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Knowledge Graphs and AI in the Pharmaceutical and Healthcare Industry: Part 1

November 5, 2018

With the increasing speed of technological advancements, the pharmaceutical and healthcare industry needs to break up departmental data silos with Knowledge Graphs and AI to understand the value of their data.

Our partner Findwise, global experts in search-driven solutions for the pharmaceutical and healthcare industry, presents a series of four blog posts to help you understand how knowledge graphs and AI can leverage your data-driven innovation and improve healthcare outcome. Thanks to Fredric Landqvist for his contribution and expertise in knowledge engineering and information management.

Closing the Well-Being, Healthcare Circle with Knowledge Graphs and AI

We face grand societal challenges pinned down in the 17 UN sustainability goals and specifically number 3 Good Health and Well-being. Humans live a longer life, which shifts the population pyramid. There is a rapid increase in costs attached to healthcare provisioning and at the same time radical changes to practices and improved patient outcome measure (ICHOM). The digital revolution will create better healthcare (The Creative Destruction of Medicine, Eric Topol). In the same ballpark, the development of future personalized pharma treatments and medication from the life sciences’ industry undergoes radical changes. A sanity check sadly exposes a lack of interoperability. Instead of realizing a cohesive well-being and healthcare information commons, or smart data-driven innovation in the Lifesciences.

The focus has to be people-centric, both regarding practices and disciplines within medicine, healthcare, and the social welfare continuum, or life sciences. As well from the individual view with measures to well-being, coherence to treatments, self-monitoring and more.

Contemporary themes revolve around the concept smart, as with smart society, -cities, -houses, -watches, -mobile phones and more, and usually combining the prefix impose a utility with some AI.

Top Three Challenges for Knowledge-Intensive Organizations

The top three challenges for knowledge-intensive organizations and institutions as within healthcare and life sciences to enable data-driven innovation and improved healthcare outcome:

  1. How to create sustainable knowledge management & knowledge engineering practices, processes, governance and tools to connect the dots in the contemporary maze-like workplace? The connected enterprise, with all levels of interactions with actor networks within and outside the corporate walls, intertwingled.
  2. How to infuse knowledge graphs and AI at Work, for clinicians or life science practitioners?
  3. How to close the circle with people in the overarching good health and well-being challenge? With patient participation and engagement to self-monitor and personalized treatments for improved life quality, and feedback to data-driven innovation?

In this first post of a blog post series about knowledge graphs and AI in the pharmaceutical and healthcare industry, you get an overview of the challenges knowledge-intensive organizations face to leverage data-driven innovation and improve healthcare outcome. Read the second part of the series here and learn by the hand of a use case about how connecting your siloed departmental data with external authoritative resources will leverage the value of your content assets. Read the third part to learn how knowledge engineering and knowledge graphs contribute to the digital workplace of clinicians.

Learn more about how PoolParty Semantic Suite has helped the pharmaceutical and healthcare sector in our industry case study.

Closing the well-being, healthcare circle with Knowledge Graphs and AI

Fredric Landqvist

Fredric Landqvist

Principal Information Strategist and Researcher