A batch of one
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The document discusses the transformative role of AI in life sciences, emphasizing applications in areas such as disease understanding, drug discovery, and patient engagement. It highlights how integrating clinical and chemical data can enhance treatment strategies and optimize care delivery while improving population health management for payors. Additionally, the document mentions the emergence of advanced consumer-facing AI technologies that enhance interactions in health segments like nutrition and personalized medicine.
A batch of one
- 1. Batch of One Batch of One Leo Barella Chief Technology Officer - Takeda
- 5. Genome Transcriptome Proteome Phenotype Environment (Diet, Age, Lifestyle, Drug, Disease) What could happen What might happen What is happening
- 6. AI has broad utility across a number of use case LIFE SCIENCES • Disease Understanding • Drug Repurposing • Drug Discovery PAYORS • Risk Stratification • Patient Engagement • Customer Service CARE DELIVERY • Care Management Plans • Treatment Selection • Remote Monitoring CONSUMER • Nutrition • Care Management • Wellness
- 7. Life Sciences REDEFINING BIOLOGICAL UNDERSTANDING OF DISEASE The integration of clinical data, millions of chemical profiles, in-vivo assay and AI drives target discovery, generating more effective leads at scale DISEASE UNDERSTANDING Breaking down biochemical processes and physiology to better map natural history of health, disease and the diagnostic process specifically analyzing the interaction with the individual patient DRUG REPURPOSING Mapping relationships between known drugs to novel indications by creatively leveraging compound libraries DRUG DISCOVERY With an understanding of structural biology creating new classes of drug categories and interventions. It is more important to know what sort of person has a disease than to know what sort of disease a person has. Hippocrates (460 b.c. - 375 b.c.)
- 8. Payors RETHINKING RISK STRATIFICATION & POPULATION HEALTH Payors are aiming to strike a balance between broad population coverage and meeting members expectations on how they interact with technology. Further AI approaches can help facilitate value based reimbursement strategies RISK STRATIFICATION Applied analytics to predict patient outcomes and inform treatment recommendations PATIENT ENGAGAMENT Machine learning to tailor member outreach based on clinical, claims and contextual data CUSTOMER SERVICE Virtual agents to help members navigate their benefits quickly and efficiently
- 9. Care Delivery Gaining 360º view of patient need Care delivery can be viewed as a complex process with many interdependencies. AI approaches can help streamline the delivery of care and how clinical insights are discovered CARE MANAGEMENT PLAN Optimizing care management and creating guidelines to manage follow-ups, intakes , readmissions and more TREATMENT SELECTION Identifying methods to provide better treatments, early switch rates and improve adherence REMOTE MONITORING Medical or Consumer grade sensors and clinical algorithms track high risk patients beyond facility walls
- 10. CONSUMER Novel interfaces and visual experiences Consumer facing applications of AI are emerging across every major health segment. Advancements in natural language processing (NLP), sensors, voice recognition, augmented reality (AR), sentiment analysis, and more are raising the sophistication of digital interaction and reshaping the consumer experience NUTRITION Virtual agents, food image analysis and personalized nutrition based on microbiome and other biological determinants CARE MANAGEMENT Enabling personalized medicine, based on the use of genomics and research models NOVEL EXPERIENCES Interactive technology and new engagement models via robotics