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Indian healthcare providers are increasingly moving towards AI-powered clinical intelligence. They realise the need to move beyond passive patient monitoring by continuously analyzing health data, identifying potential risks early, and delivering actionable insights. This proactive approach supports timely interventions, personalized care plans, and faster recovery, ultimately improving patient outcomes while enhancing the efficiency and quality of healthcare delivery.
Surjeet Thakur, founder and CEO, TrioTree Technologies noted that technologies like the Remote Patient Monitoring (RPM) is helpful in post discharge care for patients. There are wearable sensors, connected medical devices, and mobile applications that help healthcare providers in tracking the heart rate, blood pressure, oxygen saturation, glucose levels, and medication needs even from far away.
However, it does not always explain why those changes are occurring or what the most appropriate intervention should be, especially when the patient is recovering remotely. Maybe there is a patient recovering from a serious infection may show small fluctuations across multiple health indicators. Individually, these changes may appear normal or insignificant but when viewed together, they may signal an increased risk of deterioration, he added.
It is here clinical intelligence can identify and predict probable risks early, and help clinicians to take right action before a patient's condition worsens. Even as there is a gap between patient alerts and clinical action as doctors receive hundreds of alarms every day from connected devices and monitoring platforms, everything cannot be a medical emergency. This can lead to delays in identifying patients who genuinely need immediate attention, he said
This challenge becomes even much concerned in India, where healthcare resources are often stretched across large patient populations. It means that important conditions can be overlooked, interventions may be delayed and it could be a life-threatening condition for the patient. Now this challenge becomes even more critical in India, because here healthcare resources are being shared and stretched among large populations, said Thakur.
The ability to identify which patient is at risk, and the kind of intervention is required can make the difference between timely recovery and avoidable readmission. To deal with this, we need systems that can provide context, prioritize risks, and help clinicians focus on the patients who need intervention the most, he noted.
The need of the hour is to work towards converting patient data into actionable healthcare converted into actionable insights requiring connecting information from multiple sources, identifying patterns that may not be visible through individual readings. We need to work on converting patient data into timely clinical decisions that support recovery before complications escalate, said Thakur.
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