Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL

Abstract

Technologies utilizing cutting-edge methodologies, including artificial intelligence (AI), machine learning (ML) and deep learning (DL), present powerful opportunities to help evaluate, predict, and improve patient outcomes by drawing insights from real-world data (RWD) generated during medical care. They played a role during and following the Coronavirus Disease 2019 (COVID-19) pandemic by helping protect healthcare providers, prioritize care for vulnerable populations, predict disease trends, and find optimal therapies. Potential applications across therapeutic areas include diagnosis, disease management and patient journey mapping. Use of fit-for-purpose datasets for ML models is seeing growth and may potentially help additional enterprises develop AI strategies. However, biopharmaceutical companies often face specific challenges, including multi-setting data, system interoperability, data governance, and patient privacy requirements. There remains a need for evolving regulatory frameworks, operating models, and data governance to enable further developments and additional research. We explore recent literature and examine the hurdles faced by researchers in the biopharmaceutical industry to fully realize the promise of AI/ML/DL for patient-centric purposes.

The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data

“…Such methods may also be subject to patient-centric considerations and complexities, such as patient-reported outcomes and data privacies [ 95 ]. Such patient-centric considerations, especially in an era of artificial intelligence for drug development and other purposes, are increasingly seen [ 96 – 101 ]. These technological innovations can be adopted to maintain high levels of engagement, such as digital health [ 94 ] and artificial intelligence [ 96 ], among the trial participants through interactions that can respond to participants’ queries, prompt adherence to prescribed protocols, and incorporate participant feedback to fit the CT into their daily life [ 102 ].…”