AI in pharma: Can it be used in DTC?

SUMMARY: AI in the pharma industry has the ability to transform certain capabilities. DTC marketers can tap into AI to help deliver relevant messages to certain audiences, but they shouldn’t let AI do all their thinking.

According to Forbes AI can be used within pharma for:

  • Digital Therapeutics / Personalized Treatment/Behavioral Modification – This can be effectively used to assist and identify individuals to provide early insight into the condition – such as gum conditionaccurately classify cutaneous skin disorders, suggest primary treatment options with Over-the-counter medication, and serve as an ancillary tool to enhance the diagnostic accuracy of clinicians, or improve educational and clinical decisions made by your child’s teacher, or your mental health professional or even your medical doctor.
  • Drug Discovery and Manufacturing: It helps in the initial screening of drug compounds to the predicted success rate based on biological factors. Measuring RNA, DNA quickly. Precision medicine or next-generation sequencing helps in the faster discovery of drugs and tailored medication for individual patients.
  • Predictive Forecasting – Predicting an epidemic is one of the key examples of this topic. ML and AI technologies are also being applied to monitoring and predicting epidemic outbreaks or seasonal illnesses worldwide. A predictive forecast helps plan our supply chain to get the inventory at the right time and the right quantity based on the predicted intensity.
  • Clinical Trials – Identifying the right candidate for the trial based on history and disease conditions, and additional attributes, overlaying with infection rates, demographics, and ethnicity to represent the most impacted.

However, in DTC marketing, we should be careful about using AI in an era where too many patients feel like our healthcare system is dehumanizing them.

People want to do business with other people—as AI picks up, companies will want to give careful thought to the roles and tasks that become automated.

In a recent survey, 26% of respondents reported feeling “great” about Artificial Intelligence. A majority of respondents (60%) had a lukewarm acceptance of AI, allowing for its ultimate future potential and noting that we need to be careful of how it’s employed. These statistics need context, though.

There is nothing more frustrating for patients than trying to speak to someone at a doctor’s office only to go through a layered phone tree. When you have a healthcare problem, you want answers as soon as possible and don’t want to wait for a callback.

So how can AI help DTC marketers? AI may predict the questions patients and physicians have about a drug or its side effects. Using AI to anticipate questions can help DTC marketers prepare content for patients. The question then becomes, should that content be distributed by AI?

I’m working with a client now to do a test of AI within their website. Basically, the AI integration monitors social media content around certain keywords and the call center and recommends content be added to the website. The team in charge of the branded website must concur that there is a gap in content, and the content is then developed.

In the call center AI constantly updates the data base in reflection to patient or HCP questions so that call center nurses have answers to the most likely questions.

For pharma salespeople, AI can quickly identify which physicians will be receptive to new drug information and which ones won’t. It’s not easy to integrate the data, but it needs to be done with the understanding that it requires constant optimization once it’s implemented.

We tried to use AI to identify which patients, within our target audience, would be most receptive to new drug information, but frankly, we found that there were too many subgroups within our market. It was better to use AI to classify these groups and recommend potential messages as applied to online ads.

There is no doubt that AI can reduce costs and help pharma develop new drugs quickly, but we should be careful. Organizations should ensure that there are processes in place to share learnings and ask “what if?”.