Driving better patient engagement outcomes with AI & machine learning

Now more than ever, patients are willing to incorporate emerging technologies in all aspects of their lives — including using AI and machine learning to assist them in managing health outcomes.

In sharp contrast to patients’ overall comfort level with AI being used in their care, recent data suggests only 35 percent of patients say they feel valued by their physician’s office.

Furthermore, only 50 percent of patients said their physicians are readily available, with one-fifth of patients either disagreeing or strongly disagreeing that their physicians are
easily accessible. We can do better to reach this key population, providing better access to care and quality engagement.

Enter AI and machine learning. These capabilities offer healthcare providers the opportunity to more deeply engage patients and ensure they feel heard and valued by their care team.

Additionally, success with value-based care models means providers need to drive wellness and behavior change. AI and machine learning can help fill gaps in care in an efficient way to improve patient outcomes and overall satisfaction without adding to a physician’s workload.

Reaching patients where they are — at the right time — will enhance overall health outcomes while helping boost satisfaction. Using AI and machine learning will greatly help achieve these goals.

You can see more data around patients and AI here.

AI and machine learning

Before we dive deeper into how these technological capabilities will help patients stay more meaningfully engaged in their health journeys, “Machine Learning Vs. Artificial Intelligence: How Are They Different?”Intelligence: How Are They Different?” breaks down what each of these capabilities can offer your organization’s patient engagement strategy. Here’s a quick look at each capability:

AI is the output of computer programs created to not only perform certain tasks, but to also adapt to different situations. These programs are algorithmic and “intelligent,” which enable them to react similarly to humans, but perhaps more acutely.

Machine learning is a part of AI but goes a bit further into the concept of “learning.” It is based on the idea that it is more beneficial to teach machines (programs) to learn and process data, rather than teach them to react to every possible situation that might arise.

Enabling patient engagement platforms to more effectively use AI/machine learning will strengthen patient engagement strategies by ensuring patients are contacted and engaged based on the behaviors already in motion. Patient behavior, after all, is a major factor in driving successful care plans. 

Adopting AI and machine learning for improved patient engagement strategies

Patients are more likely to stay engaged with their health and wellness if the mode of engagement is convenient. To date, major strides have been taken to help activate and motivate patients. For instance, patients can message with their providers and receive important care plan updates from their smartphone or other mobile device.

Incorporating AI or machine learning into your patient engagement strategy will be most useful when learning about each patient’s unique behavior and reacting to it in a way that reaches the patient at the best possible times, in the best possible manner. When the platform itself is learning about the patients’ motivations, it becomes much easier to promote self-healing.

Here’s a good example of how AI and machine learning can activate and motivate a patient to stay engaged with her care plan.

Sarah, a patient living with diabetes, is an early riser who likes to go for a run before having breakfast. After her run, she has a healthy meal and a cup of coffee. She tends to catch up on texts and social media before heading to work.

Her provider’s patient engagement platform — equipped with AI and machine learning — has learned that this is when she is most likely to respond to notifications, reminders and other care instructions related to her health. In fact, when she was engaged at 2 p.m., her engagement rate was just 30 percent. When the AI told the system to try engaging her at 6 a.m., her interaction and engagement soared to 90 percent.

The platform will curate individualized information for Sarah and push it to her when it knows Sarah is available and more willing to engage. This “learning” is critical as it promotes Sarah’s self-management of her health at her convenience. When we make care more easily accessible (and manageable), we make positive outcomes attainable.

But what about patients who aren’t as motivated to engage? How can AI and machine learning capabilities help keep them motivated?

One way to accomplish this is to enable the AI and machine learning to learn about these patients’ behaviors and send different types of reminders at different times of day to keep the appointments and other instructions top of mind. Instead of designating a staff member to deliver reminder after reminder, AI and machine learning will determine when and how to best engage the patient.

This not only keeps the patient active in his or her health, it helps organizations save administrative time (and money) that can be better spent on other tasks.

Population health with AI and machine learning

At Allscripts, we see these emerging technologies as positive and empowering.

From a population health perspective, AI and machine learning are now helping change the face of patient engagement.

On a daily basis, we see the results FollowMyHealth®, built on the Microsoft Azure cloud platform, clients are achieving with AI and machine learning and the results are energizing. Patients want to be engaged, and it is our job to enable this deep engagement. Across the healthcare industry, it is time to set our sights on driving smarter healthcare and changing what’s possible for every patient, every day.

To learn more, visit allscripts.com. 

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