Knowledge Based Systems play a very important role, within Healthcare, with a primary goal of supporting a high quality service at an optimal cost. A widely accepted knowledge acquisition technique is through the use of Business Rules in a natural language like format. As clinical terminology is centric to HealthCare, a large percentage of these rules use industry standard codes to describe clinical concerns. Usually there is a large number of codes to denote the various clinical processes and procedures within Healthcare. These can translate to a combinatorially explosive number of Business Rules within the HealthCare IT system. It is common practice to leverage a Rule Engine to execute these rules and produce decisions within the system. A Rule Engine accepts a collection of rules called a ruleset. Within Healthcare Insurance, these rules and rulesets embody regulatory, policy and contractual concerns. To effectively manage this huge body of rules and rulesets, it is typical for Knowledge Based Systems to reuse rules across rulesets. Further, the knowledge required to author these rules and constructing comprehensive rulesets is specialized and requires deep expertise within the domain. Further, this also requires an expertise with authoring unambiguous business rules and operating the Rule Management Systems or Knowledge Based Systems. There is a need for the continuous Governance of these rule based knowledge assets for the organization. Typically knowledge experts in this field have a decade or more of experience. An emergent challenge or trend witnessed within the industry is that the experienced knowledge workers are retiring and their positions are being replaced by people not as experienced. This paper proposes techniques to build a layer of intelligent capabilities that can transform the methods for the automation, creation and maintenance of knowledge artifacts, that can aid and support the inexperienced knowledge workers with the effective management and administration of these assets within a Healthcare system.
CITATION STYLE
Agaram, M. K. (2019). Intelligent Foundations for Knowledge Based Systems. Advances in Science, Technology and Engineering Systems, 4(4), 73–93. https://doi.org/10.25046/aj040410
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