patient journey trajectory
Many traditional LLMs only consider the patient's diagnosis and age. But what if this extended to multiple and diverse records such as demographics, clinical characteristics, vital signs, smoking status, past procedures, medications, laboratory tests, etc.? By integrating these capabilities, patient creates a more comprehensive view of the disease, allowing for a more comprehensive treatment plan.
Adding data can significantly improve model performance for various downstream tasks, such as predicting disease progression and subtyping different diseases. Given its additional features and interpretability, LLM can help physicians make more informed decisions about disease course, diagnosis, and risk factors for various diseases. It's easy to see how this approach can be applied to the customer journey for marketers, or risk assessment for insurance and financial companies. The possibilities are endless.
Improving medical chatbots
It's important to combine structured data, such as electronic medical records and prescriptions, with unstructured data, such as clinical notes, medical images, and PDFs, to create a complete picture of the patient. This data can be used to provide user-friendly interfaces, such as chatbots, to collect information about patients and identify cohorts of patients who may be candidates for clinical trials, population health, or research efforts. Masu. Sounds easy, but don't forget the privacy and data limitations that make this difficult for healthcare and other compliant environments.
