Linked Clinical Knowledge for Adaptive Coding and Risk Tiering
Keywords:
Connected Clinical Knowledge Network,Adaptive Clinical Coding,Patient Risk Stratification,Clinical Knowledge Graphs,Healthcare Data Interoperability,Predictive Health Analytics,Electronic Health Records,Intelligent Medical Coding,Clinical Decision Support,Patient Risk Prediction.Abstract
A clinical knowledge network integrates clinical data sources, a risk model calculation, and an adaptive coding function. Adaptive coding adjusts the risk profiles of patients in a diverse cohort, up-coding or down-coding them according to each patient's clinical history. The adjusted risk profile then triggers coding mechanisms designed to address specific risk factors or signs of patient deterioration. These mechanisms query data sources external to the cohort to identify care processes and interventions known to influence the down-coded risk factors.
A clear understanding of the adaptive coding framework and its patient risk stratification methods is essential for applying adaptive coding successfully and for judging its expected benefits in the right context. Adaptive coding is intended to improve the performance of data mining and machine learning processes, especially those that draw on historical cohort data and the scientific literature. By adaptively coding a patient's risk profile, these processes can focus on particular aspects of that patient's health. This paper introduces the Connected Clinical Knowledge Network (CCKN), which was developed to support adaptive coding, together with its patient risk stratification methods.
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