Machine learning powers many of the services we regularly use, from personalized recommendations on Netflix and Spotify to voice assistants such as Siri or Alexa. In recent years, machine learning has gained momentum in healthcare due to the vast amount of available clinical, financial, and operational data.
According to the MIT Technology Review, machine learning is a subfield of artificial intelligence (AI) that uses statistics to identify patterns in massive amounts of data. Due to its accuracy and ability to be refined and improved over time, machine learning can be used to predict medication non-adherence and improve the effectiveness of patient interventions and support programs. In addition, it can be used to improve other clinical behaviors, such as gaps in care and therapy initiation.
Let’s talk about how machine learning can help address medication non-adherence:
Identifying Patients with a Greater Risk of Non-Adherence
Machine learning can improve medication adherence by making robust predictions about each patient’s risk of non-adherence. It does this by correlating levels of medication adherence with thousands of data variables pulled from many different sources, including:
- Prescription data
- Medical claims data
- Historical program data
- Consumer behaviors
- Social determinants of health
These variable datasets provide the technology with multiple views of patient behavior and are proven to be predictive of medication adherence—or lack thereof.
Determining Which Patients Are Most Likely to Be Influenced
Once the AI identifies the patients with the highest risk of becoming non-adherent, it then targets the patients who are both at risk and whose behaviors can be influenced through proactive interventions. This allows healthcare organizations to effectively allocate resources by prioritizing patients who would benefit the most from an adherence intervention.
Predicting the Optimal Intervention Channel for Each Patient
Improving medication non-adherence isn’t just about targeting the right patients for each intervention. It’s also about knowing which channel is best suited to each individual. Research shows that omnichannel interventions are proven to influence patient behavior.
Rather than playing a guessing game or simply sticking to phone calls, AI and machine learning allow us to personalize interventions for every individual. There are numerous ways of intervening to improve adherence, and determining which method will be most effective is key to ensure the greatest impact.
Pinpointing Intervention Messaging, Timing, and Frequency
From there, the AI takes it even further by determining the right messaging, timing, and frequency—so not only how to intervene, but also when and how often. For example, some patients may respond better to a short and sweet text message reminder to take their medications in the afternoon, whereas others may be more strongly influenced by a phone call, in-app notification, or email.
Machine learning can tell us all of this and more, enabling pharmacies, payers, and pharmaceutical companies to customize their patient outreach by channel, content, and timing for the best possible results.
Addressing medication non-adherence is just one example of how advancements in AI, machine learning, and predictive analytics are revolutionizing the way we approach patient interventions—not to mention countless other applications in healthcare. Leveraging the power of prediction and data-driven technology, healthcare organizations can deliver more effective, personalized, and cost-effective interventions, all with the ultimate goal of improving patient adherence and health outcomes.
The AllazoHealth AI engine is designed to drive lasting behavioral changes and get smarter and more effective as it goes. Curious about how AI and machine learning can be used to make better decisions regarding patient interventions? Schedule a demo to learn more about our AI technology.
About the Author
Dev joined AllazoHealth to help drive healthcare innovation. As VP, Business Development, Dev leads commercial and strategic activities to drive the company’s growth. Before joining AllazoHealth as employee #2, Dev held strategic roles at scaling digital health companies including CipherHealth & CareDox. These operating leadership roles included spearheading business development activities as well as financing initiatives such as managing a Series B round and acquiring a tech enabled services business. Earlier in his career, Dev spent several years at Booz & Company and Cognizant within their transaction services and healthcare practices.