It has been almost a year since AIGC became widely popular, and its momentum has only continued to grow. It is clear that AI and big data have become drivers of transformation across industries. Especially in life sciences, the combination of these two technologies is providing unprecedented insight for market strategy and enterprise decision-making. "Digital intelligence" and "digitalization" have become some of the most frequently used terms at life sciences conferences.
Recently, we have shared thoughts on content management, representative visits, customer engagement, and more. Today, we want to talk about patient insight in the context of AI + big data.
01 Data Is the Foundation of Insight
As everyone knows, research is the gateway to market strategy. But when facing massive data and complex patient groups, its limitations gradually become visible.
In the past, traditional patient research methods in the life sciences industry often faced challenges in time and cost. This was especially true when handling large and widely distributed datasets. From selecting target populations, conducting in-depth interviews and quantitative analysis, processing data, and finally producing reports, the process was long, costly, and limited in scope. When facing patient samples that are highly diverse and widely distributed, traditional research often struggles to cover them comprehensively and can miss important information.
Through AI + big data, enterprises can obtain large amounts of patient information in a short period of time, cover broader populations, and make up for the shortcomings of traditional research. Big data can quickly generate large volumes of data, while AI can analyze online physician-patient data and behavior. This method not only expands data coverage, but also improves data processing efficiency. In a hematology patient insight project (click for case details), we helped the client collect more than 20,000 complete and valid data records. This was an unprecedented data volume and an unimaginable breadth in the era of traditional research.
02 AI Lets Data Regenerate Data
As mentioned above, traditional patient research lacks breadth, has limited coverage, is hard to replicate at scale, and struggles to answer representative questions. But it is not without advantages. For example, in-depth interviews can adapt flexibly, continue probing, and produce rich insight. They may not be broad, but they are deep.
So how can research be both deep and broad? Research is the gateway to market strategy, and the patient journey is the golden key to patient research.
Returning to our hematology patient insight project, after collecting more than 20,000 records, we used AI technology to break those records into more than 1 million valid analytical data points. This allowed AI to fully understand patient information, needs, treatment status, diagnosis and treatment paths, and other dimensions contained in the data. It built a communication bridge between pharmaceutical companies and patients, forming a clear "context." In effect, it helped patients directly communicate their needs to pharmaceutical companies and helped companies understand the patient journey across a broader scope: who patients are, where they are, how they seek care and move through the system, what medication feedback they have, and more.
In practice, AI + big data helps pharmaceutical companies understand patient flow data that is closest to reality. It enables comprehensive understanding of patient distribution, movement, and preferences by steps, regions, and levels, thereby better identifying target patient groups. By analyzing real medication use and feedback, companies can discover many unexpected insights. These insights may become the gateway to the next market strategy and bring new growth opportunities to the enterprise.
For example, in real patient feedback, companies may discover overlooked information that becomes an important marketing element or a direction for product improvement. By gradually deepening their understanding of patients in this way, enterprises can better meet patient needs and improve HCP satisfaction.
03 Who Said We Have to Abandon the Old for the New?
Does having AI + big data mean traditional research models can be abandoned?
Our practical experience shows that the current relationship is: we need both, and more. In research, we do not need to choose only one method and abandon the other. Instead, we can use traditional methods together with big data + AI to obtain more comprehensive information.
For patient insight, we need traditional research methods to obtain deep insight, while also using big data and AI to conduct a broader "census." Especially in relatively special scenarios, relying only on traditional research can miss a great deal of important information. That is when big data and AI must help.
In short, comprehensive information search helps us better understand patient needs and behaviors, allowing us to formulate more precise market strategies. By combining these two methods, we can bring information that was left in the corners back into the light and gain more valuable insights. After all, our shared purpose is everything for patients, and for every patient.
