If you’re a Medtech marketer, chances are you’ve heard of the difference artificial intelligence (AI) and real-world data (RWD) can make in Health Professional Outreach and Engagement Programs. But despite all the hype, many marketers have struggled to find effective and workable ways to turn these tools into executable programs.
The main challenge is the time lag inherent in extracting actionable insights from real-world data assets. Claims data – a common data asset used to create marketing strategy – is retrospective in nature. Even when the data is received in real time, by the time it is modeled to predict HCP activity, the patients it is based on have disappeared, limiting the model’s applicability to current use. Some of the challenges marketers face when using this data include:
- Actionable HCP messaging: Providing targeted messages based on past activity is a shot in the dark. There is no guarantee that a physician is still treating a potentially qualified patient today, which limits the relevance of the message.
- HCP targeting: Many RWD models use past prescribing behavior as an indicator of which healthcare professionals to target, but past activity will not always correlate with future patient flow.
- Limited sources / integration complexity: Claims data is a very popular and readily available source of data, but it only reflects a patient’s treatment history and omits other factors that influence future care needs, such as change in coverage insurance and formulary restrictions or current lab results. Yet adding additional sources can increase the complexity of the model beyond what a human analyst can turn into an executable strategy.
So what’s the secret to running an AI/RWD program efficiently?
To implement an effective AI/RWD strategy, you need to align your demand for data with the answers it is best able to provide. This means shifting your approach to focus on real-world data assets that tell us about a patient’s longitudinal journey through current stages of care, and then using an AI model to predict their potential future need for treatment. .
Instead of asking “how likely is this healthcare professional to encounter brand-eligible patients?”, ask “at what point in this patient’s journey are they likely to qualify for treatment and when/where are they likely to meet their supplier?” From there, you can focus on engaging their healthcare professional with the most relevant content for that patient – at that critical time in their journey.
By analyzing and using RWD in its proper historical context, you can avoid the challenges of data ingestion delays, while customizing your communications to reflect current patient and healthcare professional situations. At OptimizeRx, this is our approach to implementation precision commitment.
What is this new approach to precision engagement?
Our approach to precision engagement uses RWD to predict when a potentially treatment-qualified patient will meet their provider and engages that HCP with treatment information targeted to the verified needs of the patients they are currently seeing.
As a result, this framework enables life science companies to deliver contextual and actionable insights with immediate value to physician and patient. Here’s what it means for providers, patients and your brand:
- Healthcare professionals get help identifying and engaging brand-eligible, hard-to-find patients
- Healthcare professionals receive communications at a specific time and in an accessible format
- Messaging is personalized and tailored to the needs of the healthcare professional and the patient
- Healthcare professionals and life sciences have a better understanding of the patient’s situation
Ultimately, healthcare professionals and life science companies are looking to improve patient outcomesand precision engagement is a better way to use AI and RWD to achieve this goal.
Interested in learning more about the difference a precision engagement framework makes? Download our latest E-Book for additional information to improve your HCP engagement programs.