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Susan Abedi is EVP, Strategy & Insights with 81qd, an advanced health care data analytics company. She can be reached at firstname.lastname@example.org.
Three ways artificial intelligence can drive the necessary engagement between patients and HCPs.
I once thought I had multiple sclerosis.
A few months ago, a series of MS disease-awareness pop-ups began showing up on my web browser. They captured my attention because I’d been experiencing a mix of health ailments, but I kept putting off a doctor’s visit. Were the pop-ups indicative of an underlying disease?
Today, AI-driven algorithms can help identify undiagnosed patients with rare and difficult-to-diagnose diseases and the healthcare providers (HCPs) who are managing them.
When pharma company executives hear about the promise of AI for diagnosis, they hope to walk into an HCP office and jump right into a conversation about undiagnosed patients or to engage patients online, leveraging look-alike models. These approaches have had mixed success. Since patients are de-identified, the discussion is necessarily general. Patients may not respond to online messaging, depending on how well the call to action motivates them.
While critical, identification is only the first step. Effective engagement is needed to drive diagnosis. Coupled with identification, AI can deliver a disruptive impact in three areas to drive diagnosis: Awareness, Activation, and Adoption.
AI can raise awareness of the clinical journey that undiagnosed patients undergo. Machine-learning algorithms can be used to mine massive amounts of data to understand the nuances of undiagnosed patients. Historically, we have assumed that undiagnosed patients “look the same” as diagnosed patients. In difficult-to-diagnose yet highly prevalent diseases, this is often not the case.
AI can elucidate the treatment journey for undiagnosed patients. Primary care physicians (PCPs) often treat these patients, but engaging a broad range of PCPs across the country is rarely, if ever, feasible.
AI platforms can map connections among specialties to assess which HCP networks to activate, which, in turn, facilitate appropriate diagnoses.
AI can drive the adoption of clinically appropriate diagnostic approaches.
Educating clinicians is imperative to help shape treatment approaches. Key to success is identifying highest-impact HCPs whose networks include HCPs with significant numbers of undiagnosed patients. These clinical influencers have been shown to be highly effective in driving educational initiatives for diagnosis.
While AI did a good job by targeting me with MS disease-awareness messages, I really wasn’t activated to do something about it—not just yet, anyway. Maybe when I see my PCP, AI will have already alerted her to the diagnostic approaches that can help determine whether or not I have MS.
This might finally be the right time to find out for sure.