Data analytics and artificial intelligence (AI) will dominate the priorities healthcare organisations need to tackle this year according. Developing an enterprise-wide data strategy and demonstrating the value of AI are among the key areas of focus.
Leaders from McKinsey, Deloitte and Carnegie Mellon University debated what AI, and analytics solutions healthcare stakeholders should prioritise when planning their 2023 strategy. In the interview they gave to Health IT Analytics they emphasised the need to identify the value and justify the investment into analytics.
Identifying the value
Venkat Inumella, partner at McKinsey & Company argued that the pharmaceutical industry has no complete understanding and conviction on how much value data analytics can drive.
And given the size of the investment flowing into data science, there is an urgency to identify and establish the values AI brings to the table.
Improving the efficiency of internal processes and other non-clinical areas such as claims management and revenue cycle management are among the most prominent use cases. AI and data analytics have a sound ground in clinical areas as well, from population health management to AI-enabled imagining and diagnostics.
Despite the numerous application areas, pharmaceuticals still struggle with demonstrating and measuring the value-add of the technology.
Investing in the basics
In the long run, it pays off to invest in the foundations that will fuel scalable advanced analytics solutions. The basics include enterprise-wide data governance, cloud infrastructure and recruiting talent.
It’s easy to overlook the foundations and prioritise the delivery of the shiny, AI-enabled data visualisation tools to impress management and shareholders. However, without a stable data management strategy in place, innovation will be an uphill battle.
Despite the recent hype around AI, data analytics methods are better placed to reach quick wins from structured data and reveal invaluable trends and insights for pharmaceuticals. Data analytics have a significant potential to demonstrate value to the organisation, more so than AI has.
However, about 80% of medical data remains unstructured and untapped after it is created. Organisations have been adapting NLP (natural language processing) methods to make sense of the immense amount of clinician notes, medical images, healthcare records and social media data generated by patients and healthcare professionals.
This is why immersing in AI is inevitable, even though data analytics is more cost-effective and accessible.
Healthcare organisations must continue to invest in AI-based innovations to remain competitive and deliver on the promise of the future of healthcare.
Read more about tackling unstructured data in the healthcare industry with NoSQL databases.
An enterprise-wide data strategy
Sounds obvious, but it’s not as common as it should be. Enterprise-wide data strategy is more of a buzzword than an actual plan in large pharma companies. In an established, highly-regulated organisation creating an aligned data strategy is like climbing the North Face of Mount Everest. It’s steep, difficult and you likely step on a few toes and get into icy situations.
But it’s worth the struggle if the aim is to build scalable and sustainable data analytics and AI solutions.
James F. Jordan, professor of healthcare and biotechnology management at Carnegie Mellon University argues that the human experience is crucial when planning an enterprise-wide strategy. Putting the clinician and patient back into the centre of healthcare is an essential element for long-term success.
Beyond the human element, access to care, quality of care and health equality are evergreen topics healthcare systems should address, highlighted Kumar Chebrolu, healthcare data and AI practice lead at Deloitte.
Everything considered the experts agreed that prioritising a unified data strategy, value demonstration and investing in data foundations could bolster clinical and non-clinical goals in 2023.
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