12 Jan 2024
While artificial intelligence (AI) is not a novel concept, OpenAI’s introduction of ChatGPT went viral swiftly on social media with users sharing examples on various usages – from travel planning to content production and even coding computer programs. Since then, palpable excitement over AI and its transformative possibilities have steered tech conversations throughout the year, especially within critical sectors such as healthcare.
In 2050, the global population of individuals aged 60 years and above is expected to double to 2.1 billion. People are living longer lives than ever, which in turn has led to a greater demand for healthcare services. To meet greater demands and narrow the gap, AI is used to enable and support Healthcare professionals (HCPs) in providing timely and optimal services.
HCPs around the world are already leveraging AI capabilities across various aspects of the industry, from data analysis and workflow optimisation to decision support and operations management. Their exploration has shown that AI can reduce errors in decision-making as well as improve early disease detection, healthcare productivity and efficiency, and the entire healthcare experience for both patients and HCPs.
This promising track record, coupled with studies that estimate that clinical health AI applications will help the US healthcare economy save USD 150 billion annually by 2026, have encouraged HCPs to make AI integration part of their long-term strategy. Ten of the largest pharmaceutical companies by revenue are actively investing in AI-assisted drug development and design capabilities, and around 98 percent of healthcare leaders in a US survey plan to implement a global AI strategy for their organisation. Among the technologies being tapped for adoption include natural language processing (NLP), robotic process automation (RPA), machine learning (ML) and generative AI (GAI) – signaling the market’s confidence in investing in healthcare AI.
While the healthcare AI sector has not been spared the impact of 2022’s venture capital (VC) funding winter, it did record a lower-than-average decline. The healthcare AI market is also estimated to grow at a compound annual growth rate (CAGR) of 41-45 percent to hit USD 51-85 billion by 2027 – a testament not only to its latent potential, but also its long runway for growth.
One of the primary use cases of AI in healthcare today is to power clinical decision support systems (CDSS) for more accurate patient diagnosis, which can contribute significantly towards improved health outcomes and reduced stress on the healthcare system. Besides the inherent complexity of unclear medical symptoms and the current limitations of physical tests, HCPs also face time constraints and laborious administrative work, leading to higher instances of diagnostic uncertainty.
While AI development in CDSS systems has been driven by power players such as Google, Microsoft and Cerner, many up-and-coming healthtech startups are also launching AI-powered CDSS applications. For instance, the US-based Navina* uses generative AI to integrate and analyze fragmented patient data from EHRs and other sources, producing actionable patient portraits for better outcomes. Recently, it even unveiled a revolutionary generative AI assistant for physicians.
Acceptance of digital health tools, such as online symptom checkers and telemedicine, has skyrocketed among HCPs globally since the COVID-19 pandemic – especially in the US. Online symptom checkers enable remote disease pre-screening for patients, which helps to reduce time taken for a diagnosis. Meanwhile, telemedicine can support remote diagnosis and remotely monitor patient vital signals in real time, which improves diagnosis accuracy, treatment effectiveness and care.
Many cutting-edge platforms and devices built by healthtech players have featured AI-powered online symptom checkers and telehealth. Mayo Clinic’s API offers a symptom checker with over 11,500 content pieces and more than 500 symptoms, while the Apple Watch has shown promise in detecting a type of heart failure. Digital health platforms like Datos Health* and Speedoc*, aims to transform how healthcare services can be provided to consumers.
It is known that a healthcare career is high-stakes and can be highly demanding, but HCPs also face other unseen challenges that hamper performance. High administrative burdens, low patient data privacy, suboptimal hospital patient and resource flow, and diversions in the drug supply chain all contribute to inefficiencies in healthcare delivery. With AI’s proven capacity to automate and optimise workflow, there is significant opportunity for it to help HCPs streamline business processes, reduce administrative errors and boost productivity.
Some examples of AI use cases include:
Globally, healthcare stakeholders are already reaping the benefits of leveraging AI for workflow automation and optimisation. In India, Max Healthcare halved their claim processing turnaround time by adopting an RPA platform, while Anthem in the US saved tens of millions of dollars from reduced waste, fraud and abuse by applying ML in RCM to develop behavioural models of their providers and members. As both interest and demand grow for AI, startups such as Nintex* (previously known as Kryon), a process management and automation software provider, are constantly innovating to improve workflow automation solutions for healthcare to reduce costs, save time and improve patient outcomes.
The application of AI in healthcare is expected to reduce healthcare spending by up to 10 percent, as well as yielding annual savings of up to USD 360B for the US alone. On an operational level, it can also improve the healthcare experience for patients and HCPs. It empowers HCPs to focus on mission-critical, patient-centric work such as care, diagnosis and treatment, while supporting patients by enabling more productive encounters and optimised treatments for better health outcomes.
Despite these benefits, resistance remains towards mainstream AI adoption in healthcare due to a lack of trust. In a GE HealthCare study, only 42% of clinicians believed that AI data could be trusted – and 44% indicated it was subject to built-in bias. Another study on patient perceptions on human-AI interaction in healthcare revealed that patients with acute or chronic conditions still favoured human HCPs; their main concerns revolved around AI trustworthiness, the lack of human relations, and the transparency of AI regulatory standards to assess AI tools.
AI has demonstrated that it can improve HCP efficiency and productivity, which could potentially be the difference between life and death. However, it is also evident that AI is still some time away from being able to fulfil the intrinsic and equally important patient need for trust, empathy and care. The successful long-term integration of AI into healthcare lies in finding the right balance between technology and the human touch. Achieving this goal will be essential to meeting the long-term healthcare demands of the global population.
* Navina, Datos Health, Speedoc and Ninetex are Vertex’s portfolio companies.
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