AI, The Modern 'Archangel Gabriel': How AI- Predictive Model Could Change the Way Critical Care Works.

Time of India

a day ago

Why is this a boon?
A transition from reactive to proactive care:
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The healthcare sector has swum past violent tempests, fighting through evils, rogues, and unethical people. Like unauthorized lobotomies, non-disposable syringes, penicillin (the 'all-healing cure'), and the list shan't end. Today, past the tempest, there shines a rainbow: AI. Through its predictive model, it is rapidly changing the way critical care works in the modern healthcare sector. Healthcare professionals, according to emerging studies, may be able to predict with significant precision a patient's timing of death. Apart from that, it also makes the patient's possibility of recovery more accessible via the implementation of a real-time clinical decision support system.With such a tool, healthcare professionals could assess patients' conditions more effectively and make better decisions regarding hospital resource allocation, especially in environments where those resources are limited. Not only is resource allocation made efficient, but several other factors have been influenced as well. For instance, AI-driven imaging showcased its outperforming capabilities, wherein AI was able to identify cancer cells several months prior to how they would've been identified via traditional methods.As previously mentioned, AI's predictive nature has the potential to make resource allocation efficient, thereby contributing to lessened ICU overcrowding, resulting in being accessible to every patient and significantly reducing the chances of a patient being refused treatment under the grounds of not having enough beds in the ICU, a morbid situation the world faced amidst the COVID-19 outbreak. To look at it with a financial perspective, this AI-driven approach would also ultimately cut down hospital costs and lessen the chances of readmission. Overall, the ability to wield a power such as this would help doctors to prioritize care and allocate essential resources, ensuring the patients receive optimum support during their stay.ICU is an environment where every second counts, with the stakes and pressure high. The integration of AI-driven models with their predictive nature has changed conditions of that sort. They analyze patient data such as their vital signs, lab results, and responses to medication. With this information, AI systems can warn of potential emergencies hours prior to traditional methods.At Fortis Healthcare, for instance, these AI tools have transformed treatment for cardiometabolic diseases. The AI-driven technology used at Fortis Healthcare for cardiometabolic disorders creates a digital replica of the patient's metabolism based on thousands of digital data points collected from sources, including the patient's own self-reported preferences and 'non-invasive wearable sensors.' Further, with machine learning, precise and personalized information is curated across multiple patterns such as nutrition, sleep, activity, and breathing processes. Overall, this digital replica is the pivotal point for predicting possible future patterns caused by disease.Similarly, at the Apollo Hospitals , they use the pHRA, 'a unique AI-enabled Personalized Health Risk Assessment .' This predictive AI model predicts potential risks and spots early signs of a disease. This model acknowledges individuality when it comes to lifestyle and an immense variety in risk profile.In essence, predictive models of AI can be powerful game-changers that could potentially generate pivotal improvements in the aforementioned sector by improving access without compromising monetary requirements on massive infrastructure. AI models of this sort would not only boost clinical precision but also drive profits through efficient operations.Lastly, AI, as the public tends to be anxious over, isn't about to replace healthcare professionals; none of the white coat decisions are about to be taken over by black boxes. Rather, the black boxes shall empower the white coat decision-makers as time goes on. ( source