Cause for celebration: Humankind has entered a new era for detecting disease.
Screening that identifies disease before any symptoms manifest enables early intervention and treatment. Millions of lives — of newborn babies and people of all ages — are being saved thanks to new screening techniques that employ a multidimensional approach, combining time-tested biochemical techniques (traditional testing measures such as blood tests and imaging) with omics technologies and other big-data sources that are now widely available. This breakthrough combination helps all of humanity, but individual people also benefit, from newborns whose autoimmune disorder is detected right after birth to adults who find out their lung cancer is susceptible to immunotherapy. These are exciting times for researchers, physicians, and patients, and for the population as a whole. But that capability involves a precise balancing act: handling ever-increasing amounts of patient data, safeguarding its privacy, and ensuring that only authorized users can access it — while also remaining flexible enough to adapt to new discoveries and developments.
In this Executive Conversation, Madhuri Hegde of Revvity and Nick Jena of Persistent Systems discuss addressing those challenges in a process they describe as “a marriage of biology and digital engineering,” with the goal of making data more meaningful so that it translates into better patient outcomes. They also share insights about creating an effective data engineering ecosystem, the roles of artificial intelligence (AI), machine learning (ML), automation, telemedicine, and other technologies to continuously improve ways to identify and treat disease.
Key Takeaways
Enhanced Data Utilization and Integration
Persistent Systems and Revvity emphasize the importance of combining diverse data sets—genomic, biochemical, and imaging data—to provide comprehensive clinical interpretations. This integrated approach is revolutionizing disease detection and treatment.
Early Detection and Personalized Care
The conversation underscores the critical role of early detection, from newborn screening to adult disease management. Advanced digital engineering and AI enable healthcare professionals to detect diseases early and tailor treatments to individual patient needs.
Role of AI and Machine Learning
AI and machine learning are pivotal in managing and analyzing vast amounts of healthcare data. These technologies uncover hidden patterns and associations, allowing for more accurate diagnostics and effective therapeutic interventions.
Explore further insights from this discussion that offer actionable strategies for leveraging digital engineering to improve healthcare outcomes
Digital engineering helps early detection by enabling the use of the vast amounts of data generated by these processes. Another trend currently driving advancements is the growth of telemedicine and remote monitoring in disease screening. Those innovations will help these providers and researchers reach out to populations in remote areas.
Madhuri Hegde
Senior Vice President and Chief Scientific Officer, Revvity View bio
Early detection requires being able to harness vast stores of patient data while making sure that it stays private and only authorized users can access it. That, in turn, requires having a rigorous data engineering framework. But the approach must still be flexible enough to accommodate changes in use over time as well as the inevitable increases in data volume.
Nick Jena
Vice President, Healthcare and Life Sciences, Persistent Systems View bio
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Madhuri Hegde
Madhuri Hegde is the senior vice president and chief scientific officer of Revvity and an adjunct professor of pediatrics at Emory University School of Medicine. She has more than 25 years of experience in clinical diagnostics and is board-certified in clinical molecular genetics. She serves on the boards of the American College of Medical Genetics and Genomics Foundation and the American Board of Medical Genetics and Genomics, among others. She received bachelor’s and master’s degrees from the University of Bombay, India, and a Ph.D. from the University of Auckland, New Zealand, completing postdoctoral studies at Baylor College of Medicine.
Nick Jena
Nick Jena is vice president of healthcare and life sciences at Persistent Systems. His team of subject matter experts focuses on high-value solutions and custom development for healthcare and life sciences by leveraging advanced technologies, including artificial intelligence, cloud computing, automation, and analytics. He has been closely associated with the healthcare and life sciences industries for more than seven years. His role involves evaluating and promoting the latest solutions, partnerships, and techniques in product engineering, withemphasis on those two industries.
