The healthcare industry is well suited to leveraging GenAI to improve efficiency and quality of care: Nitin Chopra, Head of IT, Fortis Healthcare

GenAI is revolutionising healthcare by optimising tasks traditionally reliant on manual effort and expertise. Through machine learning algorithms, GenAI streamlines processes like diagnostics, treatment planning, and data analysis, enhancing both efficiency and accuracy. Its applications extend to personalised medicine, where it tailors interventions to individual patient needs. Moreover, GenAI facilitates telemedicine and remote monitoring, promoting accessibility and patient engagement. Nitin Chopra, Head of IT, Fortis Healthcare, elaborated on these advancements in an exclusive interview with Express Computer.

Here are the edited excerpts:

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?

Technology plays an ever-important role now in any organisation by becoming a game changer to meet the dynamically changing business requirements and healthcare is no different. We all have witnessed how technology played a vital role during Covid-19 period, enabling quality medical support to even the remotest part of the country via tele-consultation and further also helping to expedite the roll-out of the vaccines by significantly reducing the clinical trial timelines.

Some of the very important technology use cases for healthcare where AI would have maximum impact would be –
•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 tools can help in the early detection of diseases, such as cancer through image analysis and data interpretation.
•Robotic surgery: Surgical robots, guided by AI, enhance the precision and capabilities of surgeons during minimally invasive procedures.
•Data analytics: Healthcare organisations are using big data analytics to gain insights from patient records, optimise resource allocation, and improve healthcare delivery as well as outcomes.
•Chatbots and virtual health assistants: AI-driven chatbots and virtual assistants provide patients with health-related information, appointment scheduling, medication reminders, and support for non-emergency medical queries.
•Health monitoring and wearables: AI-powered wearables and remote monitoring devices can track vital signs and patient data, enabling real-time health monitoring and early intervention in case of anomalies.

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?

The development and application of AI technology in healthcare has to be guided by the values and principle of ethics adhered and practiced by all relevant stakeholders. Ensuring patient privacy and security when utilising AI in healthcare settings is critical to leveraging the full potential of the technology while minimising risks. Adopting a proactive, multi-layered approach to minimising the security risks of using AI in healthcare is critical to maintaining patient privacy. Protecting patient data is a legal requirement and a fundamental ethical principle to build and maintain trust in the healthcare system.

Here are some key ways to enhance patient privacy and security in AI applications in healthcare-
•Implementing robust data encryption protocols for data at rest and during transmission.
•Ensuring compliance with regulations or applicable local laws and standards.
•Secure data storage in a secure on-premise and cloud environment.
•Due diligence before entering into partnerships with third-party entities.
•Strong data security contracts with third-party vendors.
•Data minimisation that limits the amount of patient data shared with vendors.
•Strong access controls such as role-based permissions and two-factor authentication limiting access to patient data.
•Maintenance of audit logs that record access to patient data and regular reviews investigating unauthorised or suspicious activity.
•Regular vulnerability testing to identify and address potential weaknesses in IT infrastructure and AI systems.
•Training and awareness programs for healthcare professionals and staff on data security best practices, the responsible use of AI, and the importance of patient privacy.
•Development of an incident response plan to address potential data breaches or security incidents.

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

The healthcare industry, with its various manual, knowledge-heavy, and text-focused tasks, is especially well suited to leveraging GenAI to improve efficiency and quality of care. In the coming year, healthcare’s digital transformation will likely be marked by a heightened focus on personalised medicine, enabled by advanced analytics and artificial intelligence (AI). These technologies will streamline diagnosis and treatment planning, leading to more tailored approaches for individual patients.

Moreover, telemedicine and remote monitoring will continue to gain traction, offering patients greater accessibility to healthcare services while reducing the strain on traditional care facilities. Patient satisfaction and engagement by improving the quality of care and clinical outcomes with the help of digital technology will be of utmost importance. We will see continued momentum around outpatient/home health/care-anywhere models.

Advancements in wearable technology and health-tracking apps will empower patients to take a more proactive role in managing their health, contributing to improved overall outcomes. Furthermore, collaborative efforts between healthcare providers and tech innovators will drive the development of innovative solutions, such as AI-generated imaging and computer-aided design (CAD) tools, enhancing the efficiency and effectiveness of medical product development processes.