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Symptom checkers have emerged as an important tool for collecting symptomsand diagnosing patients, minimizing the involvement of clinical personnel. Wedeveloped a machine-learning-backed system, SmartTriage, which goes beyondconventional symptom checking through a tight bi-directional integration withthe electronic medical record (EMR). Conditioned on EMR-derived patienthistory, our system identifies the patient's chief complaint from a free-textentry and then asks a series of discrete questions to obtain relevantsymptomatology. The patient-specific data are used to predict detailedICD-10-CM codes as well as medication, laboratory, and imaging orders. Patientresponses and clinical decision support (CDS) predictions are then insertedback into the EMR. To train the machine learning components of SmartTriage, weemployed novel data sets of over 25 million primary care encounters and 1million patient free-text reason-for-visit entries. These data sets were usedto construct: (1) a long short-term memory (LSTM) based patient historyrepresentation, (2) a fine-tuned transformer model for chief complaintextraction, (3) a random forest model for question sequencing, and (4) afeed-forward network for CDS predictions. In total, our system supports 337patient chief complaints, which together make up $>90\%$ of all primary careencounters at Kaiser Permanente.

SmartTriage: A system for personalized patient data capture, documentation generation, and decision support