AI-trained system for healthcare patients online real-time monitoring System.

 

Click: AI-trained system for healthcare patients online real-time monitoring System

17^th October 2023

Whitepaper

AI-trained system for healthcare patients online real-time monitoring System.

1.    The AI system is well-trained by Training data.

2.    The AI system is well-tested on Test data.

3.    Matured AI system in healthcare on unseen Data and sending Alerts to Doctors, and nursing control room.

Note: Suddenly AI system observes patterns for which is system is not trained but close to other patterns.

In these cases, how AI system be managed and what alerts will be sent to Doctors, and the nursing control room?

Pradeep K. Suri

Author and Researcher

In the scenario, where an AI-trained system for healthcare patients' online real-time monitoring encounters a situation it hasn't been explicitly trained for but is close to other patterns, there are several steps to manage the situation and send appropriate alerts to doctors and the nursing control room:

1. Anomaly Detection and Assessment:

   - The AI system should have a robust anomaly detection mechanism in place to identify cases that deviate from the usual patterns it has been trained on.

   - When the system detects an anomaly, it should assess the situation by comparing it to similar patterns in its training and testing data.

2. Risk Assessment:

   - After assessing the anomaly, the AI system should perform a risk assessment to determine the potential severity of the situation.

   - It can consider factors like vital signs, patient history, and the specific nature of the anomaly.

 

3. Alert Classification:

   - The system should classify alerts into different categories based on the level of risk. For example, alerts can be categorized as low, medium, or high risk.

4. Consultation with a Human Expert:

   - If the AI system is unsure about how to handle the situation, it should consult a human healthcare expert. This can be done by connecting to a remote doctor or healthcare professional.

5. Alerts to Doctors and Nursing Control Room:

   - Based on the risk assessment and consultation (if necessary), the AI system should send alerts to doctors and the nursing control room.

   - The content of the alert should include information about the anomaly, the risk level, and any relevant patient data.

6. Adaptive Learning:

   - The AI system should also have a mechanism for adaptive learning. This means that it can learn from these situations and gradually improve its ability to handle previously unseen scenarios.

 

7. Continuous Monitoring:

   - The system should continue to monitor the patient in real-time, gathering additional data to refine its assessment and improve its response.

 

8. Patient Privacy and Consent:

   - Ensure that patient privacy and consent are respected throughout this process. Patients should be informed about the AI system's capabilities and any data collection.

The nature of the alerts sent to doctors and the nursing control room will depend on the specific details of the situation. For instance, in the case of an unexpected parental pattern, the AI system might send an alert like:

- Alert Level: Moderate

- Anomaly Description: The AI system has detected a pattern that is similar to, but not exactly like, its trained data. It appears to be related to parental patterns.

- Risk Assessment: The situation is currently considered moderate risk. The patient's vital signs remain stable.

- Recommendation: Consult with a healthcare professional for further assessment and advice. Continue monitoring the patient closely.

It's crucial to maintain a balance between the capabilities of AI and the expertise of human healthcare providers to ensure the best possible patient care in situations that AI systems haven't been explicitly trained for.

   Thank you