The Future of Regulatory Intelligence With Conversational AI

Introduction

In order to achieve enhanced patient safety, life science industry is highly regulated, adding complexity to achieve product compliance.¹ Life sciences regulators are responsible for ensuring that food, human and veterinary drugs, medical devices, cosmetics, and other pharmaceutical products meet the applicable regulations.² Advancement in technology and scientific research has increased the pace at which regulations change. In addition to regional regulatory requirements, pharmaceutical organizations need to comply with international regulations too.

The first challenge in the changing regulatory landscape is the rapid increase in the number of drugs under observation as well as the ever-evolving standards and guidelines by regulatory agencies.³ Consequently, the amount of data that needs to be searched and analyzed has also increased exponentially (see Figure 1 below). Life sciences industry regulations are complex and the amount of data that needs to be analyzed is huge, thereby necessitating humongous manual efforts and time to maintain compliance of the product.

Figure 1. Information sources for regulatory intelligence

Source: TCS ADD

The second challenge is to interpret the information and assess the impact of changing regulations on the project, organization, and other stakeholders. Post collating changes in regulations, product assessment needs to be followed to maintain its compliance against the new regulation. However, the entire procedure to filter regulatory communication for products and process this communication is time-consuming. While it is of utmost importance for individuals and organizations to be informed and act according to these regulations, it can be challenging to do so since there are multiple regulations spanning across different geographies with varying frequency of regulatory updates.⁴

The third challenge is the ability to respond quickly and efficiently to changes in regulatory standards and to support the pace of rapid development. If any communication is missed; impact assessment is delayed impacting compliance of the product with revised or new regulations.⁵

Artificial intelligence in regulatory

The use of Artificial Intelligence (AI) in the Lifesciences space has tremendous potential and impact with analyzing complex data sets to enhance drug development. AI can handle the quantum increase in data for regulatory domain by processing tedious tasks and increasing the speed and accuracy of certain regulatory functions such as regulatory information processing, generation of impact analysis reports, gathering insights on current trends, etc. One possible solution to this problem is to utilize an intelligent chatbot, that leverages the concept of conversational AI.

A chatbot is a software that utilizes Artificial Intelligence (AI) to simulate human-like conversation through text or speech. Many chatbots have been created and deployed across a wide range of customer-facing domains such as e-commerce, healthcare, education, and retail. In our case, the chatbot will extract data from regulatory websites and provide information and updates tailored to a customer’s request. Using a chatbot provides a user-friendly and efficient platform for customers to interact with and receive responses to their specific requests without having to search through the mass information available online.

What is conversational AI?

With the use of smartphones and digital devices, user preference is shifting towards personalized experiences. Conversational AI is the subbranch of AI that deals with empowering machines to understand, analyze, and process human language.⁶ As explained in Figure 2 below, chatbots use large datasets, natural language processing, and Machine Learning to understand the context and imitate text and speech responses. Conversational AI assistants (Chatbot) offer human-like conversations and a better experience for users.

Figure 2. Architecture for Conversational AI

Source: TCS ADD

A conversational AI can communicate and empathize with users in a compelling way to assist in desired tasks. Chatbots have moved from a traditional rule-based approach like answering FAQs to more engaging and context-based reasoning.^7,8 They offer various features enlisted below:

1. Time Efficiency—Chatbots can easily connect to various systems and retrieve information in seconds that saves a lot of time for users. This boosts efficiency and reduces the burden of work
2. Ease of use—One of the key features is its ease of access, that simulates a human-like conversation with text or speech format
3. Real time access—To fulfill users' dynamic needs, chatbots can cater to queries through real-time access to various databases
4. Contextual understanding—Each industry carries its own unique terminologies. Chatbots can be easily trained to understand this contextual knowledge and provide accurate responses
5. Scalability—Based on industry needs chatbots are easily scalable to address varying workloads

There are various chatbot frameworks available such as Dialog flow.⁹ Microsoft Bot framework, RASA opensource etc. that can be effortlessly customized and configured to solve specific business problems.

How chatbots can help in regulatory domain?

In regulatory space, a regulatory professional performs various tasks such as maintaining files and data of various events for audit purposes and future reference, recommending strategies to achieve necessary compliance in the company, reporting compliance status, etc. A chatbot will help the regulatory professional to expedite the process of intelligence gathering by using Nextgen technologies such as AI, graph database etc.

The power of conversational AI can be utilized for higher productivity and improved user experience. Chatbots can handle significantly higher requests than humans and process complex information to provide results quickly. It can also work 24/7 and lower query resolution time drastically. In addition, chatbots requires lower operational cost as usage charges will be based upon the utilization of services and provide higher productivity.

Most regulatory professionals work with various products and standards across geographies for data mining, cleaning, and insights collection.¹⁰ Chatbots can easily fetch data for the regulatory updates from official regulatory bodies such as USA’s Food and Drug Administration (FDA) or EMA. Depending on the availability, chat bot either uses the Open FDA API, or extracts data by web scraping the official website. The retrieved data accumulates into datastore. Often regulatory professions juggle between various data sources to get the right information. Chatbot, on the contrary acts as an easy to use and reliable medium to reduces manual work.

The process flow for chatbot-based regulatory intelligence is shown in Figure 3 below. After a user input is received, the Natural Language Understanding (NLU) module identifies the intent/entities and extracts it from the user message. Entities are saved into slots that act as a memory for the chatbot.^11,12 Until all slots are filled for a particular intent, follow-up questions are asked to allocate the slots. When all slots are acquired, the defined action is executed to provide response to user.

Figure 3. Process flow for chatbot

Source: TCS ADD

When compared to the traditional and archaic regulatory intelligence process, chatbots offers multiple benefits.

Figure 4. Traditional vs AI driven approach for regulatory intelligence

Source: TCS ADD

Conclusion

The regulatory chatbot can bridge the gap between users who seek information and vast amounts of data contained across the world in different regulatory websites. It is not only able to efficiently respond to user queries by searching through regulatory information, but is able to accomplish this in a user-friendly, easy to understand manner. Suffice to say, this offers a key to increasing its usability and versatility across the life sciences domain.

Future work and discussion

While the chatbot can classify and recognize the specific user query from the input to return updated URLs, its functionality can be enhanced further. Instead of returning the relevant links when, for example, a user searches for ‘safety communications about Infusion pumps in 2020’, the chatbot could make things easier by responding with a summary of information from those links. This would increase convenience for the user multifold since he/she would not have to individually check each website link. They would be able to extract the relevant information by simply reading the chatbot’s summary of each link and can then choose to visit the website for further information if needed.

The usage of chatbot is not relegated just for insight collection but can be enhanced further by enabling access to various cross-functional teams within the organization. This will decrease the mundane, operational jobs by accessing internal systems of the organization.¹³ As result, surveillance efforts will be reduced, and information will be accessed quickly.

Another area for progress would be to increase the usage of the regulatory body’s API. This would allow a larger variety of questions to be asked by the user and more detailed information to be provided. The usage of API instead of web scraping would also increase code reusability and efficiency. The regulatory chatbot helps in achieving clinical functions such as stacking regulatory changes and creating relevant SOPs for regulatory professionals, identifying recurring patterns in data to improve the process etc. resulting in increased efficiency and productivity.

Rohit Kadam, Researcher, Research and Innovation, Saurabh Das, Head, Research and Innovation, Ashutosh Pachisia, Data Scientist, Research and Innovation, Niketan Panchal, Researcher, Research and Innovation, Anita Ramachandran, Domain Consultant, Medical Devices, Dr. Ashish Indani, Former Head Research and Innovation, Prashant Chaturvedi, Former Data Scientist, Research and Innovation; all with TCS ADD™ Platforms, TCS

References

1. https://www.productlifegroup.com/services/regulatory-intelligence/?msclkid=099684dcaf2111ec9c510499e0ab3545
2. https://www.pharma-iq.com/business-development/articles/regulatory-intelligence-how-ai-will-change-regulatory-operations?msclkid=09966582af2111ecabee610c314e5124
3. https://www.raps.org/news-and-articles/news-articles/2019/2/regulatory-intelligence-risk-management-and-drug?msclkid=09963fa1af2111ecb8c16383eb3860fd
4. https://www.raps.org/news-and-articles/news-articles/2019/1/managing-regulatory-intelligence-for-medical-devic
5. https://www.raps.org/news-and-articles/news-articles/2019/1/proactive-regulatory-intelligence-communication
6. https://www.linkedin.com/pulse/how-conversational-ai-improving-customer-experience-yosef#:~:text=%20The%20Technologies%20Used%20In%20Conversational%20AI%20,enormous%20volumes%20of%20data.%20Conversational%20AI...%20More%20
7. https://www.tcs.com/content/dam/tcs/pdf/Industries/Banking%20and%20Financial%20Services/Charting%20the%20Future%20of%20the%20Wealth%20Management%20Industry.pdf
8. https://medium.com/conversationalai/the-future-of-conversational-ai-16626a81f071
9. https://blog.rasa.com/integrating-rasa-with-knowledge-bases/
10. https://open.fda.gov/apis/
11. https://www.ijert.org/research/an-analytical-study-and-review-of-open-source-chatbot-framework-rasa-IJERTV9IS060723.pdf
12. https://medium.com/coinmonks/rasa-python-weather-chatbot-51fc218d346d
13. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/