The Institute of Medicine states that most diagnostic errors are caused by flaws in clinician diagnostic thinking. Accurately inferring the correct diagnosis from the patient history is the best way to improve diagnostic accuracy and efficiency. Such an improvement is contingent upon training early phase medical learners how to organize data from a patient history to arrive at the most likely diagnosis of the patient's chief health concern (CC). We describe how organizing the traditional history of present illness into what our trainees have come to call the "All-Inclusive History of Present Illness" (AIHPI) by applying the Bayesian statistical concepts of chronologically sequencing, as suggested by Skeff, both relevant historical risks and known medical events generate a series of pre-event probabilities of the most likely disease causing a patient's CC. Our trainees have enthusiastically recognized that the AIHPI organization process helps them improve both their ability to deliver well-organized, succinct verbal case presentations and the efficiency of generating and communicating what they think is the most likely disease causing a patient's CC.