GenAI’s interaction in healthcare holds the promise of greatly improving services across the ecosystem: Srinivasan Raman, Asst. GM- IT, Kokilaben Dhirubhai Ambani Hospital

During an exclusive interaction with Express Computer, Srinivasan Raman, Assistant General Manager- Information Technology, Kokilaben Dhirubhai Ambani Hospital, highlighted the hospital’s utilisation of AI and data analytics to enhance patient care and operational efficiency. He emphasised predictive analytics, AI-driven clinical decision support, and GenAI’s potential in the healthcare ecosystem. Raman also stressed the importance of addressing ethical concerns in AI deployment. Looking forward, he discussed trends like precision diagnostics, Emotion AI, immunomics, and AI’s role in drug discovery and chronic disease management.

Here are the edited excerpts:

1. The healthcare sector is undergoing a significant digital transformation. How are you leveraging technologies, including AI and data analytics, to enhance patient care, operational efficiency, and overall healthcare outcomes?

Kokilaben Dhirubhai Ambani Hospitals and Research Centre uses Artificial Intelligence and data analytics to enhance patient care, operational efficiency and overall healthcare outcomes.

Predictive analytics for early intervention:

By analysing large volumes of patient data, including electronic health records (EHRs), and genomic information, we use AI algorithms to identify patterns and trends indicative of potential health risks and complications. We use these data for predictive analytics, enabling us to intervene earlier, prevent adverse events, and ultimately improve patient outcomes.

Clinical decision-making:

AI-driven decision support systems play a crucial role in improving patient safety and outcomes. Our system analyses patient data, medical literature, and best practices to assist clinicians, including myself in making informed decisions about diagnosis, treatment, and care management.

2. How do you foresee GenAI improving healthcare services as it embraces digital transformation, and in what particular areas of the healthcare ecosystem do you see GenAI having a major impact?

As a healthcare professional, I firmly believe that GenAI holds tremendous potential to revolutionise healthcare services across various aspects of the healthcare ecosystem. In my experience, I have witnessed firsthand the impact it can have in several key areas, to name a few:

Machine learning and predictive analytics: 

AI can analyse vast amounts of patient data to predict disease outbreaks, identify high-risk patients, and improve treatment plans.

Natural language processing (NLP): 

NLP can be used for analysing unstructured clinical notes and converting them into structured data for research and decision support.

Diagnostic AI: 

AI-driven diagnostic systems that use image analysis and data interpretation can aid in the early detection of diseases like cancer.

Robotic surgery: 

AI-guided surgical robots improve the accuracy and skills of doctors doing minimally invasive treatments.

Chatbots and virtual health assistants: 

AI-powered chatbots and virtual assistants offer patients non-emergency medical help, appointment scheduling, medication reminders, and health-related information.

Health monitoring and wearables: 

Wearables and remote monitoring devices with AI capabilities can follow patient data and vital signs, allowing for real-time health monitoring and prompt intervention in the event of abnormalities.

Telehealth: 

GenAI-powered remote monitoring devices and telehealth platforms enable continuous monitoring of patient vital signs, medication adherence, and overall health status outside traditional healthcare settings. This allows for early detection of health issues, timely intervention, and improved patient management, particularly for individuals with chronic conditions or limited access to healthcare services.

Drug discovery and development: 

GenAI accelerates the drug discovery and development process by analysing vast amounts of biological data, identifying potential drug targets, and predicting the efficacy and safety of patients.

3. With the increasing use of AI in healthcare, addressing ethical concerns is crucial. What safeguards ensure transparency and patient trust, and how do you ensure the ethical application of GenAI in healthcare?

Ethical layers of AI:

In my opinion, AI has become an essential component in various sectors, notably healthcare, finance, transportation, and entertainment. As AI systems become increasingly sophisticated, it’s imperative to discuss the ethical implications of this technology. Exploring critical ethical issues like privacy, algorithmic bias, the impact of automation on jobs, and the nuances of AI in healthcare.

a. Privacy in the AI Era 

AI’s prevalence raises concerns about personal data privacy. Sensitive personal data is needed for applications like digital assistants, algorithms for electronic medical records, and technology for recognising patterns in large amounts of data.

Key areas in privacy protection include:

– Transparent data collection

– Robust data storage

-Responsible data utilisation

b. Tackling bias in AI algorithms 

I’ve noticed a significant concern surrounding AI’s propensity to reflect biases present in its training data, potentially leading to biased outcomes. This issue holds particular gravity within the healthcare sector, where biased algorithms could exacerbate existing health disparities by impacting diagnosis and treatment decisions. It underscores the critical importance of addressing bias in AI algorithms to ensure fair and equitable healthcare delivery for all individuals, regardless of demographic factors.

Strategies to address bias include:

-Expanding data diversity

-Promoting transparency

-Conducting bias audits

c. Automation’s ripple effect on employment 

AI-driven automation enhances productivity but may displace jobs. In the healthcare sector, this transition has the potential to reshuffle roles and responsibilities, prompting a crucial emphasis on reskilling initiatives. Ensuring healthcare professionals are equipped with the necessary skills to adapt to evolving technologies is important to navigating this transformation while maintaining high standards of patient care.

Key considerations include:

– Reskilling initiatives

-Reinforced social support

-Encouraging AI-enhanced roles

4. As the healthcare sector undergoes digital transformation, what notable trends do you foresee shaping the approach to patient care and operational efficiency in the coming years?

To answer this question, I foresee notable trends in AI that are poised to revolutionise the healthcare sector, such as:

Precision diagnostics:

AI’s role in precision diagnostics is growing, with applications in real-time prioritisation, triage, and the automated classification of medical images.

Emotion AI:

Emotion AI and speech analysis are emerging tools for enhancing telemedicine and diagnosing mental health issues and developmental disorders.

Immunomics:

AI’s application in immunomics promises personalised preventive strategies and a deeper understanding of disease at the cellular level.

Drug discovery:

Integration of GenAI is accelerating drug discovery by analysing biological data to identify drug targets and predict drug effectiveness and safety.

Chronic diseases:

In my view, AI technology plays a crucial role in diagnosing chronic diseases, and providing personalised treatment plans grounded in thorough data analysis.