Since OpenAI’s official launch of ChatGPT, we have seen a sharp increase in the adoption of AI tools in healthcare across a variety of use cases, from improving diagnostic precision and personalizing treatment plans to streamlining administrative processes. Several military hospitals in Asia have started adopting AI solutions in diagnostics and teleconsultation services.
AI has the potential to transform the healthcare ecosystem, which has traditionally been a reactive industry where patients already feel unwell when they come seeking help and diagnoses. Because of the amount of specialization required before doctors can identify diseases and suggest treatment plans, clinics and hospitals are chronically understaffed, causing patients to wait very long periods of time before they can see a medical provider who can answer their questions and concerns.
This is where we see AI coming in and revolutionizing the way clinics and hospitals operate: while it cannot replace doctors, it can significantly improve patient waiting times and take on the work needed to evaluate patient data and pinpoint the exact disease of a patient. factors that make this worse, and treatment options they can take advantage of. Of 50% of healthcare providers in the Asia Pacific region that wants to invest in generative AI applications, the future of healthcare and AI are inextricably linked. However, we need to understand how we can adapt to emerging technology now to ensure we can take full advantage of it while avoiding speed bumps in the future.
AI for patient groups
There has been an increase in the number of patient groups using this technology to promote awareness, treatment and assistance with pain/disease management. Part of the appeal is AI’s ability to provide customized health management tools, such as predictive analytics for disease progression and personalized treatment recommendations based on genetic information. AI can holistically handle 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 interact with the technology using wearable devices and health apps. Not only can this help track their conditions and make it easier to use telehealth services, it can also provide health professionals with accurate, valid data for future diagnoses and provide analysis of environmental factors that could have contributed to the health problems.
For example, pools of standing water are breeding grounds for mosquitoes, and improper water storage practices have been linked to the transmission of the dengue virus. By analyzing a variety of data points from a population with a sudden increase in dengue cases, we saw that AI has the potential to not only diagnose the disease but also recommend community or social solutions to reduce transmission or reduce the re-emergence of the virus. More often than not, solutions are quite easy to implement, freeing up more resources to treat more serious and chronic diseases. By some estimates, genAI is expected to contribute to this approximately $100 billion in health care savings in APAC because it frees up 10% of physicians’ time by streamlining operational flows and allowing time to be reallocated to other patients who need more medical supervision.
The future of AI in healthcare
We’ve already seen AI being used in medical settings: the Fred Hutchinson Cancer Center’s use of Natural Language Processing (NLP) to match patients with cancer clinical trials is an example of AI’s potential to revolutionize in 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 the impact of AI on various medical areas and patient groups.
Patient groups are an incredibly vital cog in this emerging AI-powered healthcare machine. AI applications and platforms work smoothly and accurately thanks to access to anonymized medical information and patient data. By choosing to contribute their health data (with appropriate privacy protections), patients can help refine AI models, leading to improved diagnostic tools and treatments. AI-powered platforms can also enable patient groups to access specialist support and resources, increasing their ability to manage chronic conditions and manage their health journey 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-enabled remote monitoring for chronic disease management.
Technologies such as ChatGPT can improve patient education and support by providing personalized, interactive guidance and information. These developments promise to make healthcare more proactive, personal and accessible to patient groups.
Addressing access barriers and other issues
However, there are a handful of barriers to full use of the technology, such as accessibility, digital literacy, privacy concerns, and skepticism about the technology’s effectiveness. But healthcare providers can work to overcome them by doubling down on the technology to disseminate accurate AI-related healthcare information, debunk myths, and share patient success stories with AI technologies. Engaging with patient groups to discuss advances in AI and how it supports work in clinics and hospitals can also further educate people about the benefits of the technology. Partnering with patient influencers and advocacy groups on social media can also increase the reach and impact of these efforts.
Bringing together patient groups and the healthcare community to share use cases, lessons learned and knowledge is critical. For example, the Alliance & Partnerships for Patient Innovation & Solutions (APPIS) platform brings together patient communities and key stakeholders in the healthcare ecosystem to prioritize action to address patient access barriers in the region. At our upcoming APPIS Summit 2024 on March 19-20, which focuses on the key themes of health literacy, shaping health policy and future preparedness, I will lead the Future Readiness theme together with fellow APPIS 2024 council members Dilek Ural, professor at the Department of Cardiology at Koc University, Türkiye, and journalist Nam Soohyoun from Korea JoongAng Daily. The APPIS Summit will feature five special sessions that will delve deeper into how to leverage AI and digitalization to address barriers to healthcare and promote healthier communities.
Digital tools such as AI-powered diagnostic systems, personalized health monitoring apps, and telehealth services are poised to significantly impact patient outcomes. Healthcare organizations must also do their part to adapt to the changing health technology landscape by training healthcare professionals to integrate these technologies into their operational workflow and prioritize staying abreast of developments in the field. Creating a culture of continuous learning within healthcare organizations encourages the adoption of new technologies and ensures that professionals are equipped to effectively integrate these developments into patient care.
Looking forward
Patient advocacy groups have historically had a lot of influence on the way patients view medical treatments. In particular, their relationship with chronic health conditions has enabled them to become voices for change within the healthcare ecosystem – be it by raising awareness about conditions or partnering with hospitals to encourage preventive care such as regular cancer screenings. Thanks to AI and other technological advances, 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 increased financial or government support for rare diseases or genetic conditions. With estimated healthcare savings from genAI running into the billions, there are good arguments for reallocating that money to R&D or increasing access to treatment options among the population. Using predictive analytics, patient groups can champion their goals with data-driven models that efficiently represent the long-term effects of redistributing funds in their respective communities.
Going forward, healthcare organizations must consider ethical aspects such as data privacy, consent, limiting bias and transparency when implementing AI. 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 patients’ rights and promotes equitable access to healthcare advances. It also creates opportunities 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 circumstances. Education about the ethical use of AI for both healthcare professionals and patients is crucial, as is establishing oversight mechanisms to monitor AI applications and their effects on patient care.
By answering these questions comprehensively, with an emphasis on the specific impact and considerations for patient groups in the healthcare ecosystem, we can understand the nuanced role of AI in improving patient care, the challenges associated with its adoption, and the strategies that necessary to navigate these evolving developments. landscape responsibly and effectively.
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Dr. Adam Chee is an Associate Professor at the 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 with experience in healthcare, informatics, innovation, technologies and business, and has extensive experience in strategy consulting, technology consulting, data-driven system design and solution implementation in the Asia Pacific and Middle East region..