The Impact Of Algorithmic-Enhanced Care

March 21, 2019 | Algorithms are changing clinical care, says Sandy Aronson, Executive Director of IT, Partners HealthCare Personalized Medicine. True AI—neural networks—will play a role, but less sophisticated algorithms are already powering dramatic improvements.

“We are seeing the benefits of introducing algorithms into the care delivery process to enable us to really harness that data and help the way we make decisions,” Aronson told Clinical Research News. “It's logic, often complex Boolean logic, that enables us to get started, improve the care process, increase the amount of data that flows through the care process,” he says. “That should, in turn, set us up for more and more use of AI.”

On behalf of Clinical Research News, Gemma Smith spoke with Aronson about the impact of algorithmic-enhanced care—real world examples he’s seen at Partners and the progress he expects to come. These aren’t just “last mile” technologies, he believes. Algorithm-enhanced care will have broad benefits to healthcare.

Editor’s note: Gemma Smith, a conference producer at Cambridge Healthtech Institute, is helping plan a track dedicated to AI in Healthcare at the Bio-IT World Conference & Expo in Boston, April 16-18. Aronson is speaking on the program. Their conversation has been edited for length and clarity.

Clinical Research News: Can you give me examples of where algorithmic-enhanced care has had a significant impact, and the benefits you've seen from that?

Sandy Aronson: Sure. One example would be the way that we allocate platelets within the hospital system. At Birmingham Women's Hospital, the way that we obtain platelets is you have these donors who come in and, again and again, sit for up to two hours while their blood is cycled outside of their body to obtain a bag of platelets. That altruistic action is critical to giving us the ability to perform bone marrow transplants.

Once a patient gets a bone marrow transplant, we take their platelet count to zero. They reach the hospital floor after they've received their transplant, and we start transfusing them with platelets, and about 15% of the time, the patient immediately rejects the platelets that we gave to them because of a lack of a match between the patient's HLA type (Human leukocyte antigen), and the donor's HLA type.

In that scenario, you not only have not gotten any value out of this altruistic act from the donor, but it's also an expensive process. You've incurred costs, you haven't given the patient the platelet bump that we were looking for, so they remain at risk for bleeding, and you potentially introduce new antibodies into the patient that could make them harder to match in the future.

The reality is that there's a relatively small number of altruists who consistently come in and donate most of these platelets, so they can be HLA typed.  We have to HLA type the recipient in order to match the bone marrow donor. So the information is available do a much better job at matching platelets to patients. Instead of the oldest bag of platelets being taken automatically and given to the patients when platelets are ordered, what we do now is we provide an application that uses an algorithm to sort the bags of platelets in the inventory so that the blood bank technician can see which bags of platelets are most likely to be accepted by the patient so we can prioritize those.

We're still gathering data on the impact of this, but the initial data we've gathered is promising relative to using platelets much more efficiently and getting much better platelet count bumps when we transfuse platelets into patients. So, we're looking forward to collecting more data and then processing that data to assess the true impact of this program. That's one example.

Another example is the way that we care for patients with heart failure, hypertension, or high lipid values. There are guidelines for treating these patients, but in typical clinical care it is extremely difficult to bring patients into compliance with those guidelines at scale. These are conditions that effect a lot of patients. The process of implementing the guidelines requires a level of iteration that it difficult to implementing within the traditional clinical workflow. The patient needs to see a clinician, clinician needs to prescribe medication, we need to see what the effect of that medication is, and then you need to make adjustments either to the medications being given or to the doses of the medications being given to optimize the patient’s care.

The problem is, we all know that doctors are incredibly busy, patients lead busy lives as well, and scheduling these visits where this optimization can happen often takes a lot of time, and therefore the time to get the patient to the optimal treatment is longer than we'd like.

We've instituted a program where we built the guidelines into an algorithm that's contained within an App. Navigators are assigned to work with patients and they contact the patients at the time that is optimal for the patient and their care, and they don't have to consider the scheduling of the busy clinician. They can contact the patient, work with that patient to gather information that assists in determining what tests ideally should be ordered when, track their progress, and then they work with a pharmacist and overseeing clinicians to adjust medications. We find through this process that we're really able to bring down lipid and blood pressure values in a way that's really pretty gratifying to see. So those are two examples of where algorithms are entering care and making a difference.

What is the single biggest challenge that you faced when implementing algorithmically-enhanced care like you describe here?

We initially thought we could just surgically interject these algorithms into the existing care delivery process so that they could help someone make a better decision. What we really found though, and both of these are examples of this, is that as you're introducing algorithms into care, it gives you the ability to rethink the care delivery process as a whole. And that's hard; it takes a great deal of effort from both clinicians and IT folks—and sometimes business folks and others—to really figure out how to reformulate the care delivery process. But, that's also where the power is. That's where you can really think deeply about the optimal experience from a patient care perspective and how to deliver that. That often involves bigger changes than were first anticipated.

What makes you most excited about the use of AI and algorithms in the healthcare industry?

I truly believe that we are on the cusp of very, very significant changes and improvements to the healthcare system. Traditional care delivery pathways have evolved over a long time, and been incrementally improved over a long time, but what we have now is the ability to really look at how we fundamentally change these processes to make them better. The could be in the context of new technologies, new forms of data coming online, new ways we can interact with patients becoming available, and new algorithmic capabilities. And it's not just that. When you move to algorithmically-based care, it forces you to collect clean data to drive the algorithm, and that's something that the healthcare system hasn't traditionally been very good at.

By introducing a process that collects that type of clean data, that data then has the potential to become the fuel for machine learning to improve the process. When you implement algorithmically-based care, what you've really done is systematized part of the care delivery process. As a result of doing that, you can feed back improvements into that process far faster than you could in a traditional care delivery setting where decision making is so distributed.

This starts to set us up for continuous learning processes that have the potential to make clinicians far, far more powerful in terms of being able to diagnose, monitor, and treat patients in ways that constantly improve. Folks have talked about the continuous learning healthcare system for a long time, but I do think we are seeing the beginning of the process that can truly make that real. I really think in the best case scenario—and we've all got to try to deliver the best case scenario—it could deliver improvements in human health on a scale that we've never seen before.


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