How GenAI can tackle healthcare’s biggest challenges

In October 2022, the Singapore government projected that the country would require 24,000 more nurses by 2030. This is in light of its rapidly aging population – by 2030, one in four Singaporeans are expected to be over 65 years old.

Concurrently, the island-nation has also become a leader in the adoption of artificial intelligence (AI) and smart health systems – many of which will better help healthcare staff carry out their tasks. The adoption of AI will help alleviate some of the increasing demand for healthcare staff by automating administrative tasks and allowing clinicians to operate faster.

IBM Consulting’s Global Healthcare & Life Sciences Industry Leader, Andrew Cohen, shared how generative AI (GenAI) is a critical tool that Singapore’s public health system can tap on to better meet challenges and support healthcare professionals in their tasks.

Cohen oversees global trends in the healthcare and life sciences industries at IBM and offers leaders support on how they can use technology in their organisations to drive better patient outcomes, affordability, and accessibility.

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Alleviating labour shortage

Of the many emerging use-cases that GenAI offers within healthcare, Cohen pointed to its ability to mitigate the effects of the labour shortage, increase patient and clinician engagement, speed up processes, and help address the challenges of the ageing population.

“Healthcare organisations really need to think differently about how they deliver healthcare to their citizens, in order to better meet the objectives of the ‘quadruple aim’ in healthcare,” he said.

GenAI differs from traditional AI in that it can consume, digest, and summarise complex information for recipients – such as patients, clinicians, and programmers – and help them make better decisions quickly, he noted.

In practice, this means GenAI can enhance patient experience, support more efficient and effective care delivery, and streamline administrative processes for staff – reducing their workload and enabling them to do more, resulting in better outcomes.

For instance, patients could more easily search for personalised healthcare information via chatbots powered by large-language models (LLMs), without needing to talk to a customer service agent, he explained.

Similarly, doctors can use GenAI tools to write clinical notes and case summaries, create intelligent workflows to speed up their processes, and quickly search for policy information to ensure they are applying the right policy at the right time, he noted.

Unlike rules-based chatbots of yesteryears, LLM-powered chatbots can answer a wider range of questions and their responses can be validated against internal documents.

GenAI can also “complement” traditional AI systems, which doctors more often use for predictive and prescriptive capabilities, he said.

Setting the right foundations

However, it is critical that organisations first adopt the right data and AI strategy so that they can reap the benefits in a patient-centric, secure way, Cohen noted.

“As more medical information is put into electronic medical systems, people want to be able to use that data in different ways… How do we start to use that information and combine it with other information?” he said. These could include cost data, data around social determinants of health, and other forms of clinical and non-clinical data.

About 97 per cent of hospital data goes unused today, so there is an opportunity to tap on such data to improve patient experiences, create new value, and improve access to health care, he added.

First, leaders should identify where they are along the “AI Innovation Continuum” and take stock of their current landscape. Is the data still siloed? Have they already begun using AI? Are they already carrying out GenAI pilots?

“Organisations have to assess where they are today, where they want to go, and how fast they want to get there.” This will help in the development of an executable roadmap that aligns with the broader strategy.

Next, it is important to build the business case to support the adoption of new tech and ensure that the business team and tech teams are aligned. Drawing on his experience, Cohen said that he frequently sees situations where the business side may not be ready to adopt new tech.

According to recent study of 3,000 CEOs across 30 countries, 61 per cent of respondents said they are pushing GenAI adoption more quickly than what some people are comfortable with.

Leaders can do this by starting small and identifying a few priority use cases to develop and scale pilot solutions – “it's never too early to get started and to start experimenting with these types of tools.” The important thing to consider is where the most value will be derived so that priorities can be established, budgets created, and a plan can be executed.

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Principles-driven AI strategy

Leaders can define a clear enterprise data and AI strategy by locating and connecting all sources of data; identifying the right AI models to deploy, be it private or open models; and then tapping on GenAI as a competitive advantage, he shared.

Many have already begun deploying AI in targeted ways to address domain-specific problems – and the final stage is to deploy GenAI to transform care delivery across the organisation, he says.

This can look like enhanced human experiences, reduced operational costs, streamlined administrative processes, and improved collaboration between stakeholders.

Finally, when beginning on an AI journey, leaders should ensure they ground their approach within a strong set of principles to enable “value-based adoption,” he shared.

This means that leaders should consider how such technologies may change the behaviour of clinicians, consumers, and patients, and ensuring that such technologies help achieve better decision making and better outcomes.

AI adoption should also be grounded in governance principles such as privacy-by-design, trustworthiness, transparency, explainable, and auditable, he says.

“Nobody can do this alone, so it’s going to be critical to pick the right partners, build the right strategy, execute against a roadmap that’s realistic, and then making sure they’ve got the right people, processes, and technology to make it successful.”

Andrew Cohen will be sharing more of his perspectives on GenAI in Singapore’s public health ecosystem at GovInsider Live Healthcare Day 2024 on 17 September, along with a panel of esteemed public health tech leaders. Register here to save a seat!