Click: AI-trained system for healthcare patients online real-time monitoring System
17th 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
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