The postoperative phase is critical in which vigilant monitoring and personalized care are paramount to successful recovery. AI-driven postoperative monitoring systems represent a significant leap forward in providing continuous, real-time assessment of patients as they transition from the operating room to recovery. These systems utilize ML algorithms to analyze a plethora of data, including vital signs, pain levels, and recovery metrics. By doing so, AI facilitates early detection of postoperative complications, enabling healthcare providers to intervene promptly. Integrating AI in postoperative monitoring not only enhances the accuracy of complication detection but also contributes to the efficient allocation of healthcare resources.
(Artificial intelligence-assisted system in postoperative follow-up of orthopedic patients: exploratory quantitative and qualitative study. Bian Y, Xiang Y, Tong B, Feng B, Weng X. J Med Internet Res. 2020;22:0)
- But what if we don't have access to patient data ?
- How can we streamline a process to improve the experience for both the patient and the doctor ?
One solution would be to use a Large Language Model(LLM) trained to understand the patient history and specific type of operation. This LLM should be trained with the help of a doctor.
Instead of having a relationship between the doctor and the patient, we could follow a simplistic approach :
- The trained LLM knows how to structure the questions to retrieve sentiment data. The sentiment data is sent to a tracking sentiment service.
- Patient Score : The score would indicate whether they are at risk or in a healthy condition, offering valuable insights for both patient and doctors.
- The doctor can access the patient score within the server, ask personalized and streamline their workflow.
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