Natural language based clinical recommendation system

A clinical recommendation system, comprising a processor and a non-transitory memory storing instructions that when executed, cause the processor to:

• receive a natural language query from a care provider via a user interface of the clinical recommendation system, the natural language query including patient data of a patient;
• generate a response to the natural language query using a large language model (LLM), the response including a recommended treatment based on the patient data;
• store the response in a patient decision workflow of the patient;
• and
• deliver the response to the care provider via a display device and/or a speaker of the clinical recommendation system;
• wherein
• the LLM is trained using supervised learning on human- or chat-bot-generated natural language training pairs generated in accordance with a recommendation model based on clinical guidelines, and optimized by training the LLM on various corpora of unlabeled data using reinforcement learning based on a reward model trained on human-ranked training data.

A method for providing clinical guidance to care providers via a clinical recommendation system, the method comprising:

• receiving a query from a care provider, the query including data of a patient;
• translating the query into a natural language prompt;
• submitting the natural language prompt to a large language model (LLM), the LLM trained to output a response with a recommended treatment for the patient based on the patient data and one or more sets of digital clinical guidelines;
• extracting the patient data from the natural language prompt;
• predicting a most probable treatment for the patient based on the extracted patient data, using a prediction model trained on population statistics and anonymized historical patient data and/or prior recommended treatments;
• extracting the recommended treatment included from the response generated by the LLM;
• comparing the recommended treatment with the predicted most probable treatment;
• in response to the recommended treatment matching the predicted most probable treatment, storing the recommended treatment in a patient decision workflow of the patient and delivering the response to the care provider via a display device and/or a speaker of the clinical recommendation system;
• and
• in response to the recommended treatment not matching the predicted most probable treatment, logging an inconsistency of the recommended treatment with a current practice.