Harnessing Generative AI for Standardised Responses to Medical Enquiries in the Pharmaceutical Industry Getting Started with Patient Engagement: Key Principles and Top Tips From Challenge to Opportunity: Enhancing Literature Surveillance in Medical Affairs
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Harnessing Generative AI for Standardised Responses to Medical Enquiries in the Pharmaceutical Industry
Paul Dames
Getting Started with Patient Engagement: Key Principles and Top Tips
Trishna Bharadia and Liz Clark
From Challenge to Opportunity: Enhancing Literature Surveillance in Medical Affairs Dr. Sarah Guadagno
HARNESSING GENERATIVE AI FOR STANDARDISED RESPONSES
TO MEDICAL ENQUIRIES IN THE PHARMACEUTICAL INDUSTRY
Responding to medical enquiries is vital for pharmaceutical companies, ensuring excellent customer service and regulatory compliance. Traditional methods can be resource-intensive and prone to inconsistencies. This article explores the potential of generative artificial intelligence (AI), specifically large language models (LLMs), to streamline this process, addressing volume, regulatory compliance, timeliness, and accuracy challenges. We discuss AI’s capabilities, implementation considerations, potential pitfalls, and best practice guidelines for its use in the pharmaceutical industry.
INTRODUCTION
The pharmaceutical industry faces a significant challenge in generating standardised, compliant, and timely responses to the high volume and variety of medical enquiries they receive. The need for meticulous adherence to regulatory standards, such as the Association of the British Pharmaceutical Industry (ABPI) Code, adds further complexity. Generative AI, a branch of AI capable of creating text, images, or other media in response to prompts, offers a promising solution to optimise this process. Large language models (LLMs), a generative AI trained on massive text datasets, are particularly well-suited for this application due to their ability to understand and generate human-like text.
GENERATIVE AI AND LARGE LANGUAGE MODELS
Generative AI refers to algorithms that create new content, such as text, images, or music, similar in style and structure to existing data. LLMs, a subset of generative AI, are trained on massive amounts of text data from the internet, books, and other sources. This training enables them to understand the nuances of human language and generate contextually relevant and coherent responses to a wide range of prompts. LLMs have demonstrated
remarkable proficiency in tasks like translation, summarisation, and creative writing, making them a powerful tool for generating standardised responses in the pharmaceutical industry.
PROBLEM STATEMENT
Several issues hinder the effective generation of standardised responses in the pharmaceutical industry:
• Volume and Variety: The sheer volume and diverse nature of enquiries and complaints strain resources and can lead to inconsistencies in response quality.
• Regulatory Compliance: Ensuring each response aligns with stringent regulations like the ABPI Code is labourintensive and requires specialised expertise.
• Timeliness: Timely responses are essential for patient care, customer satisfaction, and regulatory compliance. Delays can have detrimental consequences.
• Accuracy and Consistency: Human error and subjective interpretation can introduce response variability, impacting accuracy and reliability.
PROPOSED SOLUTION: GENERATIVE AI
Generative AI, particularly large language models (LLMs), presents a promising avenue to revolutionise the handling of medical enquiries within the pharmaceutical industry. By leveraging their ability to process vast amounts of information and generate human-like text, LLMs offer a multi-faceted solution to the challenges that have long plagued this critical aspect of pharmaceutical operations.
The implementation of generative AI can significantly enhance efficiency by rapidly processing enquiries at a scale unattainable by human teams. Moreover, AI algorithms trained on established templates and regulatory guidelines ensure consistent, standardised responses across all communications, minimising the risk of noncompliance and improving the quality and reliability of information provided to stakeholders. Furthermore, by automating the response generation process, AI reduces the reliance on manual input, thus mitigating the potential for human error and enhancing the accuracy of information disseminated.
Generative AI offers a multi-faceted approach to address these challenges:
• Efficiency: AI can rapidly process large volumes of data and generate responses at a scale unachievable by human operators.
• Consistency: AI algorithms, trained on established templates and regulatory guidelines, ensure consistent, standardised responses across all communications.
• Regulatory Compliance: AI can be programmed to integrate regulatory requirements into every response, minimising non-compliance risk.
• Accuracy: By reducing the reliance on manual input, AI minimises the potential for human error, improving the accuracy and reliability of responses.
IMPLEMENTATION CONSIDERATIONS AND BEST PRACTICES
• Training Data: High-quality, diverse, and compliant training data is essential for effective AI model development. This data should include a wide range of medical enquiries, and corresponding responses that adhere to regulatory guidelines.
• Template Creation: Templates should be curated by subject matter experts (SMEs), medical professionals, and regulatory professionals to ensure accuracy, completeness, and adherence to guidelines like the ABPI Code. Templates should be regularly reviewed and updated to reflect changes in regulations and industry practices.
• Quality Assurance: Rigorous quality assurance processes are crucial for maintaining the accuracy, relevance, and safety of AI-generated responses. The methods include continuous AI performance monitoring, human review of responses, and regular feedback loops to identify and address errors or biases.
• Non-Promotional Content: System owners must train AI models to avoid any language construed as promotional, adhering to regulatory boundaries. The training requires careful selection of training data and ongoing monitoring of AI outputs.
• Explainability and Transparency: Mechanisms must be carefully designed to prompt AI systems to explain their responses, making it easier for human reviewers to understand the reasoning behind the AI’s output and identify potential errors or biases.
• Bias Mitigation: AI models can inadvertently perpetuate biases present in the training data. Implementing strategies to mitigate biases is crucial, such as diversifying training data, using fairness metrics, and regularly auditing the model›s performance across different demographic groups.
POTENTIAL PITFALLS
• Overreliance on AI: While AI can be a powerful tool, it should not be seen as a replacement for human expertise. Human oversight and review remain essential to ensure the quality and safety of AI-generated responses.
• Data Privacy and Security: The use of AI in healthcare requires strict adherence to data privacy and security regulations. Robust measures must be in place to protect sensitive patient information and ensure compliance with applicable laws.
• Lack of Transparency: AI models can be complex and challenging to interpret, leading to a lack of transparency in decision-making. It is essential to develop AI systems that can provide clear explanations for their outputs, enabling human reviewers to understand the reasoning behind the AI’s decisions.
CONCLUSION
Integrating generative AI, specifically LLMs, into the response generation process represents a paradigm shift for the pharmaceutical industry. AI can enhance efficiency, improve customer service, and ensure adherence to regulatory standards by addressing challenges of critical volume, compliance, timeliness, and accuracy. However, careful implementation, ongoing monitoring, and adherence to best practices are crucial for realising the full potential of AI while mitigating potential risks. With these considerations in mind, generative AI can revolutionise communication within the pharmaceutical industry, benefiting patients, healthcare professionals, and companies.
If you’re interested in finding out more about integrating generative AI into medical information systems, then join us for the PIPA 2024 MI conference on 19th September, where Paul will provide more detail on this subject. For further information and to book, please go to: https:// pipaonline.org/conference/conference-2024/
Paul Dames ApprovalFlow International PTY LTD
GETTING STARTED WITH PATIENT ENGAGEMENT: KEY PRINCIPLES AND TOP TIPS
The premise of patient engagement is simple – the people using a product or service are the ones who are best placed to provide input on its design so that it is designed WITH users rather than with a perspective of solely “for” users. In much of the work we do in the pharmaceutical industry, the end user is a person living with a condition or a carer or family member. They are the ones that will be affected by the medicine, information or solution, such as an app or educational programme, that is developed.
In this article we consider the fundamental principles of collaborating with these end users and some of the key practicalities to consider. We also provide top tips for collaborating with patients. Whilst areas of collaboration may differ depending on specific projects, the fundamental principles remain the same.
GETTING YOUR PATIENT ENGAGEMENT OFF THE GROUND
Start by asking yourself or the project team, “how and at what points could involving patients improve this piece of work?” At this stage do not dismiss ideas that you believe are impractical or which would not receive ‘compliance’ approval – the aim is to be clear on specific areas in which patient input could be valuable.
Once you are clear on how and at what points patient input would improve your work, you can start thinking about how you might involve patients. To do this, consider the principles recommended by the NHS Health Research Authority (HRA)1:
• Involve the right people
• Involve enough people
• Involve those people enough
• Describe how it helps
PRINCIPLE
1: INVOLVING THE RIGHT PEOPLE
Does your work need expert guidance, or are you seeking to learn more about the user experience? This will determine who you involve. It’s important to remember that, just as among healthcare professionals, there is varying depth and knowledge that patients can bring, as illustrated by the schematic below. At the top of the pyramid, patient key opinion leaders (KOLs) will have extensive experience in many areas of pharma which will be applicable across therapy areas. They will also be knowledgeable about the drug development process and commercialisation activities, and could also have other specific expertise, like health technology assessment or diversity and inclusion.
In contrast, patients by experience can share their personal experience of living with a condition or contribute to mapping unmet needs or patient pathways, but are unlikely to be aware of how pharma works. Choosing the right type of patient for an
engagement is similar to choosing the right type of person for a job role. You wouldn’t ask a recent medical school graduate to perform a heart transplant, for instance!
You may wonder where patient organisations fit into this, and there is no single answer. It’s important to understand that patient organisations are not necessarily collectives of individual patients. Each one will have their own focus such as advocating for (or even funding) research or providing support and information. These may or may not be aligned with your work. If alignment exists, you could consider collaborating with a patient organisation, but bear in mind that the ideal is for you to have a direct interface with the end users of any information, product or tool you develop. Patient organisations may also be able to help you find people to contribute without being involved themselves.
Other places to find patient collaborators include:
• social media (including LinkedIn for patient KOLs)
• conferences/congresses/events
• patient networks (e.g. HealthUnlocked, Patients Like Me)
• recommendations from colleagues and patient advocates
Figure 1. A model to inform involving the most appropriate patients
PRINCIPLE 2: INVOLVING ENOUGH PEOPLE
If you require user insight, you will need a representative number of contributors, so they reflect the diversity of the group for whom your material is intended. If you require expert support, you may only need input from a single individual.
PRINCIPLE 3: INVOLVING PEOPLE ENOUGH
A common problem is patients being invited to review or feedback on materials late in the development process. This has several drawbacks: at this stage deadlines are often looming and resource has been largely expended, limiting the capacity to implement changes. Therefore, consider involving patients at the design stage so that they can advise on what they actually need. With medical information, this might mean patients advising on template design, format and language.
A ladder of participation (Figure 2) can aid your patient engagement planning, with the aim of involving patients at the highest level possible, ideally co-production. If your initial reaction is that coproduction or co-design isn’t possible or appropriate, challenge yourself to consider why this is - we often underestimate what is possible in this space. This is also where an expert patient could share their experience with you.
The Patient Information Forum has developed a validated process for co-producing health information, The Perfect Patient Information Journey3. You can find more information on this, including a number of case studies, on their website.
More general tools for co-production are also available at University College London’s Co-production Collective. (See ‘Sources of Helpful Guidance’)
PRINCIPLE 4: DESCRIBING HOW IT HELPS
At first, this final element of HRA guidance may seem unnecessary if you were able to plan your activity. In its original context, it refers to being able to explain why patient engagement will benefit the project. In the broader context this means being able to convince others to support your patient engagement work. This could be colleagues in legal, compliance or quality who might be unaware of the concept but whose support will be necessary for your patient engagement to progress. So you need to be able to both convince them how it will improve the outputs and how it can be done in line with guidance and regulations. Useful materials to support this are listed in the ‘Helpful guidance’ box.
GETTING STARTED
If you haven’t previously involved patients in your work you may feel slightly daunted by the idea. This is not unusual. A good first step is to check whether your company has staff dedicated to supporting patient engagement and consult them. If not, there is plenty of guidance available – the challenge might be finding it and determining which resources will be useful in your particular circumstances. You may also want to consider engaging an expert patient in a consultancy role. They can provide valuable insight into similar work with other organisations, including how to develop or adapt processes.
Figure 2. Ladder of patient participation in the context of information production
SOURCES OF HELPFUL GUIDANCE:
TOP TIPS FOR SUCCESS
• Follow the HRA principles and use the ladder of participation to plan
• Involve patients early in the material development
• Involve key stakeholders (e.g. legal, compliance) early
• Keep agreements/contracts simple (see Useful Resources list for guidance)
• Have a single point of contact for patient contributors. As you introduce others from the company, remember to describe their role.
• Use lay language and avoid acronyms/companyspecific terms. Patients who may not have knowledge of medicines development, medical information etc. are usually keen to learn. Take time to describe your work. If they have deeper interest, the EUPATI website is a good source of information.
• At the start, clarify what the project is about, including how and when participants will contribute, as well as what they can expect from you and your company.
• Your world may be new to some patients, particularly Code of Practice considerations, e.g. not promoting ahead of licence. Explain parameters in which an engagement can happen and why some things may not be possible.
• Respect people’s time – contribution to your project is likely to be just one part of a busy life. Be flexible, especially as a patient’s circumstances might change unexpectedly, due to their health condition.
• Patients may not be tech-savvy. Be ready to flex or simplify your approach.
• Thank patients and tell them how their insights have been utilised (or if not, why not). The feedback loop is important.
PFMD PEM Suite: https://pemsuite.org
PFMD Training in patient engagement for pharma and Medtech (free to use): https://pemsuite.org/patient-engagementtraining/
WECAN guidance on agreements (includes templates): https:// wecanadvocate.eu/rapp/
The Co-production Collective, University College London: https:// www.coproductioncollective.co.uk
ABPI Working with patients and patient organisations - A sourcebook for industry (August 2022) https://www.abpi.org.uk/ media/dj5dvske/11555-abpi_patient-sourcebook_aw_v22023.pdf
CIOMS Working Group XI report, Patient involvement in the development, regulation and safe use of medicines: https://doi. org/10.56759/iiew8982
Patient Information Forum: https://pifonline.org.uk/
References:
1. NHS Health Research Authority. Four principles for meaningful involvement of patients and the public in health and social care research. https://www.hra.nhs.uk/planning-and-improvingresearch/best-practice/public-involvement. Accessed 20 JUN 2024
2. https://q.health.org.uk/blog-post/repeatprescribing-through-co-design-a-q-exchangeproject-update/ Accessed 20 JUN 2024
3. The Perfect Patient Information Journey, Patient Information Forum. https://pifonline.org.uk/services/perfectpatient-information-journey/ Accessed 20 JUN 2024
For further information, please contact the authors: Trishna Bharadia at trishna.bharadia@gmail.com Liz Clark at Liz@Kissanes.co.uk
Liz Clark Consultant Pharmaceutical Physician & Visiting Lecturer & Patient Engagement Theme Lead, Centre for Pharmaceutical Medicine Research, King’s College London
Trishna Bharadia
Patient Engagement Consultant, The Spark Global; Visiting Lecturer, Centre for Pharmaceutical Medicine Research, King’s College London
From Challenge to Opportunity: Enhancing Literature Surveillance in Medical Affairs
During the past decade, the role of Medical Affairs (MA) professionals has evolved from custodians of scientific data to pivotal players at the intersection of research, development, and commercialisation strategies. A recent McKinsey report titled “A Vision for Medical Affairs in 2030” emphasised the importance of MA professionals in integrating data and insights to meet evolving healthcare demands. The Medical Affairs Professional Society (MAPS) echoes this sentiment, emphasising the growing importance of understanding medical and technological advancements to develop impactful MAdriven strategies.
Amidst this evolving landscape, MA professionals face the daunting task of managing an ever-increasing volume of biomedical research, with over 1.7 million new citations indexed by PubMed in 2022 alone. This deluge of data presents a formidable challenge:
Trishna Bharadia
HOW CAN MA PROFESSIONALS EFFICIENTLY SIFT THROUGH INFORMATION TO FIND RELEVANT, HIGHQUALITY STUDIES FOR INFORMED DECISION-MAKING?
The answer lies in embracing innovative solutions such as literature management technologies, specialised databases, multi-disciplinary collaboration, and real-time digital reporting and sharing. These strategies are not just necessary for navigating the vast landscape of medical research; they are crucial for shaping the future of patient-centric healthcare decision-making.
this enables swift navigation through extensive literature, ensuring relevant information is quickly accessible.
Something to Consider: despite the advances in technology, it’s important to recognise that manual search and sorting efforts continue to play a crucial role in many MA teams’ workflows, providing a necessary layer of human oversight.
ENGAGE WITH SPECIALISED DATABASES
When focusing on utilising databases that are specifically tailored to specific therapeutic areas or diseases, you can minimise the retrieval of irrelevant data, ensuring that search results are as relevant and useful as possible. This access to key findings, including real-world evidence and clinical trial results, is enhanced by tapping into specialised databases and registries dedicated to clinical studies, imaging, and genetics. This provides a more targeted and efficient search strategy.
Implementation Tip: offering a curated list of essential databases in these specialised areas can provide more precise guidance to researchers, streamlining the research process further.
FORM MULTI-DISCIPLINARY TEAMS
Consider creating collaborative teams that include a diverse group of professionals such as MA professionals, data scientists, epidemiologists, health outcomes researchers, clinical researchers, and safety specialists. These types of multi-disciplinary teams are instrumental in evaluating the outputs of literature searches. By harnessing diverse subject matter expertise, the team can enhance their ability to interpret various data types and integrate them into actionable insights that are practical and valuable.
Patient Engagement Consultant, The Spark Global; Visiting Lecturer, Centre for Pharmaceutical Medicine Research, King’s College London
Value: a collaborative effort is key to synthesising comprehensive views from disparate pieces of literature, thereby enhancing the quality and applicability of the insights generated.
ADOPT REAL-TIME DIGITAL REPORTING/INFORMATION SHARING
EMBRACING LITERATURE MANAGEMENT TECHNOLOGIES
Leveraging custom and/ or market-available software solutions designed specifically for the efficient handling of medical literature can have immense benefits. These technologies play a crucial role in streamlining the tasks of searching, retrieving, and organising publications to enhance efficiency and save time in today’s high-pressure environment. By employing advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms to markedly enhance search accuracy and speed,
Implement or adopt digital reporting tools and dashboards that provide instant notifications and updates on new studies, research findings, and developments within specific areas of interest. Realtime tools are crucial for maintaining an up-to-date knowledge base in rapidly evolving fields, allowing MA professionals to collaborate and facilitate informed decision-making.
Going beyond emails, it’s essential to explore and utilise creative dissemination techniques for these reports to ensure that the communication is effective and engages all relevant stakeholders
efficiently, moving beyond traditional methods like email for more dynamic and interactive sharing of information.
Here are some examples of what that could look like:
• Interactive Dashboards: sse real-time data visualisation and alert systems for immediate updates (Tableau & Power BI offer these options)
• Collaborative Platforms: employ digital platforms (tools like Slack or Microsoft Teams) for specialieed discussion channels and information sharing
• Short Video Summaries: offer concise recorded video overviews of key publications/ findings for ease of understanding and reference (Note: engage your training teams to lead these!)
• Automated Alerts: implement tailored notifications for the latest publications or news
• Mobile Applications: consider using or developing an app for instant notifications on new research, competitive intelligence, and industry updates
• Regular Virtual Meetings: plan and conduct online sessions for ongoing learning and strategic discussions
Optimising medical insights and capabilities within Medical Affairs organisations requires at least these key strategies to be embraced. If you can ensure you have a tailored software solution to efficiently handle medical literature, employ specialised databases when resources tailored to specific therapeutic areas are required to minimise irrelevant data retrieval and ensure relevance. Form and
engage with multi-disciplinary teams to foster diverse perspectives and enhance data interpretation.Lastly, embrace real-time digital reporting and information-sharing tools to facilitate instantaneous updates on new studies and developments. This will ensure your team remains abreast of relevant information in their respective fields of interest.
This article was written in partnership with CCC. Learn how CCC’s solutions support medical affairs teams here
Dr Sarah Guadagno Independent Medical
Communications and Medical Affairs
Consultant
Adjunct Faculty at the Massachusetts College of Pharmacy and Health Sciences (MCPHS) – School of Professional Studies
