Healthcare represents at least a tenth of developed country’s GDP, with demography pushing to higher levels year after year. In principle technology could drive costs in both directions; in practice it has worked like a ratchet: upside, innovations are extending the scope of expensive treatments, downside, institutional and regulatory constraints have hamstrung the necessary mutations of organizations and processes.
As a result, attempts to spread technology benefits across healthcare activities have dwindle or melt away, even when buttressed by major players like Google or Microsoft.
But built up pressures on budgets combined with social transformations have undermined bureaucratic barriers and incumbents’ estates, springing up initiatives from all corners: pharmaceutical giants, technology startups, healthcare providers, insurers, and of course major IT companies.
Yet the wide range of players’ fields and starting lines may be misleading, incumbents or newcomers are well aware of what the race is about: whatever the number of initial track lanes, they are to fade away after a few laps, spurring the front-runners to cover the whole track, alone or through partnerships. As a consequence, winning strategies would have to be supported by a comprehensive and coherent understanding of all healthcare aspects and issues, which can be best achieved with ontologies.
Ontologies vs Models
Ontologies are symbolic constructs (epitomized by conceptual graphs made of nodes and connectors) whose purpose is to make sense of a domain of discourse:
- Ontologies are made of categories of things, beings, or phenomena; as such they may range from simple catalogs to philosophical doctrines.
- Ontologies are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
- Ontologies are meant to be directed at specific domains of concerns, whatever they can be: politics, religion, business, astrology, etc.
That makes ontologies a special case of uncommitted models: like models they are set on contexts and concerns; but contrary to models ontologies’ concerns are detached from actual purposes. That is precisely what is expected from a healthcare conceptual framework.
Contexts & Business Domains
Healthcare issues are set across too many domains to be effectively fathomed, not to mention followed as they change. Notwithstanding, global players must anchor their strategies to different institutional contexts, and frame their policies as to make them transparent and attractive to others players. Such all-inclusive frameworks could be built from ontologies profiled with regard to the governance and stability of contexts:
- Institutional: Regulatory authority, steady, changes subject to established procedures.
- Professional: Agreed upon between parties, steady, changes subject to accord.
- Corporate: Defined by enterprises, changes subject to internal decision-making.
- Social: Defined by usage, volatile, continuous and informal changes.
- Personal: Customary, defined by named individuals (e.g research paper).
Ontologies set along that taxonomy of contexts could then be refined as to target enterprise architecture layers: enterprise, systems, platforms, e.g:
Depending on the scope and nature of partnerships, ontologies could be further detailed as to encompass architectures capabilities: Who, What, How, Where, When.
Concerns & Architectures Capabilities
As pointed above, a key success factor for major players would be their ability to federate initiatives and undertakings of both incumbents and newcomers, within or without partnerships. That can be best achieved with enterprise architectures aligned with an all-inclusive yet open framework, and for that purpose the Zachman taxonomy would be the option of choice. The corresponding enterprise architecture capabilities (Who,What, How, Where, When) could then be uniformly applied to contexts and concerns:
- Internally across architecture layers for enterprise (business and organization), systems (logical structures and functionalities), and platforms (technologies).
- Externally across context-based ontologies as proposed above.
The nexus between environments (contexts) and enterprises (concerns) ontologies could then be organised according to the epistemic nature of items: terms, documents, symbolic representations (aka surrogates), or business objects and phenomena.
That would outline four basic ontological archetypes that may or may not be combined:
- Thesaurus: ontologies covering terms, concepts.
- Document Management: thesaurus and documents.
- Organization and Business: ontologies pertaining to enterprise organization and business processes.
- Engineering: ontologies pertaining to the symbolic representation (aka surrogates) of organizations, businesses, and systems.
Global healthcare players could then build federating frameworks by combining domain and architecture driven ontologies, e.g:
As a concluding remark, it must be reminded that the objective is to federate the activities and systems of healthcare players without interfering with the design of their business processes or supporting systems. Hence the importance of the distinction between ontologies and models introduced above which would act as a guaranty that concerns are not mixed up insofar as ontologies remain uncommitted models.
- Open Ontologies: From Silos to Architectures
- Ontologies & Enterprise Architecture,
- Systems, Information, Knowledge
- Knowledge Architecture
- Ontologies & Models
- Data Mining & Requirements Analysis
- Davis R., Shrobe H., Szolovitz P., “What is a Knowledge Representation?”, AI Magazine, 14(1):17-33, 1993
- Sowa John F., John A. Zachman, “Extending and formalizing the framework for information systems architecture”
- James Odell, CSC Catalyst Ontology
- Provost F., Fawcett T., “Data Science for Business” O’Reilly (2013)
- Alphabet’s Cityblock
- Reference Architecture for Healthcare (Open Group)