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AI Tool Helps Physician Group Manage Prescription Refills

Posted on April 25, 2018 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Most of the time, when we hear about AI projects people are talking about massive efforts spanning millions of records and many thousands of patients. A recent blog item, however, suggests that AI can be used to improve comparatively modest problems faced by physician groups as well.

The case profiled in the blog involves Western Massachusetts-based Valley Medical Group, which is using machine learning to manage medication refills. The group, which includes 115 providers across four centers, implemented a product known as Charlie, a cloud-based tool made by Healthfinch 18 months ago. (I should note, at this point, that the blog maintained is by athenaHealth, which probably has a partnership with Healthfinch. Moving on…)

Charlie is a cloud-based tool which automates the process of prescription refills by integrating with EHRs. Charlie processes refill requests much like a physician or pharmacist would, but more quickly and probably more thoroughly as well.

According to the blog item, Charlie pulls in refill requests from the practice’s EHR then adds relevant patient data to the requests. After doing so, Charlie then runs the requests through an evidence-based rules engine to detect whether the request is in protocol or out of protocol. It also detects duplicates. errors and other problems. Charlie can also absorb specific protocols which let it know what to look for in each refill request it processes.

After 18 months, Valley’s refill process is far more efficient. Of the 10,000 refill requests that Valley gets every month, 60% are handled by a clerical person and don’t involve a clinician. In addition, clerical staff workloads have been cut in half, according to the practice’s manager of healthcare informatics.

Another benefit Valley saw from rolling out Charlie with that they found out which certain problems lay. For example, practice leaders found out that 20% of monthly refill requests were duplicate requests. Prior to implementing the new tool, practice staff spent a lot of time investigating the requests or worse, filling them by accident.

This type of technology will probably do a lot for medium-sized to larger practices, but smaller ones probably can’t afford to invest in this kind of technology. I have no idea what Healthfinch charges for Charlie, but I doubt it’s cheap, and I’m guessing its competitors are charging a bundle for this stuff as well. What’s more, as I saw at #HIMSS18, vendors are still struggling to define the right AI posture and product roadmap, so even if you have a lot of cash buying AI is still a somewhat risky play.

Still, if you’re part of a small practice that’s rethinking its IT strategy, it’s good to know that technologies like Charlie exist. I have little doubt that over time — perhaps fairly soon — vendors will begin offering AI tools that your practice can afford. In the meantime, it wouldn’t hurt to identify processes which seem to be wasting a lot of time or failing to get good results. That way, when an affordable tool comes along to help you’ll be ready to go.

Number Of Healthcare AI Investments Climbing Rapidly

Posted on August 31, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

I’ve written frequently about the growing influence of artificial intelligence tools on healthcare delivery. These include not only support for advanced analytics and adaptive processes but also a growing number of clinically-oriented chatbots.

As far as I knew, these trends were early in their lifecycle, and ventures dipping their toes into healthcare AI were still just dots on a map. Apparently, I was way off on this one.

According to a recent article from CB Insights, healthcare has been, and continues to be, the top industry for AI investment deals. According to the company, there were 29 venture capital investments in healthcare AI last quarter, and from what analysts are saying, that number may rise substantially over the next few quarters. In fact, analysts noted that as of late August, it looked like this quarter’s level of healthcare AI deals would beat the previous quarter’s results.

Just to be clear, CB Insights’ definition of “healthcare AI” covers a lot of ground. The firm defines AI in healthcare as occurring when startups leverage machine learning algorithms to reduce drug discovery times, provide virtual assistance to patients or improve the accuracy of medical imaging and diagnostic procedures – plus some additional unspecified additional applications. (Its list does exclude hardware-focused robotics startups and health-related AR/VR ventures.)

Still, even if you peel away the drug discovery, research and diagnostics investments, there’s plenty of VC deals to track. For example, UK-based Babylon Health raised $60 million in funding the past quarter, the largest funding round tracked by CB Insights. Perhaps this is less surprising given that Babylon Health’s first VC deal included money from Alphabet’s DeepMind Technologies, a nice pedigree for any startup, but it’s still a huge deal. (As you’ll see if you click the link, DeepMind has plenty of healthcare IT development of its own going on.)

Other interesting funding deals included investments in mental health startup Spring Health and risk analytics company OM1, which snagged $15 million in Series A funding. Also, CB Insights found that while most deals involved US companies, four healthcare AI investments went to companies in India and three to companies in China.

Having absorbed this data, I’m eager to see whether my pet interest makes it onto CB Insights’ radar for Q3 of this year. You may already have a general idea about how AI is being deployed in predictive analytics for use in clinical care improvement, or to increase researchers’ ability to pinpoint genes for precision medicine projects, but you may not be aware that another hot application for AI use in healthcare is to provide counseling (and perhaps, in the future, psychiatric services) via chatbot.

I find these services particularly interesting because psychotherapy via AI has some characteristics which differentiate it from many other forms of AI-driven clinical options. One standout is that people may actually tell a chatbot more than they will a live person in some cases, which makes such bots helpful in supporting populations (such as soldiers with PTSD) which might be unlikely to open up otherwise. Let’s see if such applications attract big VC investors anytime soon.