Alan Dix explores AI and Social Justice
Author of Introduction to Artificial Intelligence
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by Mohit Joshi, Infosys 9 March 2020
Hyper-personalization is the buzzword of our times. A taxi-hailing app that knows where you are, a map tool that proactively informs you of traffic conditions at the exact time you leave for work, or the most personalized shopping recommendations on e-commerce websites – personalization is everywhere.
But its most meaningful impact is undeniably coming from the healthcare and life sciences sectors.
With a wide variety of factors such as genetics, environment and lifestyle increasingly affecting our health and the efficacy of treatments, the need for hyper-personalized medication is here to stay. This need has opened the doors for precision medicine – that is, focusing on patient-centric treatment and medication.
Precision medicine does not necessarily focus on individual patients but rather on enhancing the efficacy of treatments by placing patients into subgroups based on genetics, environment and lifestyle (among other factors) and consequently predicting their medicinal and treatment needs.
Deloitte’s 2019 Global life sciences outlook predicts that the global personalized medicine market will rise more than 11% CAGR for the period 2017–2024. The report calls out specific technologies like healthcare data analytics and AI that are shaping the future of personalized medicine.
Not exactly. Big data and AI in medicine are helping practitioners better understand the factors that influence diseases, how diseases evolve over time, enhance diagnostic accuracy and optimize treatment plans. But the challenge is that most hospitals and physicians are not yet ready to support this form of analytics and AI implementation.
The end goal of big data and AI in precision medicine is to empower providers with the tools they need in order deliver personalized treatment to patients as well as to yield cost savings for healthcare systems. Significant progress has been made towards this goal, but the most critical impact of these technologies can only come when collaboration and information exchange happen seamlessly. When this happens, all stakeholders of the precision medicine ecosystem will be involved in data collection and analysis.
They will be able to leverage data at critical points of decision in their clinical workflow; patients will be able to share genomic and other health-related information in real-time; and researchers will be empowered with genomic information from electronic health records (EHR). Naturally, high-quality data and strong, reliable technology infrastructure that enables collaboration have become the clarion calls of the life sciences sector.
But two significant challenges emerge in leveraging the full potential of big data and AI in precision medicine - the high cost of studying human genomics and a lack of regulatory framework. Not every member of the medical ecosystem is prepared to budget for genomics and precision medicine, given the novelty of these practices and concerns around data privacy. Therefore, providers, payers, hospitals and healthcare systems continue to somewhat shy away from funding precision medicine projects.
So how do we move forwards from here?
Over the past few years, electronic medical records and wearable technology have become prevalent and inexpensive, rendering patient data easily accessible. In order to unlock the full potential of precision medicine, the ecosystem needs to find better ways to merge this data, share, collaborate and make life-saving decisions on the go.
Naturally, the ecosystem must demand solutions that break the data silos that currently exist between various healthcare delivery verticals. This can only come from a platform-based approach capable of processing high volumes of data to reveal actionable insights.
Currently, government regulations don’t allow the full potential of AI and big data in the life sciences sector to emerge, specifically in precision medicine which is almost entirely focused on population data. Nor does an industry-wide data exchange exist in most healthcare systems around the world.
We believe that governments, academia and the medical ecosystem – payers, providers, pharmaceutical and technology companies - must come together to develop a regulatory framework that focuses on data privacy and the ethical concerns surrounding AI, to allow big data and AI to live up to their potential.
It is time to have an unrelenting focus on leveraging meaningful innovation for human life and longevity. It is important that the healthcare and life sciences ecosystems rapidly prepare for this new era of hyper-personalized care. The millennial patient expects it, and global technology companies, such as Infosys, are prepared to deliver it.
Mohit Joshi is president and head of Banking, Financial Services & Insurance, Healthcare and Life Sciences at Infosys
Author of Introduction to Artificial Intelligence