How AI accelerates the need for real-world data in healthcare and life sciences
In the past five years, changes, innovation and overall disruption (HCLS) have usually occurred over the course of decades. The forefront of this rapid change is the advancement of emerging technologies such as artificial intelligence (AI) and generative artificial intelligence (Genai). If one takes a step back, it is amazing to reflect on how the discourse around emerging technologies has developed over the past two to three years. Once focused on “what” is now focused only on conversations on “how”. How do we use this technology effectively and responsibly? How do we conduct highly skilled and train medical professionals to use the technology responsibly? How do we make sure we maximize the full functionality of these tools? In this context, the rise of these technologies simultaneously illuminates another core requirement of HCLS: the need for high-quality, comprehensive real-world data (RWD).
Given the lack of high-quality data, companies that excel in compiling and managing data effectively attract the interest of industry partners and distinguish themselves from competitors.
While there are countless factors at work, here are some ways to emerging AI applications and redefine the need for RWD for HCLS,
Improve health care services
In many ways, the growth of AI applications and the demand for high-quality RWD are similar to relay races. The information chain used by clinicians to diagnose, treat and monitor patients depends on the baton of historical data. This data needs not only to be passed efficiently, but also in a way that does not ignore key details. AI proposes key links in the chain, but the ultimate RWD quality and the value it can provide is the basis for the success of AI applications.
Reshape the science of life
Similar to healthcare, AI in life sciences continues to emerge, as IT pharmaceutical manufacturers and biotech companies offer their own independent programs to discover, develop and commercialize therapeutic assets that leverage the technology. Whether it is finding and identifying new therapeutic targets or stratifying patients with more or less likely response to drugs, RWD is an anchor factor. In short, biopharma and biotech companies require high-quality RWD to power their AI-driven innovation.
It starts with data
We have heard the old adage that knowledge is power, and data is that power when it comes to AI applications about healthcare, biopharmaceuticals and medical device applications. Especially real, well-planned patient data. However, such data often presents challenges. For example, data may be in silos or in different systems. However, to draw clinical grade conclusions, the basis of such models needs to be patient data. Therefore, the rise of Genai applications in HCLS has made the demand for RWD even greater than before. Generation AI models often produce hallucinations or results that are not present in actual data. This inaccuracy makes AI applications unusable, further highlighting the essential role of quality RWD in developing functional and reliable healthcare AI applications.
The way forward
As applications across HCLS accelerate, RWD in turn needs to continue to drive this advancement, with the focus increasingly shifting to maximizing the data value of HCLS organizations. For example, we have begun to see healthcare organizations that manage patient interactions, from hospitals to home health providers, implementing steps to better organize and prepare their data for downstream purposes. This is the case in their care center and outside. Investing in data infrastructure, governance processes and strategies to effectively handle RWD is critical for HCLS organizations to continue their AI travel. Those who do well will be best suited to get value from the data while enhancing patient outcomes.
Photo: ACE2020, Getty Images
Kristin Pothier is a U.S. division life sciences leader in Kristin, KPMG, U.S., with nearly 30 years of experience in strategic consulting and research in the healthcare and life sciences industries. Her focus areas are global pharma, diagnostics, equipment and business strategies, growth strategies and M&A areas for consumer healthcare companies, investors and healthcare institutions.
Ash Shehata is a U.S. division healthcare leader at KPMG US ASH, a U.S.-based consultant director with over 25 years of experience specializing in healthcare IT, including meaningful use achievements, health information exchange, vertical clinical record, cloud-based healthcare, and clinical and other healthcare business intelligence.
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