Key Questions Answered
- What are the primary areas of specialization for an insights-driven technology and data company?
- How does such a company (an insights-driven technology and data company) utilize AI in its operations?
- In what ways does this impact patient outcomes and research advancement?
- How does this reimagine the use of real-world evidence by industry stakeholders?
AI in Market Access: Transforming Pharmaceutical Research with Real-World Evidence and Predictive Modeling
The global pharmaceutical industry is undergoing a significant transformation, with an expected compound annual growth rate (CAGR) of 5.9% between 2023 and 2028. This growth follows a 2023 slowdown driven by reduced demand for COVID-19 vaccines and therapeutics. Key factors driving this resurgence include the integration of real-world evidence (RWE) and real-world data (RWD) into drug development, and the accelerated adoption of generative artificial intelligence (Gen-AI). These technologies are revolutionizing how the industry approaches market access and clinical research, helping pharmaceutical companies navigate a rapidly changing landscape.
Since 2021, Gen-AI has dramatically advanced drug discovery by enhancing data analysis, predictive modeling, and real-time insights. This trend continues, especially as AI-powered tools enable more efficient research outcomes. Simultaneously, the increasing use of RWE provides deeper insights into therapeutic effectiveness, patient journeys, and product validation, all critical for regulatory approval and market access.
Traditionally, market access strategies have focused on areas such as oncology and rare diseases. However, there is now significant potential for growth in fields like psychiatry, immunology, and infectious diseases. By developing AI and data-driven tools for these therapeutic areas, companies can tap into new markets while improving personalized treatment approaches. Yet, one of the biggest challenges in the healthcare industry remains the fragmentation of data sources and the knowledge gaps this creates, from research and development (R&D) to clinical decision-making.
A compelling example of how AI is addressing these challenges comes from OM1, a technology and data company specializing in RWE and personalized medicine. Founded in 2015, OM1 is pioneering the use of next-generation AI platforms to improve patient outcomes by leveraging real-world data. One particularly noteworthy case study involves the development of a machine learning model to estimate Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) scores using unstructured clinical notes.
Case Study: Estimating SLEDAI Scores with AI
In clinical practice, the use of the SLEDAI—a tool for measuring disease activity in patients with systemic lupus erythematosus (SLE)—is inconsistent, and clinician-recorded SLEDAI scores are often missing from real-world datasets. OM1’s machine learning model aims to address this gap by estimating SLEDAI score categories based on unstructured clinical notes.
The model was developed and validated using a large, real-world dataset and performed well in estimating SLEDAI score categories. This AI-driven approach not only provides more accurate insights into disease activity but also makes these data more valuable for research. By generating reliable SLEDAI estimates from real-world data, this model could play a crucial role in future studies of SLE and potentially be adapted to other rheumatological conditions, improving disease activity measures across the board. (Validation of a machine learning approach to estimate Systemic Lupus Erythematosus
Disease Activity Index score categories and application in a real-world dataset:
https://www.om1.com/wp-content/uploads/2021/05/OM1_Alves_SLEDAI_AI_May2021.pdf)
The potential impact on clinical practice and future developments is significant. With access to reliable, AI-generated SLEDAI scores, clinicians can better track disease activity, even when explicit SLEDAI scores are not recorded. Moreover, researchers can use this enhanced dataset to drive deeper insights into SLE treatment efficacy, patient responses, and overall disease management.
AI-Driven Solutions for Market Access and Clinical Research
OM1’s innovations, including the machine learning model for SLEDAI score estimation, illustrate how AI is transforming market access strategies. The company’s suite of products, powered by its PhenOM® platform, enables pharmaceutical companies and healthcare providers to leverage advanced AI and real-world data to optimize decision-making and improve patient outcomes.
The Orion solution, for example, provides pharmaceutical decision-makers with a comprehensive view of the patient journey, using extensive clinical data to uncover unmet needs, real-world clinical factors, and complex disease patterns. By understanding patient subtypes—such as those with specific symptoms in systemic lupus erythematosus—pharma organizations can tailor treatments more effectively and improve product positioning. (Identifying Progressing Patients in an Artificial Intelligence (AI) Based Cohort of Nonalcoholic Steatohepatitis (NASH):https://www.om1.com/wp-content/uploads/2019/12/OM1_ISPOREU_Poster_NASH_36x72_Oct2019-1.pdf’; Using artificial intelligence to identify patient characteristics associated with rapid fibrosis progression in NASH: a retrospective cohort study: https://d1auq2hz846ncj.cloudfront.net/01_S_Gbadamosi_8a544c9299.pdf)
Similarly, the Lyra solution integrates AI-driven phenotyping into clinical practice, delivering real-time insights to clinicians and patients. Lyra helps identify underdiagnosed conditions, improves treatment adherence, and predicts patient responses to specific therapies. This enhances personalized medicine by tailoring treatment plans to individual patient needs.
Polaris, OM1’s solution for clinical trial recruitment, also leverages AI to address one of the most challenging aspects of research—identifying eligible study participants. By using advanced algorithms to analyze RWD, Polaris streamlines recruitment, reduces costs, and accelerates timelines, especially in complex disease areas like cutaneous lupus.
The Future of AI in Market Access
OM1’s focus on integrating AI into its products is driving a new era of evidence generation and personalized medicine. With solutions like Orion, Lyra, and Polaris, the company is not only transforming howpharmaceutical companies approach market access but also ensuring that real-world data can be leveraged to improve patient outcomes.
The successful validation of a machine learning model for SLEDAI score estimation demonstrates how AI can fill critical gaps in clinical data and research. By applying this approach to other disease activity measures, the pharmaceutical industry can further enhance its ability to deliver personalized, data-driven healthcare.
As the pharmaceutical industry continues its transformation, companies like OM1 are at the forefront, leveraging AI and RWE to ensure that new therapies reach the right patients while optimizing clinical and financial outcomes. These innovations are paving the way for a future where healthcare is more personalized, efficient, and accessible to all.
Monika J. Dziuba, October 2024
This article has been prepared with the assistance of AI and reviewed by an editor. For more details, please refer to our Terms and Conditions. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author.
