Artificial intelligence (AI) is remaking the way we interact, consume information, and obtain goods and services across industries. In healthcare, AI is changing the patient experience, how clinicians practice medicine, and how the pharmaceutical industry operates. Throwing light on this Dr K Hari Prasad, President – Hospitals Division, Apollo Hospitals Enterprises Limited interacted with Kaanchi Chawla of Elets News Network (ENN). Edited excerpts:

How has AI transformed healthcare delivery? What are some of the notable use cases that you recall?

Healthcare is one of the major success stories of our times. Medical science has improved rapidly, raising life expectancy around the world. But, as longevity increases, healthcare systems face a growing demand for their services, rising costs, and a workforce that is struggling to meet the needs of its patients.

Artificial intelligence (AI) is transforming the way we interact, consume information, and obtain goods and services across industries. In health care, AI is already changing the patient experience, how clinicians practice medicine, and how the pharmaceutical industry operates. The journey has just begun.

As AI finds its way into everything from our smartphones to the supply chain, applications in health care fall into three broad groupings:

• Patient-oriented AI
• Clinician-oriented AI
• Administrative- and operational-oriented AI

The future of AI in health care could include tasks that range from simple to complex—everything from answering the phone to medical record review, population health trending and analytics, therapeutic drug and device design, reading radiology images, making clinical diagnoses and treatment plans, and even talking with patients.

What according to you could be the growing potential of AI-based tools for healthcare delivery?

Early detection – AI is already being used to detect diseases, such as cancer, more accurately and in their early stages. According to the American Cancer Society, a high proportion of mammograms yield false results, leading to one in two healthy women being told they have cancer. Using AI enables the review and translation of mammograms 30 times faster with 99 per cent accuracy, reducing the need for unnecessary biopsies.

Diagnosis – The technology combines machine learning and systems neuroscience to build powerful general-purpose learning algorithms into neural networks that mimic the human brain.

Decision Making – Improving care requires the alignment of big health data with appropriate and timely decisions, and predictive analytics can support clinical decision- making and actions as well as prioritise administrative tasks.

Treatment – Robots have been used in medicine for more than 30 years. They range from simple laboratory robots to highly complex surgical robots that can either aid a human surgeon or execute operations by themselves. In addition to surgery, they’re used in hospitals and labs for repetitive tasks, in rehabilitation, physical therapy, and in support of those with long-term conditions.

Research – Drug research and discovery are one of the more recent applications for AI in healthcare. By directing the latest advances in AI to streamline the drug discovery and drug repurposing processes, there is the potential to significantly cut both the time to market for new drugs and their costs.

Training – AI allows those in training to go through naturalistic simulations in a way that simple computer-driven algorithms cannot. The advent of natural speech and the ability of an AI computer to draw instantly on a large database of scenarios means the response to questions, decisions, or advice from a trainee can challenge in a way that a human cannot. And the training programme can learn from previous responses from the trainee, meaning that the challenges can be continually adjusted to meet their learning needs.

The growing digital footprint and technology trends have enhanced healthcare systems, but there still lies the need for human manpower. Where are the gaps in curating healthcare data?

In the healthcare industry, various sources of big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of the Internet of Things (IoT). Biomedical research also generates a significant part of big data relevant to public healthcare. This data requires proper management and analysis to derive meaningful information. Otherwise, seeking a solution by analysing big data quickly becomes comparable to finding a needle in the haystack.

As technological advancements continue on a rapid basis, healthcare providers move towards more digital care options to enhance their treatment and patient experience. Your thoughts.

When the pandemic hit, most hospital systems and providers had simple implementations of telemedicine technology and multiyear roadmaps, expecting years to perfect their strategy.

The pandemic moved the “digital” part of care delivery from future possibility into the immediate core and forced digital health adoption.

Most providers made the change in a matter of weeks. Fast-tracking multi-year strategies often meant prioritising convenience over the patient’s authentic experience.

Fast forward to today, the pandemic accelerated a focus on digital health massively and irrevocably. Technology is not simply a functional tool, but a driver of the patient experience.

We believe the best path forward is simple:

• First, leaders must acknowledge that patient experience has shifted from a marketing concern to a strategic imperative.
• Then, leaders should make strategic technology decisions primarily through the lens of the patient experience.
• When done correctly, a better patient experience not only leads to improved patient satisfaction scores, but, more importantly, better patient outcomes, engagement, and loyalty.

Where do you see healthcare 5 years down the line?

Developments in the five following categories will impact the field of medicine:

• Big data, biometrics, and the Internet of Things
• Machine Learning, AI, and Advanced Analytics
• Climate change and environmental health hazards
• Internals, robotics, nanorobotics, and bionics
• Genetic engineering and bioprinting

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