By Rashmi Mabiyan and Abhijeet Singh
New Delhi: The never-ending demand for healthcare services, augmented by the presumption of favorable outcomes, is putting immense pressure on the Indian industry that still operates on traditional models and is expected to increase further, driven by the high burden of disease in the country, combined with an expanding middle-aged population that is more prone to chronic health problems.
To address this looming challenge, experts from various sectors have chosen ‘automation’ as a potential solution, backed by its ability to deliver effective services in significantly less time, thereby helping to balance demand and supply, as well as addressing the shortfall. perineal of talent in the ecosystem.
As a pioneer in this regard at the fourth edition of the Healthcare Leaders Summit, ETHealthworld convened a panel discussion on ‘Is Intelligent Automation the Key for Healthcare CIOs to Achieve Quality Management Maturity?’
The panel comprised Dr. Shafqat Khan, Public Health Officer, Government of J&K; Dr Susil Kumar Meher, Head IT, AIIMS; Arun Goyal, chief information officer, Sir Ganga Ram Hospital; Nitin Chopra, IT Director, Fortis Healthcare, and the discussion was moderated by Rashmi Mabiyan, Chief Correspondent, ETHealthworld.
Speaking in the debate, Dr. Susil Kumar Meher said, “Earlier it was just data and information, but now some new layers have emerged like knowledge and intelligence, where the latter represents AI and by 2029 you will have Super AI. The bottom line in all of this is the data, but if the data is not clean, the model will not work perfectly. Many people have used made-up data or data that has not been properly cleaned. Therefore, the data must be adequately standardized. Furthermore, we do not have any regulator that certifies the models that are emerging today.”
Regarding leveraging data through intelligent automation (AI) for continuous quality improvement, another panelist, Dr. Shafqat Khan, highlighted: “The data generated in our healthcare sector mainly comes from public health sector. The private sector generates considerably less data compared to the public sector. Unfortunately, not enough impetus has been given to the accreditation process and quality assurance system. Public healthcare facilities now understand that quantified and qualitative analysis of machinery is key to generating good data that can eventually help create much better AI models for diagnostics, robotic surgeries, etc. Highlighting the challenges in integrating AI in legacy hospitals Nitin Chopra shared, “In a legacy system, consolidating data from fragmented devices is a big challenge. Secondly, there are vulnerabilities in the legacy system such as lack of support, and then there are challenges in data migration because the existing data is in old formats.”
“Aside from these technical barriers, a key challenge is resistance to adoption of newer applications and data workflows by training staff who are still using older ones,” Chopra added.
Delving into the financial implications of AI adoption, Dr. Meher stated that if AI-based models are used carefully, they can play an important role in reducing patient recovery time and results and therefore influence key performance indicators (KPIs) which can then also improve return on investment (ROI).
Among other emerging trends, Dr Khan highlighted the success of digital platforms like CoWIN and U-WIN, which ensure transparency, accountability and ease of doing business for hospitals, while also helping patients at various stages. of the treatment. He also warned that “AI-based systems will be the future of healthcare, but when they are based on accurate and clean data. Until then, we cannot rely solely on AI systems and doctors must verify them at every stage.”
In conclusion, the fourth edition of the Healthcare Leaders Summit underlined the critical role of intelligent automation (AI) in addressing the challenges of India’s healthcare landscape, particularly in light of the growing demand and constraints of resources.
The panel highlighted that while automation, artificial intelligence and data-driven approaches can greatly improve health efficiency and quality, there are still important challenges. These include data standardizationovercoming technical and operational barriers in legacy systemsand train staff to adopt new workflows.
However, as Dr. Meher and Dr. Khan noted, AI adoption has promising potential to improve provision of medical carereduce patient response times and improve return on investment. However, for AI and AI to reach their full potential, careful data management and ongoing human oversight are essential to ensure these technologies augment, rather than replace, medical expertise.
