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In our previous post of this blog post series about knowledge graphs and AI in the pharmaceutical and healthcare industry, you got an overview of the challenges knowledge-intensive organizations face to be able to support data-driven innovation and improve healthcare outcome.

In this blog post, you will 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.

Stay tuned for upcoming blog posts that will help you understand how knowledge graphs and AI can leverage your organization. Our partner Findwise, global experts in search-driven solutions for the pharmaceutical and healthcare industry, are bringing all their expertise in information management and knowledge engineering into this blog post series.

Knowledge Graphs and the Connective Tissue

A smart semantic information engine will connect a variety of information systems relevant within the pharmaceutical and healthcare industry. These information systems will not be limited to own data repositories but will also link to external authoritative resources. Here we have different resources that are available.

On the one hand, there are standards for information exchange such as HL7/FHIR and code systems and terminologies such as SnoMedCT, ICDx, UMLS, MeSH, EuroVoc, IPSV and ESCO.

On the other hand, linked-open-data ontologies such as ISA2 core vocabularies and business vertical ontologies are available.

Furthermore, there are also open-data registers, and nationwide registers with healthcare provider directories, population registers and many more key masters.

A Simple User Scenario (Use-Case)

  1. A user (i.e clinician) interact (create, read, update and delete [crud]) with his/hers information system (i.e electronic healthcare record system)
  2. The interaction might tax queries to be sent to the information engine to qualify the accuracy of data, classification or other means to improve/ synchronize data-sets.
  3. The information engine acts as a proxy to external authority resources, registers and linked-open-data spaces, and will synchronize accordingly.
  4. The information engine (+ master sources) will infuse information/data and enhance, augment, or link into the work process within the information system.
  5. The information system uses different means to do data-integration, either with an enterprise service bus (asynchronous) messages or push services to update the information engine. Each information system has their in-built schemas and business rules to data management, but the information engine acts as the connective tissue (linking) not storing all data into one major repository. In essence web of data within the organization, as enterprise-linked data.

In the best of worlds the quest: ”the ones only principle” is the holy grail to be able to automate/augment (AI), integrate (interoperate) and make do of a cohesive information environment crossing both organizational boundaries, practices & disciplines, human facets to life and modalities. This does not mean one to rule them all, but rather smart ways to connect things. As with the Internet and the web of data.

Smart Semantic Information Engine

The smart semantic information engine will usually be a combination of services and tools:

 

In this second post of a blog post series about the pharmaceutical and healthcare industry, you learned how Knowledge Graphs and AI are helping companies and organizations to connect internal and external resources to profit from a smart semantic information engine.  Read the first post of the blog post series to get an overview of the challenges knowledge-intensive organizations face to leverage data-driven innovation and improve healthcare outcome. 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.

Fredric works as an information architect at Findwise and researcher/lecturer at Gothenburg University. Focus areas are knowledge management, knowledge engineering, and information management. With almost 30 years as IS/IT professional and researcher, Fredric works within the realm of future of Healthcare and Lifescience, using semantic technologies and AI, as well research within e-Health.

  

Fredric Landqvist

Principal Information Strategist and Researcher, Findwise