When discussing the growing use of artificial intelligence, a hotly contested view is that AI will become a game-changer in healthcare – diagnosing and treating patients with serious diseases, like cancer or diabetes.
While “algorithm vs. doctor” and clinical moonshots dominate the headlines, AI is quietly solving another big problem that has long plagued healthcare: waste and inefficiency.
Unlike clinical issues, inefficiency is often overlooked because it’s complicated and “unsexy”. However, many now believe that solving operational issues is the biggest lever for fixing healthcare and the area where we can really move the needle on cost and patient experience. This is more important than ever, as hospitals are seeing more bankruptcies and face growing uncertainties around reimbursements and operating margins in the face of ACHA and other turbulent policy issues.
But tackling inefficiency is hard. That’s because hospitals are complex, unpredictable organizations. Data was expected to help. But given the massive amount of information involved, standard industry tools like dashboards and reports aren’t useful enough. In healthcare, the stakes are much higher and require more practical solutions. How can we expect busy nurses and doctors to make sense of dashboards in high-pressure moments and figure out what decision to make? It’s unreasonable and impossible.
We must also move away from “rear window” insights and into the proactive management of our problems. Let’s say that a nurse reviews a report indicating that, the day before, a toddler had her surgery canceled due to lengthy delays in the OR. This insight is meaningless because it’s too late to fix the situation.
Many correctly believe that success requires predictive analytics, but predictions are hard to interpret. They can’t help a busy nurse know exactly what she should do in that moment to prevent the chaos that may be heading her way. To make things work better for the frontline decision-makers, our tools have to be able to evaluate the possible interventions and suggest data-validated, real-time course corrections.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
For example, in the situation of the toddler, AI can predict potential scheduling conflicts in the operating room, or flag when a delay is likely. It instantly identifies the best option and then prompts the nurse to take the specific action needed to prevent the cancellation. This way, better decisions are made and issues can be dealt with before they even arise.
The results associated with the use of AI in healthcare operations are compelling. During Becker’s 8th Annual Symposium, a leading academic children’s hospital talked about a 25 percent reduction in same day surgery cancellations by using AI. Also, a prominent Midwest health system discussed steps for successfully transforming a low-performing emergency department– reducing patient wait time for a doctor by 20 percent.
Using AI, our industry can extrapolate this success to avoid many issues currently affecting healthcare costs and patient experience– such as surgery delays, overcrowding, patient falls and excessively lengthy hospital stays.
And, for our society, there are bigger goals that can be realized by focusing on these important and costly situations. Could we reduce painful facility closures facing our rural communities? Could we slow hospital spending that is now nearly $1 trillion dollars and represents a third of all healthcare costs in the U.S.? Could we reduce the 250,000 annual deaths from medical errors by making it easier for caregivers to do their job? On the most basic level, could we get patients in and out of the hospital faster and with less frustration?
To make an impact on these big picture outcomes, we have to change our mindset. Rather than seek out silver bullets, we must begin to value the small, day-to-day actions that, over time, can drive large-scale impacts.
It’s crucial that we equip all healthcare staff with the best tools to do so. That’s where AI can be a game-changer the game; if we can mold the information it delivers into the right, actionable decisions. In the next few years it will be increasingly clear that those who are able to do so will see the greatest success.
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