Objectives: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED). Methods: As part of a quality improvement project, we simultaneously implemented three interventions: a domain-specific ontology, contextual autocomplete, and top five suggestions. Contextual autocomplete is a user interface that ranks concepts by their predicted probability which helps nurses enter data about a patient‚Äôs presenting problems. Nurses were also given a list of top five suggestions to choose from. These presenting problems were represented using a consensus ontology mapped to SNOMED CT. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a mixed methods retrospective before-and-after study design. Results: A total of 279,231 consecutive patient encounters were analyzed. Structured data capture improved from 26.2% to 97.2%. During the post-implementation period, presenting problems were more complete (3.35 vs 3.66) and higher in overall quality (3.38 vs. 3.72), but showed no difference in precision (3.59 vs. 3.74). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6, a 95% improvement. Discussion: We demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution. Conclusion: Implementation of a domain-specific ontology and machine learning-driven user interfaces resulted in improved structured data capture, ontology usage compliance, and data quality.