Uncertainty and variability is pervasive in medical decision making with insufficient evidence-based medicine and inconsistent implementation where established knowledge exists. Clinical decision support constructs like order sets help distribute expertise, but are constrained by knowledge-based development. We previously produced a data-driven order recommender system to automatically generate clinical decision support content from structured electronic medical record data on >19K hospital patients. We now present the first structured validation of such automatically generated content against an objective external standard by assessing how well the generated recommendations correspond to orders referenced as appropriate in clinical practice guidelines. For example scenarios of chest pain, gastrointestinal hemorrhage, and pneumonia in hospital patients, the automated method identifies guideline reference orders with ROC AUCs (c-statistics) (0.89, 0.95, 0.83) that improve upon statistical prevalence benchmarks (0.76, 0.74, 0.73) and pre-existing human-expert authored order sets (0.81, 0.77, 0.73) (P<10(-30) in all cases). We demonstrate that data-driven, automatically generated clinical decision support content can reproduce and optimize top-down constructs like order sets while largely avoiding inappropriate and irrelevant recommendations. This will be even more important when extrapolating to more typical clinical scenarios where well-defined external standards and decision support do not exist.