2030 Life Sciences and Health in the Digital Age (Part 2)
As part of its mission to help members anticipate and prepare for changes in the pharmaceutical industry, The Pistoia Alliance has developed a research paper which sets out to consider what the life science, biopharma R&D and healthcare ecosystem might look like in 2030, in particular how the increasing adoption and sophistication of technology will affect companies and patients alike.
This article is the second part of a two-part series based on that paper. Written in retrospect from the world as it is in 2030, it looks at how healthcare has changed in the intervening decade.
The intention of this 2030 report is to stimulate debate as to what the future may hold. There are many scenarios one could legitimately put forward and challenge. We have chosen one such set of scenarios. It is not to say it will be correct. However, in presenting these scenarios it is hoped that one can identify signals that identify the likely drivers of change over the next decade.
The first article covered the socio-economic and political evolutions that have happened, the subsequent effects on population health and the innovative technologies which have had the greatest impact. This article builds on those assessments and looks at the patient-centric innovations we can expect to see in the next decade, as well as some of the expected changes in regulation, and how life science, biopharma and healthcare organisations can work together to tackle the growing skills crisis.
Outside of the technological advances we explored in the previous article, the last decade has seen several important scientific advances which have significantly improved patient care:
Real World Data (RWD):
RWD, or ‘data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources’, has been transformative for the pharma industry’s approach to clinical trials with external control arms, disease models and natural histories, site selection, patient recruitment, etc (1). The better quality and wider availability and accessibility of RWD – due to the increasing deployment of the FAIR data principles (2) – has enabled more precise in silico clinical trials to increase the efficiency and effectiveness of clinical development programmes to meet unmet medical needs (3,4).
RWD is being used extensively to inform Health Technology Assessment (HTA) and the value of therapies. Projects such as the MIT Leaps project (5) are using AI and Machine Learning (ML) to enable patients to receive timely access to the most appropriate therapeutics for their needs, while providing key stakeholders with the Real World Evidence (RWE) they need to improve their decisions related to the development, access and use of therapeutics for the target disease.....
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