Competency Framework Correlation for Clinical Onboarding
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Solution Overview
Problem
The lack of standardized clinical competency frameworks across medical facilities hinders the comparison and recommendation of learning activities for healthcare professionals, particularly for onboarding clinicians and new medical departments, leading to difficulties in recognizing and updating clinical competencies.
Innovation Solution
A recommender engine that collects and correlates clinical competency frameworks from different hospitals using machine learning to cluster similar frameworks, recommending educational content units based on matched competencies and learning activities, enabling seamless onboarding and framework updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clinical competency frameworks are customized for each medical facility to meet specific clinical needs and specializations, then the frameworks can be tailored to local requirements, but comparing and recommending learning activities across different facilities becomes difficult
Solution Approach 1:
The patent introduces a standardized competency taxonomy as an intermediary layer between customized local frameworks. This taxonomy provides common competency categories and descriptors that enable mapping and comparison across different facility-specific frameworks, allowing both customization and comparability to coexist
Solution Approach 2:
The system creates a universal competency framework structure that can serve multiple facilities with different specializations. By defining core competencies that are applicable across facilities and allowing facility-specific extensions, the system achieves both universality and adaptability
2Adaptability or versatility
If onboarding clinicians are brought in from different facilities with their existing competencies, then facility flexibility and hiring options increase, but recognizing and transferring their competencies to the new facility's framework becomes challenging
Solution Approach 1:
The system implements feedback mechanisms where competency data from onboarding clinicians is continuously mapped, validated, and updated against the standardized taxonomy. This feedback loop enables automatic recognition of transferable competencies and identifies gaps that need to be addressed through additional training
Solution Approach 2:
The system performs preliminary mapping and validation of incoming clinicians' competencies against the standardized framework before full onboarding. This preliminary action identifies compatible competencies in advance, streamlining the recognition process and reducing complexity during actual onboarding
Data Source
AI summary
A non-transitory computer readable medium stores at least one database (30) storing clinical competency framework profiles (32) for clinical competencies for a plurality of clinicians at a plurality of medical facilities; and instructions readable and executable by at least one electronic processor (16) to perform a learning activities recommendation method (100) comprising: linking educational content units (38) completed by clinicians to clinical competencies of the clinical competency framework profiles that are fulfilled by the completed learning activities; correlating clinical competency frameworks of different medical facilities that are for the same or similar clinical competencies in the clinical competency framework profiles; and recommending one or more of the educational content units to a clinician seeking to fulfill a clinical competency in one clinical competency framework based on the correlated clinical competency frameworks and the linked educational content units.