AI in the health care research industry 

Editor’s note: Matt Walmsley is the chief global services officer at Survey Healthcare Global.

The life science and health care insights industry, valued at $6.6 billion, is undergoing a significant transformation thanks to artificial intelligence. Historically, this market has lagged behind consumer market insights in adopting new technologies due to stringent privacy, compliance and regulatory requirements. Each country's unique regulations further complicate the process of sourcing primary market data efficiently and at scale.

However, the increasing need to expedite the introduction of new treatments and maximize revenue, especially in competitive therapeutic areas, is driving a willingness to modernize outdated processes. AI serves as a powerful tool for automation and efficiency, enabling suppliers to significantly reduce project timelines.

AI is a broad and evolving category with initial implementations using analytic AI, where algorithms classify, predict, cluster or segment data more efficiently. The current buzz is around generative AI, where usage of large language models can streamline participant engagement, survey administration, survey programming, data analysis and reporting. Training these models requires meticulous care to ensure accuracy and representative data from health care practitioners as well as compliance and privacy. With the right guardrails, each type of AI can eliminate mundane tasks and streamline traditional market research. 

5 ways AI has improved the life science and health care insights industry

1. Respondent targeting

Proprietary machine learning algorithms can match the best-fit respondents to research studies and predict response rates to invite the optimal number of participants. One implementation delivered 25% faster speed-to-insight after implementing this capability vs. non-AI-powered sampling techniques. The more the model is used and “trained,” the more efficient it can become, expediting the speed to insights even further.

2. Response prediction

Dynamic profiling and validation create comprehensive backgrounds on panel members, enhancing follow-up efforts. AI/ML algorithms match best-fit respondents, inviting the appropriate number based on a predicted response rate. This can shorten survey fielding times and enhance respondent engagement and response rates.

For example, in one project, the use of AI enabled a 34% improvement in physician member profile depth. This permitted 50% fewer survey invites to be sent while reducing median survey fielding duration by 20%.

3. Automated programming

Generative AI can manage survey programming tasks, reducing the time needed for custom program surveys by half. This approach not only saves time but also ensures consistency, enhancing accuracy and repeatability. It allows programmers to concentrate on more critical areas of expertise rather than spending time on repetitive tasks like copying and pasting text.

4. Target optimization

AI-guided intelligent routing of surveys to potential respondents ensures faster completion with fewer screen-outs. Algorithms analyze the timing and completion of surveys, optimizing subsequent survey offers based on respondents past engagement and topic preferences. This optimization also allows users to easily search offers to engage in subsequent surveys of interest allowing faster completes for our surveys with less respondent thrash. By offering additional relevant surveys to respondents, AI helps boost satisfaction for the community, further enhancing speed to insights.

5. Project optimization

By aggregating hundreds of projects across key consistent drivers, first-party data firms can manage ongoing fieldwork more efficiently to ensure timely and on-budget delivery for customers. Analytic AI algorithms monitor project progress, flags items needing adjustments and provides proactive warnings about potential roadblocks. This approach eliminates all research-based variables, so teams can focus on consistent and repeatable fieldwork metric standards, ensuring reliable research delivery.

Primary first-party data drives many critical business decisions in health care, influencing the development of new therapies that can alleviate disease burdens. In an increasingly competitive and complex post-COVID-19 health care landscape, stakeholders are eager for solutions that save time and reduce costs. The application of AI in primary data collection is delivering significant impact to market research companies, consultants and their pharmaceutical sponsors by accelerating the pace of fieldwork that ultimately drives medical innovation.