In this commentary, we work out the specific desired functions required for sharing knowledge objects (based on statistical models) presumably to be used for clinical decision support derived from a learning health system, and, in so doing, discuss the implications for novel knowledge architectures. We will demonstrate how decision models, implemented as influence diagrams, satisfy the desiderata. The desiderata include locally validate discrimination, locally validate calibration, locally recalculate thresholds by incorporating local preferences, provide explanation, enable monitoring, enable debiasing, account for generalizability, account for semantic uncertainty, shall be findable, and others as necessary and proper. We demonstrate how formal decision models, especially when implemented as influence diagrams based on Bayesian networks, support both the knowledge artifact itself (the "primary decision") and the "meta-decision" of whether to deploy the knowledge artifact. We close with a research and development agenda to put this framework into place.
Keywords: Bayesian analysis; decision analysis; decision support; knowledge engineering; predictive modeling.