AI plays a critical role in Max Healthcare’s digital journey
Max Healthcare is creating a Data Lake of anonymised data to enhance the focus on appropriate use cases rather than investing time for data collation
AI in healthcare has gained substantial momentum and it is changing the way healthcare is being practiced and the outcomes being measured. Apart from delivering the best in class patient care, AI has also drawn higher attention and is now a part of management vision.
“There has always been scarcity of trained healthcare professionals in the industry and the situation persists today. In order to maintain the right balance between demand and supply, technology plays a pivotal role, which bridges the gap and also creates a lot of efficiency in terms of cost optimisation and fast delivery,” says Prashant Singh, Director & Chief Information Officer, Max Healthcare.
AI plays a critical role in Max Healthcare’s digital journey and is already at different stages of AI adoption depending on the maturity levels of the model. Max is working with various startup companies to train the AI model powered by their clinical and non-clinical data in an anonymized form. Along with various other digital automation initiatives, AI is an integral part.
AI for healthcare use cases
“At Max Healthcare, we are in discussions to create a Data Lake of anonymised data which can be further used for various AI models and verticals of healthcare. It would help us focus on the appropriate use cases rather than investing time for data collation. Max Healthcare has already started testing an AI model in the field of radiology by augmenting diagnostic outcome of X-ray and CT in fracture detection, chest CT lung cancer screening and chest XR differential diagnosis, etc,” mentions Singh.
Another AI-powered model captures data and enables clinicians to instantly digitise their case sheets using a digital pen and encoded paper which transcribes OP Prescription and transfers the data back into EMR, which can be used for data analytics and clinical decision-making. Singh further highlights that manpower attrition is a major challenge, especially in nursing, and AI platforms can help to identify the potential cases for attrition. AI model being explored allows the provider to take timely interventions to prevent attrition.
Another use case of AI being discussed is for the timely identification of heart diseases to improve patient’s lives. Currently, it is difficult to identify heart diseases in regular assessments as the typical clinical indicators are not sufficient to identify the problem and sometimes specialists are not available to review and identify the critical cases. AI prototype uses a standard classifier that is trained to look at a set of input parameters of the patient and classify the patient as a positive or negative case for heart problems.
Predicting re-admission, mortality through AI model are other few parameters which enhances the clinical decision support.
AI to identify and propose the right services
“AI was originally described as a way for manufactured devices to emulate or even exceed the capabilities of humans to perform mental tasks. Today it upholds a similar definition, anchored on enabling machines to think and operates by analysing behaviour to solve problems and make decisions within various situations,” he says. By analysing large amounts of medical data, AI can help clinicians give faster and more accurate treatment to their patients, and can learn from to make better decisions going forward.
“Continuing on the identification of problems, cybersecurity is one of the biggest threats and is an appropriate example where AI can be used as a preventive and predictive way and propose the right solution,” adds Singh.
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