Implementing a Practical Approach to AI and Real World Data that Improves Care Journeys

Avatar for Mike Rousselle
VP Data Product OptimizeRx Corporation

Offering a Practical Approach to AI and Real World Data

Despite incredible advances in the availability of real-world data (RWD) and AI applications, many tech-savvy brand teams are still working to find and implement applicable use cases for this technology. 

During a recent workshop at Pharma USA, my colleagues and I worked directly with pharma marketers and digital innovation experts to help them approach AI and RWD in a practical, actionable way. Through a thought-mapping exercise, workshop attendees identified HCP audiences, messages and data triggers that aligned to specific phases of the patient care journey, creating a repeatable, actionable framework for using AI and RWD to support specific brand goals.

Finding Opportunities to Positively Impact the Patient Care Journey

Incorporating RWD and AI into your engagement strategy creates positive impacts that extend beyond the professional and into the personal realm. This is an idea that hit home for me when a close family member was diagnosed with a life-threatening illness. Despite the severity and late-stage diagnosis, I’m happy to report that my family member was able to receive treatment, and is now living a happy, healthy life. But in reflecting on the situation, I realized that with the right application of AI and RWD, my family member’s unique health indicators could have helped doctors identify and treat the underlying condition earlier—significantly increasing the therapy success rate and improving the quality of life for both my family member and all of the loved ones involved in the rehabilitation process.

During the workshop, we explored three specific AI / RWD use cases—all of which offer brand teams the opportunity to overcome barriers and positively impact the health of real people. I’ve broken down insightful takeaways from each of the breakout topics and below.

Identifying Mis- or Undiagnosed Patients

Oncology was the TA of focus for the group considering how to best identify mis- or undiagnosed patients, particularly given the increasing importance genetic testing plays in determining a course of therapy. Using RWD on patients’ prior Rx and family history, for example, AI could predict what genetics tests HCPs should consider, and alert HCPs to the tests’ relevance. Furthermore, the data from those tests could also be used to help HCPs correctly diagnose a specific tumor subtype that may benefit from more targeted therapies. 

Predicting Changing Lines of Therapy 

While the line of therapy changes occur in multiple disease areas, this group also chose to focus on oncology – perhaps one of the most important areas to identify the correct line of therapy as efficiently as possible. While several ideas were discussed about how RWD could identify a pattern in a patient’s longitudinal dataset, one of the most compelling was to use an AI model to earlier identify individuals who aren’t responding to therapy as quickly as similar patients, then flag these patients to their HCP for intervention, while also sharing information about suggested therapy changes.

Flagging Signs of Non-Adherence

During the group discussion on the challenge of non-adherence, behavioral conditions of the central nervous system, like addiction, schizophrenia, and multiple sclerosis were mentioned as being particularly difficult, due to both their long-term and the episodic presence of symptoms, which make it hard for people to stay on therapy. The group suggested using AI and RWD to pick up on early identifiers of relapses or medication gaps, and then use that information to drive early intervention with at-risk patients’ HCPs. And by using AI/RWD models at the point of care to look for signs of non-adherence anywhere in the patient journey, these models could prevent a pattern of non-adherence from emerging, keeping patients on their life-improving therapy for longer.

By utilizing advanced AI and RWD tactics in combination with HCP and patient communication, brands have the power to increase patient and provider awareness, boost therapy initiation, and overcome adherence challenges. But the true impact of this technology is so much more than that—it’s about real people. That’s why OptimizeRx’s approach is centered on the patient and designed to help brand teams support healthcare providers with relevant and timely content that improves patient outcomes.

If you’re interested in exploring how AI and RWD can positively impact the patient journey or would like to learn more about our approach to the use cases described above, we’d welcome the opportunity to discuss how you can deploy these tools for your brand.

Schedule a time to start a conversation today

VP Data Product
OptimizeRx Corporation