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Time of India
3 days ago
- Health
- Time of India
Narayana Health Develops AI Model to Predict Heart Function from ECG Images
Bengaluru: Narayana Health's Clinical Research team, in collaboration with its Advanced Analytics and AI division, Medha AI , has developed an artificial intelligence (AI) model which is indicated to be capable of predicting left ventricular ejection fraction (EF) from standard ECG images in near real-time. The tool is designed with the aim to support early identification of heart failure and expand diagnostic access, particularly in resource-limited settings. According to the company, the AI model leverages widely available ECG machines and cloud-based analytics to address limited access to echocardiography (ECHO) especially in rural areas. It is indicated that the model is trained on over 100,000 ECG images paired with ECHO reports. According to the company, the model has shown strong performance in external validation across 14 tertiary centres, covering more than 57,000 patients. It identified 97% of individuals with severely reduced EF (≤35%) and flagged them an average of 58 days before ECHO was eventually ordered. The tool has been integrated into Narayana Health's in-house Electronic Medical Record system, Athma, and is undergoing validation at key locations including the Narayana Institute of Cardiac Sciences, Mazumdar Shaw Medical Center, select clinics in Bengaluru, and the Rabindranath Tagore International Institute of Cardiac Sciences in Kolkata. Designed to deliver results in near real-time, the model aims to aid triage and care planning without disrupting clinical workflows. It is also indicated that future development is underway to expand its capabilities to detect other echocardiographic abnormalities and early signs of coronary artery disease. The organization plans to deploy the model via its mobile medical units, targeting underserved regions as part of its broader digital public health strategy. Regulatory submissions and efforts to enable open-access distribution are also in progress. The model has been presented at several international conferences including the Heart Rhythm Society Conference (Boston), APHRS (Sydney), IHRSCON (Kolkata), and HRX (Atlanta). It has also been published in the Indian Heart Journal and received the BMJ South Asia Award for Digital Innovation.
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Business Standard
5 days ago
- Business
- Business Standard
Narayana Health to add 2,000 beds, expand insurance schemes in FY26
Bengaluru-headquartered hospital chain Narayana Health aims to grow its domestic revenue by 10 per cent in FY26, in line with industry benchmarks, a senior company official said. Managing director and chief executive officer Emmanuel Rupert told Business Standard: 'Last fiscal we did about Rs 4,349 crore in India revenue and we want to grow at a rate at which the healthcare industry is growing — over 10 per cent. We look to be on the same momentum and revenue.' Sandhya J, chief financial officer at Devi Shetty's Narayana Health, said the company plans to scale up its Aditi and Arya health insurance schemes in Karnataka and West Bengal. 'The entire healthcare ecosystem is failing in trust. We are on the path to solve the trust deficit and episodic interference of fund transfer. On the back of this, we aim to double down on our insurance scheme and build a strong customer base in Bengaluru, Shivamogga, Mysore, and Kolkata.' Commenting on efforts to improve profit after tax (PAT), which declined 2.3 per cent to Rs 196 crore in Q1 due to increased investments in integrated care and other financial costs, Sandhya said PAT optimisation will improve as the India business grows. Some clinics have started breaking even, reducing losses from the health insurance schemes. 'Over a period of time, we will be able to keep the losses in check and expand margins in the domestic business. PAT should improve. Finance cost will continue to increase due to the Rs 3,000 crore project to add 2,000 beds across India over the next three years. The integrated care dilution will continue over the next year but we hope to maintain a decent level of cash burn.' She added that a large part of the funding will come through bank borrowings and non-convertible debentures (NCDs) as needed. So far, Rs 800 crore has been raised for projects already signed. The CFO noted that about Rs 1,500–2,000 crore in loans might be raised in this fiscal, depending on upcoming project developments. 'We are exploring opportunities to expand, some might be inorganic as well. Borrowings will be linked to the construction progress of our current projects.' During the quarter, revenue from international patients declined, primarily due to reduced footfall from Bangladesh. Narayana expects this trend to continue, given ongoing geopolitical tensions. 'The goal is to keep focusing on domestic growth and see how well we can push the numbers. Eventually, in the next two to three years, we believe that revenue contribution from Bangladesh will go down to zero. Not to miss, the country is building its own healthcare infrastructure.' Looking ahead, Narayana expects its integrated care business to account for a larger share of overall revenue. At present, cardiac sciences and oncology contribute approximately 33 per cent and 16 per cent, respectively. On Tuesday, the Clinical Research team and Medha AI developed an artificial intelligence (AI) model that predicts left ventricular ejection fraction from ECG images, enabling early heart failure detection and improved diagnostic access. 'All along we have waited for Western countries to develop these technologies. Today we are developing this because this country is blessed with phenomenally skilled, passionate software engineers and AI experts. This is our strength. We have made massive investments in building the digital platform, which is far ahead of all the other hospitals. With the great combination of a digital platform and the AI tool, healthcare will never be the same,' said Devi Prasad Shetty, chairman and founder of Narayana Health, at a media briefing. The company said that trained on over 100,000 ECG images paired with echocardiogram reports, the model showed strong predictive performance. With external validation across 14 tertiary centres and over 57,000 patients, it identified 97 per cent of individuals with severely reduced ejection fraction (≤35 per cent).
