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February 20, 2024

AI for patient engagement

The practical applications landscape for patient engagement, including opportunities pharmaceutical teams can leverage right now.
AI for patient engagement

Artificial intelligence transforms our personal and professional lives, from navigation software to facial recognition, voice assistants, search engines, grammar and spell check tools, and even our personalized social media feeds. In the pharmaceutical industry, potentially high-impact AI applications offer tantalizing possibilities. However, given the relative infancy of generative AI for healthcare, innovators and regulators have much more work to ensure safe AI-based technologies for diagnosis, treatment, and other patient-facing uses. How can pharmaceutical teams use AI for patient engagement?

How does AI currently support patient-facing functions?

At the closest point of care for clinicians, AI currently supports the provision of care by:

  • Reducing administrative burden and improving patient engagement
  • Summarizing long documents and highlighting certain aspects of them, clinical trial reports, case studies, or care guidelines
  • Authoring a document with instructions from the physician or clinician, like a letter justifying a procedure to a payer
  • Provision of notes about patients’ conditions in their own language, in age-appropriate terms of understanding in some cases

“Saving four minutes on notes when minutes matter over the course of time and multiples of patients at a time when demands on clinicians related to numbers of patients seen is very high has a great deal of value.” – International Society for Pediatric Innovation

Pharmaceutical companies and their leaders know they will need AI to remain competitive in their respective races to identify promising new candidates, achieve regulatory approval, market adoption, patient access, and improve outcomes. Still, questions remain about how to safely onboard generative AI technology and how to use it ethically when communicating with and engaging patients.

How could AI improve pharma’s commitment to patient-centricity?

Designing and executing better clinical trials could have immense positive income on developing better drugs for patients worldwide. Data aggregation and analysis is one area in which AI has matured to a point of high utility and impact in clinical trials. AI is being used to replace placebo control groups in clinical trials, with ‘digital twins’ of subjects for randomized clinical trials, allowing clinical teams to reduce the size of their control groups and minimize patient disruption. AI applications are being used to clean, aggregate, store, and manage clinical big data – helping clinical teams inform better site selection, optimize study designs, and accelerate the informed consent process.

Predictive AI models can also calculate the results of a given treatment on a subject or group of subjects, potentially eliminating the need for animal testing and accelerating the preclinical phase.

AI for patient engagement: where medical affairs sees potential

One way AI could be used to affirm pharma companies’ commitment to patient-centricity is by making patient interaction more accessible.

In a MAPS workshop led by Within3 CEO Lance Hill and medical affairs leaders from Ipsen and Eisai, medical affairs leaders named access barriers like language differences and the dearth of information on rare diseases as factors that further compound the challenge of engaging patients across different channels. Generative AI could translate medical information into plain language or create detailed FAQs for patients and their caregivers to learn more about disease states and treatments. Other potential patient-facing uses might include:

  • Simulators, call center responses, and FAQ answers. Simulators or chatbots could be used to answer patient questions and concerns.
  • Data quality and compliance. Ensure data quality is injected into AI models and address compliance concerns related to patient interaction and engagement.
  • Social media engagement. Explore compliant ways to connect with patients through social media platforms.
  • Proactively address privacy concerns. Address concerns regarding data privacy and ensure patient data protection in AI-driven initiatives.
  • Creating plain-language materials or translating materials into multiple languages. Technology could significantly speed up the process of creating materials in patient-friendly language or multiple languages.
  • Customization and targeting. Tailor engagement strategies based on patient profiles, preferences, and disease states for more effective communication.
  • Automate MSL insights. Explore automation of MSL insights generation and set clear expectations about AI capabilities.

Ultimately, presenters and participants agreed that if medical affairs organizations want to evolve patient engagement strategies to leverage third-generation AI technology, they must achieve the three Cs:

  • Comprehensive. Changing an organizational approach to patient engagement is a broad undertaking that requires alignment of internal goals and objectives with resources and regulatory requirements.
  • Collaborative. Because one of the challenges of patient engagement is who owns that function within the organization, internal alignment is necessary to move forward with an effective strategy.
  • Compliant. Technology offers intriguing possibilities, but organizations must use it without incurring risks.

Within3’s assisted moderation for pharma

Within3’s Moderator Assistant is built on proven AI capabilities and is unique to the industry. It provides 3-7x the feedback of typical engagement settings like Zoom, Teams, or in-person meetings. This feature also eliminates about 90% of the work associated with moderating, analyzing, and reporting on insight-gathering activities like advisory boards, steering committees, and other common types of engagements, a particularly powerful prospect, especially for medical affairs, MSLs, and field medical teams.

The impact on medical affairs teams could mean more effective and efficient patient engagement, with pharma professionals freed up to focus on using higher-quality insights to create more patient education materials or identify gaps in communication so they can better support patients.

Conclusion

Opportunities for AI in pharma – from accelerating reporting and insights analysis to AI for patient engagement and data analysis – will continue to reshape the industry. Patients will benefit from effective drugs developed in shorter timelines, with more reliable and accurate data sets and better patient education to improve adherence and outcomes. Simply put – AI enables the life science industry to innovate faster and, in time, improve the provision of care.

Learn more about AI and medical affairs in our podcast: AI is an opportunity for medical affairs.

If you’re interested in learning more about our insights management platform, especially AI-supported insights reporting to make your pharma team and stakeholders more productive and accelerate your commercialization processes, book a demo.

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