Medicine revolves around quality of and access to care. For the pharmaceutical industry, that means delivering the right information and pharmaceutical response to those who need it. Often, that response has been via a doctor’s prescription pad driven from broadly targeted ads on TV or in magazines.
With the help of AI, the pharmaceutical industry will soon be able to proactively and promptly reach out to sufferers to provide a streamlined, personalized treatment experience. That’s just the beginning. AI will also shape R&D, front-line responses, and marketing, just to name a few.
Supported by new insights from AI, the pharma industry of the near future will increasingly take an outcomes-based approach, centered around the “true customer experience”—drawing on the successes of other industries with highly advanced customer experience management. Plus, with new AI research targets that include genomics, proteomics, and DNA sequences, the new pharma landscape will be a patient-centered world where medicine comes to the individual rather than the other way around. “The Patient Will See You Now” will become the new normal.
Let’s take a look at some examples of how AI will help make this vision a reality.
Real-time Monitoring and Outreach
Pharma is well-known for its substantial marketing spend. And yet, as an industry it’s relatively taciturn on social media and with other online discussions. Listening is a different matter. A huge amount of personal medical information is voluntarily shared online through social media networks, ailment-specific forums, and more.
What used to happen on open communities—“public” spaces owned and overseen by companies or organizations—is now taking place on virtual communities. These are adaptive, community-run spaces that are self-owned and self-governed, and which may span multiple networks or domains.
Being able to reach into these spaces, whether public or private, represents a huge opportunity for the pharma of tomorrow. Using AI for real-time social listening, the pharma industry will be able to monitor, identify, and add value to conversations around specific ailments, treatments, and drugs. Say, for example, a patient is suffering from IBS but is unhappy with the current formulation of their medication—specifically how it interacts with medication being taken for a thyroid condition.
An AI can recognize that this patient suffers from two separate conditions and is unhappy with their current formulation. It can then be programmed to reach out to the patient with advice about contacting their doctor about a different formulation or treatment, pointing to a centralized resource.
This same approach can be used to gather information more generally in order to gauge patient satisfaction, issues around a condition, and the market. This can in turn help inform marketing and R&D.
Countering Knowledge Gaps and Bias
Medicine draws upon a knowledge base that we know to be imperfect. Note the known issues in diagnosing heart attacks in women vs. men. Similarly, autoimmune conditions, more common in women than men, have been underdiagnosed or have historically taken a long time to pinpoint and treat. Much of this is due to gaps in clinical data, or biases that have focused on particular groups for particular types of illnesses. Proper, uncolored listening can help shift away from these biases by taking a larger and more comprehensive snapshot of patient data. It can also be used to monitor, identify, and track community sentiment around particular drugs, clinicians, and outcomes.
Using AI to monitor, parse, and understand voluntarily shared information on message boards, health apps, or social media will identify gaps and biases in clinical data or understanding. This knowledge can then be used to direct subsequent studies, marketing outreach, or novel formulations. AI can also be used to create cohorts of patients for enrollment in trials, reducing enrollment time and associated costs, and speeding up the process of getting essential treatments into the hands of sick patients.
This represents a significant benefit not just for those groups who have been traditionally underserved by medicine, but also for pharma companies for whom new R&D and marketing opportunities will open up.
Beware! One very, very common misconception is that an AI is like Spock from Star Trek—a being of perfect logic, uninterested in such petty things as prejudice or systemic social bias. Sadly, AIs are taught based on real-world data, and we’ve seen again and again that this data is biased because society is biased. However, there are some interesting efforts to produce more transparent machine learning processes (if you’re interested, please check out the Fairness, Accountability, and Transparency in Machine Learning group).
The New Frontline of Medicine
The way patients engage with healthcare is changing. Dispersed populations, changes in healthcare provision, and a shift towards digital interfaces mean that an RN or family doctor are unlikely to be at the front lines of healthcare in years to come. Convenient and accessible, computers will become the fundamental tool for triage and basic diagnosis.
Under the umbrella of “telemedicine” or “telehealth,” this approach involves the remote provision of real-time medical or well-being services respectively. Using technology and AI in tandem, it seeks to improve the patient experience by delivering on-demand care online and in as seamless a manner as possible.
Imagine chatbots that are able to function as that front line of care. Whether installed on condition-specific sites or forums, or written into the websites of clinics or insurance providers, they’ll be programmed to conduct a basic back-and-forth exchange exploring an individual’s condition and alternatives to treatment. These can complement or even in some instances replace the need to see clinical staff—although to what extent remains to be seen.
As such AI will help take the pressure off front-line staff, while also delivering valuable information about treatment. There is, of course, the need to not be seriously irritating—nobody loves a phone menu where they are stuck in “press 9 to repeat these options,” and chatbots can suffer from the same flaws.
Improved Healthcare Experiences
In healthcare, empathy is key. As AI grows more sophisticated and nuanced, it will give pharmaceutical companies insight into how patients talk about their conditions, treatments, and formulations. This awareness will allow pharma companies to tailor their tonality, topics, and timing to resonate with the needs and expectations of patients.
Moreover, AI will be used to collect and analyze written data in both public and private forums to gauge patient—and practitioner—experience with the healthcare system more generally. These genuine, candid responses will help shape the future delivery of healthcare and ensure better outcomes for all.
The healthcare industry is already monitoring and analyzing patient and practitioner perspectives through surveys and influencer feedback. But with AI’s support, these efforts can scale up, and shift from the general to the granular. In a few years’ time, AI will make pharma marketers’ jobs easier and more streamlined—and patients will benefit too.
