We integrate pragmatic, transparent machine learning and natural language processing into healthcare workflows to assist clinicians, reduce documentation burden, and predict capacity bottlenecks without replacing human medical judgment.
Converting conversational doctor-patient dialogue into draft SOAP note structures with entity extraction for medications, dosages, and complaints.
Extracting ICD-10-CM and CPT code suggestions from narrative discharge summaries to assist health information management (HIM) coders.
Time-series forecasting predicting inpatient admission surges and emergency department wait times based on seasonal and epidemiological trends.
OCR models extracting structured lab values and physician signatures from faxed paper reports into searchable FHIR observation records.