge we’ve gleaned from our analytics, from payers and from our staff.
And yet, I am still not comfortable with what I hear about artificial intelligence for revenue cycle. The pathway to meaningful AI and machine learning requires smart design, lots of data, and continuous focus. I’m not sure that our industry is doing this well yet; many technologies have failed to meet their promises in the past.
On the other hand, I am excited about Salud’s foundation, its current adoption of technology, and its vision for the future. Our company recognizes that revenue cycle impacts patient, jobs, and community health. Our technology utilizes data from client, payer, and internal sources to ensure that we properly serve patients, empower our staff with meaningful work, and help us share opportunities for our clients to improve their own processes. We aren’t just dangling our feet in AI; we’re wading out into the stream, but the real value will be when we can swim with it.