| CPC G16H 10/60 (2018.01) [G16H 50/50 (2018.01)] | 20 Claims |
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1. A system comprising:
at least one processor; and
at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, the disease specific treatment algorithm including a decision tree that includes guidelines for treating a specific disease;
processing data corresponding to the disease specific treatment algorithm by inputting the data into a Large Language Model (LLM), the LLM being trained to process disease specific treatment algorithms to generate Clinical Quality Language (CQL) models compatible for Fast Healthcare Interoperability Resources (FHIR) and configured to trigger Clinical Decision Support (CDS) hooks;
receiving one or more first COL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, the one or more first COL models include at least a first CDS hook;
receiving an update to a patient record of the patient from the first EHR system;
generating an updated disease specific treatment algorithm based on the update;
inputting the updated disease specific treatment algorithms to the LLM to receive one or more second CQL models from the LLM;
executing the one or more second CQL models triggering at least a second CDS hook to generate an alert; and
causing transmission of the alert for a medical practitioner associated with the first EHR system.
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19. A method comprising:
receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, the disease specific treatment algorithm including a decision tree that includes guidelines for treating a specific disease;
processing data corresponding to the disease specific treatment algorithm by inputting the data into a Large Language Model (LLM), the LLM being trained to process disease specific treatment algorithms to generate Clinical Quality Language (CQL) models compatible for Fast Healthcare Interoperability Resources (FHIR) and configured to trigger Clinical Decision Support (CDS) hooks;
receiving one or more first COL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, the one or more first COL models include at least a first CDS hook;
receiving an update to a patient record of the patient from the first EHR system;
generating an updated disease specific treatment algorithm based on the update;
inputting the updated disease specific treatment algorithm to the LLM to receive one or more second CQL models from the LLM;
executing the one or more second CQL models triggering at least a second CDS hook to generate an alert; and
causing transmission of the alert for a medical practitioner associated with the first EHR system.
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20. A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, the disease specific treatment algorithm including a decision tree that includes guidelines for treating a specific disease;
processing data corresponding to the disease specific treatment algorithm by inputting the data into a Large Language Model (LLM), the LLM being trained to process disease specific treatment algorithms to generate Clinical Quality Language (CQL) models compatible for Fast Healthcare Interoperability Resources (FHIR) and configured to trigger Clinical Decision Support (CDS) hooks;
receiving one or more first CQL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, the one or more first CQL models include at least a first CDS hook;
receiving an update to a patient record of the patient from the first EHR system;
generating an updated disease specific treatment algorithm based on the update;
inputting the updated disease specific treatment algorithm s to the LLM to receive one or more second CQL models from the LLM;
executing the one or more second CQL models triggering at least a second CDS hook to generate an alert; and
causing transmission of the alert for a medical practitioner associated with the first EHR system.
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