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Since OpenAI’s official launch of ChatGPT, we have seen a sharp increase in the healthcare industry’s adoption of AI tools across different use cases, from improving diagnostic precision and personalising treatment plans to streamlining administrative processes. Several military hospitals in Asia have begun adopting AI solutions in diagnostics and teleconsulting services.
AI has the potential to transform the healthcare ecosystem, which has historically been a reactive industry where patients are already unwell when they come to seek help and diagnoses. Due to the amount of specialisation required before doctors can identify illnesses and suggest treatment plans, clinics and hospitals are chronically understaffed, leaving patients waiting for a very long time before they can see a medical provider who can answer their questions and concerns.
Here is where we see AI coming in and revolutionising the way clinics and hospitals work: while it cannot replace doctors, it can significantly improve wait times for patients and take on the leg work needed to evaluate patient data and determine the exact illness a patient has, factors that acerbate it, and treatment options that they can leverage. With 50% of healthcare providers in the Asia Pacific region looking to invest in generative AI applications, the future of healthcare and AI are inextricably linked. However, we must understand how to adapt to emerging technology now to ensure we are using it to its fullest potential while avoiding any speedbumps in the future.
AI for patient groups
There has been a rise in the number of patient groups leveraging this technology to drive awareness, treatment, and help in pain/illness management. Part of the appeal is AI’s ability to offer tailored health management tools like predictive analytics for disease progression and personalised treatment recommendations based on genetic information. AI is able to engage holistically with a variety of data points available from the patient’s history and community during the diagnostic process or providing treatment options.
With AI’s data collection capabilities, patients can actively engage with the technology by using wearable devices and health apps. Not only can this help in tracking their conditions and making it easier to use telehealth services, it can also provide healthcare workers with accurate, valid data for future diagnoses and provide an analysis of environmental factors that could have contributed to the health conditions.
For example, pools of standing water are breeding grounds for mosquitos and improper water storage practices have been associated with the transmission of the dengue virus. By analysing a variety of data points from a population with a sudden surge in dengue cases, we have seen that AI has the potential to not only diagnose the illness but also recommend community or social solutions to reduce the transmission or reappearance of the virus. More often than not, solutions are fairly easy to implement, allowing more resources to be freed for the treatment of more severe and chronic illnesses. By some estimates, genAI is expected to contribute to around $100 billion in healthcare savings in APAC as it frees up 10% of clinicians’ time by streamlining operational flows and allowing for the time to be reallocated toward other patients who require more medical oversight.
The future of AI in healthcare
We have already seen how AI has been used in medical settings: The Fred Hutchinson Cancer Center’s use of Natural Language Processing (NLP) to match patients with clinical cancer studies exemplifies AI’s potential to revolutionise patient care and research. Additionally, AI applications in managing kidney disease at the Renal Research Institute demonstrate how AI can improve disease management through advanced diagnostics and predictive analytics, showcasing AI’s impact across various medical fields and patient groups.
Patient groups are an incredibly vital cog in this emerging AI-powered healthcare machine. AI applications and platforms run smoothly and accurately thanks to access to anonymised patient medical information and data. By opting to contribute their health data (with appropriate privacy protections), patients can help refine AI models, leading to improved diagnostic tools and treatments. AI-driven platforms can also enable patient groups to access specialised support and resources, enhancing their ability to manage chronic conditions and navigate their health journeys more effectively.
Forums and platforms where AI-driven insights are shared help us see the future of AI in healthcare and how it will help foster a community of informed patients and give rise to the possibility of community-driven support for patients. Emerging AI trends include the use of NLP for improved patient communication and education, machine learning models for predictive health analytics, and AI-enhanced remote monitoring for chronic disease management.
Technologies like ChatGPT could improve patient education and support, offering personalised, interactive guidance, and information. These advancements promise to make healthcare more proactive, personalised, and accessible for patient groups.
Addressing barriers to access and other concerns
However, there are a handful of barriers that prevent complete utilisation of the technology such as accessibility, digital literacy, privacy concerns, and scepticism about the technology’s effectiveness. But healthcare providers can work to overcome these by doubling down on the use of the technology to disseminate accurate, AI-related healthcare information, debunk myths, and share patient success stories involving AI technologies. Engaging with patient groups on AI advancements and how it supports work in clinics and hospitals can also educate people further on the benefits of the technology. Collaborating with patient influencers and advocacy groups on social media can also extend the reach and impact of these efforts.
Bringing patient groups and the healthcare community together to share use cases, learnings, and knowledge is key. For example, The Alliance & Partnerships for Patient Innovation & Solutions (APPIS) platform brings patient communities and key stakeholders in the healthcare ecosystem together to prioritise action towards addressing barriers to access for patients in the region. At our upcoming APPIS Summit 2024 on 19-20 March, which focuses on the key themes of Health Literacy, Health Policy Shaping, and Future Readiness, I will be leading the Future Readiness theme alongside fellow APPIS 2024 Council Members Dilek Ural, professor at the Department of Cardiology in Koc University, Türkiye, and journalist Nam Soohyoun of Korea JoongAng Daily. At the APPIS Summit, there will be five dedicated sessions that will take a deeper look into leveraging AI and digitalisation to address barriers to healthcare and foster healthier communities.
Digital tools like AI-powered diagnostic systems, personalised health monitoring apps, and telehealth services are poised to significantly impact patient outcomes. Healthcare organisations must also do their part in adapting to the changing landscape of health tech by training medical staff to integrate these technologies into their operational workflow and prioritising staying up to date on developments in the field. Creating a culture of continuous learning within healthcare organisations encourages the adoption of new technologies and ensures that professionals are equipped to integrate these advancements into patient care effectively.
Looking ahead
Patient advocacy groups have historically had a lot of influence on how patients view medical treatments. Their relationship with chronic health conditions in particular has allowed them to become voices for change within the healthcare ecosystem – be it through raising awareness about conditions or working with hospitals to encourage preventative care like regular cancer screenings. With AI and other technological developments, these patient advocacy groups have access to more resources than ever to build their credibility and disseminate accurate information about various conditions.
Access to data and information can also be a game changer when it comes to advocating for more financial or government support for rare diseases or genetic conditions. With estimated healthcare savings from genAI in the billions, there is a good case to be made for that money to be reallocated to either R&D or increasing access to treatment options among the population. By utilising predictive analytics, patient groups can champion their causes with data-driven models that efficiently display the long-term effects of reallocating funds in their respective communities.
Moving forward, healthcare organisations must consider ethical aspects such as data privacy, consent, bias mitigation, and transparency in AI implementation. Ensuring responsible use involves conducting thorough impact assessments, involving patients and patient groups in the development and evaluation processes, and establishing clear guidelines for data use and AI interactions. Building trust through transparency and patient engagement ensures that AI technologies are implemented in a way that respects patient rights and promotes equitable access to healthcare advancements. It also creates pathways for patient groups to be more involved in the development and evaluation of AI tools to create accessible, effective, and relevant solutions for their specific needs and conditions. Education on the ethical use of AI for both healthcare professionals and patients is crucial, as is the establishment of oversight mechanisms to monitor AI applications and their effects on patient care.
By addressing these questions comprehensively, focusing on the specific impacts and considerations for patient groups in the healthcare ecosystem, we can appreciate the nuanced role of AI in enhancing patient care, the challenges that come with its adoption, and the strategies needed to navigate this evolving landscape responsibly and effectively.
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Dr Adam Chee is an Associate Professor at Saw Swee Hock School of Public Health, National University of Singapore, and a member of the Alliance & Partnerships for Patient Innovation & Solutions (APPIS) 2024 Council. He is a convergence scientist skilled in healthcare, informatics, innovation, technologies, and business, and has extensive experience in strategy consulting, technology advisory, data-driven system design, and solution implementation across Asia Pacific and the Middle East.
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