Value-Based Care Success for Pharma: Requires Machine Learning for Patient Adherence

How pharma rethinks its marketing will be on par with how healthcare will embrace value-based care. Succeeding in value-based care may indeed be the impetus for pharma to embrace new “technologies” to help patients better adhere to medication regimens.  Speaker Kevin Troyanos, SVP Marketing Analytics from Saatchi & Saatchi Wellness, at the IIEX Health Conference gave an excellent overview of why machine learning and predictive analytics has become the technology of choice for healthcare marketers to tackle patient adherence.

Patient Adherence: Essential for successful outcomes
“Healthcare marketers are now addressing what it means to incorporate this value-based ‘thing’,” imparted Troyanos.  However, this “thing” is tied to a vested financial interest for both health providers and hospitals to effectively improve outcomes like reduced readmissions.  Even after discharge from the hospital or receiving a prescription post a physician visit, a patient may need better care coordination in order to follow a designated care plan. Healthcare stakeholders have full awareness of the hurdles to adapt and achieve the benefits of a bundled-payment program.  Capped health services during a clinical care episode and preventing penalties, has led to instituting new workflows and patient care plans. Patient adherence to follow-up appointments, educational resources, and medication adherence is very important to avoid readmissions. Pharma marketing companies are also motivated to help patients succeed in their care and wellness. And perhaps, the biggest change has been to relook at the patient data for answers.

Embracing the World of Analytics is Crucial
Predictive models are all around us and you may not even realize their influence in your day to day life.  “Maybe you have received a message that there is the possibility of a fraudulent claim on your credit card,” Troyanos shared as an example.  And in jest, he continued that there isn’t a little elf in your computer viewing you all your transactions, but an algorithm that has been trained on past fraudulent claims generating a probability estimation for potential future fraudulent behaviors. You can even “chase a face” on Facebook.  Image recognition models have become extremely popular.  How does one know that the probability vector of pixels is a particular image? The answer is machine learning, which is a type of artificial intelligence that allows computers to learn how to model data without being pre-programmed.  These algorithms have the ability to predict a correct image with a very high degree of accuracy.  Data science is becoming more and more common in the healthcare space.   Thanks to the increase and the accessibility of data from EHRs and HIEs, we can even predict hospital readmissions before they occur.

Machine Learning: A Democratized Technology
“We are really seeing an explosion in machine learning techniques in the industry,” pointed out Troyanos.  The democratizing of this technology and its tools is being powered by the open source environment, including the programming languages Python and R that are absolutely free.  The same phenomenon is going on with algorithms.  New machine learning algorithms are being developed in academia such as neural networks, extreme gradient boosting, tree algorithms and random forests. We are also seeing a high demand for Data Science studies being taught in academic environments as well as online courses.

What is interesting is the how the data can differentiate the type of machine learning algorithm to use.  There are two big categories: supervised learning and unsupervised learning.  We use supervised learning methods on data when we know what happened for at least a certain group of individuals. For example, if a dataset contains patients that “lapse” in their therapy, we can build a predictive model using supervised learning methods like regression and neural networks.  The other case is unsupervised learning when you don’t necessary know what you are looking for, but instead, you let the machine learning algorithms (such as hierarchical cluster, association maps, and self-organizing maps) find patterns inherent in the data itself.

Predicting Risk of Prescription Lapse
Starting at the time a patient gets the first script, he or she can either persist to the second script or lapse.  For the majority of brands, persistency curves show that half of the patients’ lapse by the 6th month.  Usually, the first to second transition has the largest drop-off.   How do you know which patients have the highest risk to lapse?  The answer is uncovered with machine learning.   An optimal modeling approach would be a Markov chain, where the probability of next event only depends on the previous event and the not sequence of prior events.  Training lots of algorithms require longitudinal data containing prior long-term behavior of patient adherence. Those patient characteristics or descriptors affecting the target variable are used to convert the patient type into a probability of lapsing.  The model of choice is the one that best predicts lapse. Then you can look at the “whole universe of probabilities” to find patients who are most likely to lapse. Essentially, you want to develop risk assessments of your patients and be able to cluster them into segments. Unequivocally, Troyanos advised that the highest risk patient segment is where to initiate an early spend and effort for behavioral change.

Learn more about the Insight and Innovation Exchange Health (IIex Health) Conference that was held April 4, 2017, in Philadelphia.


This article appeared in RCM Answers and has been reprinted with the permission of Answers Media Network LLC.

Healthcare’s Investment in Machine Learning and Artificial Intelligence

The “Rise of the Healthy Machines” sounded more like a screening of a sci-fi movie than a Medstartr Health 2.0 NYC event. But to this group of over 180 attendees, this title conjures images of “machines” that help us stay healthy, help doctors give better care and help researchers give us better answers.  Moderator Fard Johnmar, President of Enspektos  fielded questions from the audience to the expert panelists on the newest analytics and algorithms now embedded in health technology. Dialogues highlighted, on a personal level, the struggle healthcare is having with the hype of machine learning and artificial intelligence and the whether it is a “one-size fits all” solution.

A 30-second definition of machine learning and what may be its biggest benefit for healthcare was the first request of the panel.  Spencer Greenberg, Ph.D., CTO of UpLift volunteered and introduced machine learning as the study of how to make accurate predictions from data, at its simplest form.  He explained that there used to be a field peopled called AI, Artificial Intelligence, which included a lot of things.  Machine learning, being part of it, has done so well that usually when people say AI they mean Machine Learning.   Greenberg feels that where ever you can imagine being able to make a prediction in healthcare, perhaps about a patient outcome or on which doctor will prescribe which drug, machine learning can be incredibly valuable.

Skepticism about ML and AI Solutions: A Hype or Help?
A member of the audience posed her concern and shared her red flag moment when she asked a question to her “Alexa” and it gave a response completely out of context.   As a physician, the point she was making was that many of her patients aren’t predictable and don’t behave like an algorithm.  She feels that 95% of medicine probably fits normal curve but fears the outliers are probably going to do really badly.  Panelists commented on why the benefits of machine learning depend on the continued learning from more and more data, and how outliers should not be discounted but serve an important role in research.

“I use AI to find rare disease. Outliers. One in a million patient,” Fernando Schwartz, Ph.D., Chief Data Scientist at Prognos.AI  told the audience.  His company uses reams of lab data to uncover biomarkers that can predict disease onset at the earliest point in time.  And even though the biggest impact of AI may be on trying to scale up and model the masses, there is also huge value in identifying those outliers. He pointed out that pharma companies, working on new drugs for rare diseases, can be doing really well until they need to find the next new patients. “Alexa may be wrong, but if you have a rare disease it is very likely we will find you,” replied Schwartz.

“Machine Learning is definitely over hyped but it is an incredibly valuable set of technologies,” proclaimed Greenberg.  He has encountered many companies that may want to use machine learning, but not only don’t know what it is, or are applying it fruitlessly where it doesn’t apply.  He also explained that machine learning is a predictive science, not a causal science.  To predict whether a patient with a given set of conditions is more likely to get cancer, and with the right data, machine learning can do that.  A study of whether a patient will respond better to treatment X versus treatment Y is not in the realm of machine learning.   He noted that unfortunately, at times the application gets misapplied and it does get overhyped.   Greenberg clarified that a wonderful benefit of machine learning is the continuous “training” from data that occurs, and in a sense, Alexa gets smarter from making a misclassification error. “All the data that you are feeding Alexa, you are making ‘her’ smarter. Every year Amazon is working with Alexa, it gets better and better and better,” he imparted to the audience.

Plausible Solutions for Reducing Tech Interference
An observation from the audience put an interesting spin for a use case for machine learning.  She relayed that having attended market research studies on EHR/EMR usage, the negative feedback from physicians was extremely potent. A major sentiment was technology caused friction during the patient and physician visit.  Was there anything in the tech world that could address this problem? Several panelists shared their opinions on AI and tech solutions for reducing the burden clinicians have right now with the EMRs.

“AI for a purpose is a good way to put it.  What is AI for the EMR? What is AI going to do? ” questioned Schwartz.  He pointed out from a business perspective, you may have to start developing some ideas like Alexa where you can have a voice command.  From the investors’ point of view, he sees this type of AI lacking a strong business case.  Maybe AI will help us interact better with the system in the future, but he contends the problem may rest in demonstrating how to monetize the technology.

“It is not merely a technology problem, but doctors have to be willing to adopt technology to make EMRs more seamless,” interjected Gary Cheung, Sr. Software Architect from Cigna. From his perspective, the pain point is on transferring paper documents to EHRs and taking steps to move away from writing notes and records to an iPad when seeing patients.

Jeff Nadler, CTO of  Teledoc, had a pragmatic approach “There is friction in the patient-doctor relationship. I think we are going to see technology not eliminate that friction but make it less, like the voice recognition and natural language processing,” he stated unequivocally.  Nadler sees more of a complimentary adoption over time by both younger physicians, having grown up with smartphones and tablets, and the expansion of sophisticated technology and tools that will guide the physician through the visit.   Teledoc is leading with branching logic decision support permitting intuitive prefilled menus for speed and ease of exposition.

Companies like the new San Francisco-based Forward are taking on this communication challenge with its super hi-tech primary care system. “And one of the interesting things that they do,” Johnmar described, “is take natural language processing, basically taking speech, and making it understandable. Their patient and doctor consult technology essentially allows them to have a conversation without having to take notes into the EHR/EMR.”

Learn more about Medstartr Heath 2.0 NYC and please join us at our next event.

This article appeared in RCM Answers and has been reprinted with the permission of Answers Media Network LLC.