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Moving Towards Patient-centered Health with Generative AI: World Economic Forum’s white paper
Moving Towards Patient-centered Health with Generative AI: World Economic Forum’s white paper

February 27, 2025

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Introduction:

The COVID-19 pandemic exacerbated healthcare challenges such as workforce shortages and growing health disparities, which compels the world to move towards patient-centered Health with Generative AI. Noticeably, the WHO estimates the global shortage of healthcare workers stands at 15 million today and is expected to decline to 10 million by 2030. All the current global healthcare threats increase the dependency on various forms of Artificial Intelligence to meet these challenges. As the world healthcare consumers expect personalized and accessible healthcare facilities, Generative AI holds great potential to transform healthcare into convenient and transparent health systems. The white paper published by the World Economic Forum outlines the generative AI’s potential to revolutionise patient care experiences and focuses on key findings, global trends, opportunities, challenges, and recommendations for stakeholders. 

Generative AI Use Case categories: 

Industry leaders believe Gen AI can be applied in diverse ways in healthcare. Gen AI has the potential to assist providers in making informed decisions and actions by offering intuitive, human-like conversations with patients and caretakers. The use cases of Gen AI in the healthcare setup can be broadly categorised as, 

  • Productivity Boosters: Automation of administrative tasks like transcribing visits, drafting emails, and summarizing clinical studies to reduce the burden on healthcare providers. Enhance data management by extracting insights from unstructured data.
  • Insights Generators: Real-time data analysis of structured and unstructured datasets to aid healthcare providers in decision-making.
  • Action Drivers: Enhanced patient interactions through intuitive, human-like conversations better than traditional chatbots as they fail to convert insights into action.

 

Increasing patient engagement with Gen AI:

Traditional healthcare systems lack patient engagement without a human-in-loop, connected ecosystem and technical limitations. Authors of the report claiming that Generative AI can make it better. Gen AI can increase patient engagement, influence and gather insights to transform the entire patient health journey.

  • Health Education Assistance: A survey found that 94% of U.S. healthcare consumers use the internet for health education, yet most online resources fail to meet quality standards, exacerbating issues like low health literacy and limited access, particularly in underserved communities and LMICs. Generative AI, particularly LLMs trained on high-quality health data, could bridge these gaps by delivering reliable, accessible, and culturally relevant health information in multiple languages, improving preventive care and decision-making. This innovation has the potential to reduce significant economic costs associated with low health literacy and empower both healthcare consumers and caregivers globally.
  • Co-pilots for patient triage: The COVID-19 pandemic worsened global healthcare provider shortages, leading to increased disparities, rising costs, and declining patient experiences. Early triage solutions using chatbots have shown potential to address care navigation gaps but struggle with scalability and cultural nuances. Generative AI, with domain-specific and culturally diverse training, can help stratify patients, improve care routing, and automate patient intake, alleviating administrative burdens and enhancing access to healthcare.
  • Disease management interventions: Managing chronic illness hinges on treatment adherence, yet up to 60% of patients fail to follow prescribed regimens within the first year, costing healthcare systems billions annually. While traditional algorithms predict non-adherence, generative AI can create personalized interventions, enhancing disease management for both providers and biopharma. Additionally, generative AI can streamline administrative tasks and integrate data streams, such as genetics and biometrics, to deliver tailored treatment plans and personalized care paths.
     

Barriers to adopting effective patient-safe Generative AI:

  • Trust and Mistrust: Building trust in AI outputs remains a significant barrier, with concerns over accuracy and bias in AI-generated information.
  • Data Limitations: There are ongoing issues related to the quality and comprehensiveness of data used to train generative AI systems, particularly in diverse cultural contexts.
  • Scalability: Implementing generative AI solutions effectively across varied regions and healthcare systems poses logistical and financial challenges.
     

Recommendations for Stakeholders to Bring Generative AI into Reality:

  • Build Trust through Empathy: Healthcare stakeholders should prioritize the development of empathetic AI models, ensuring they are tested and refined based on real-world interactions.
  • Mitigate Bias: Organizations must work to create a more interconnected data ecosystem to address biases inherent in AI training datasets.
  • Keep Humans in the Loop: Implementing processes that involve healthcare professionals in reviewing AI recommendations can help ensure safety and accuracy.
  • Plan for Scalable Solutions: Developing flexible, context-sensitive generative AI applications is essential for addressing the unique needs of different healthcare environments

 

Real-World Generative AI Examples

  • Ada Health: Utilizes generative AI for a digital symptom checker that outperforms traditional diagnostic methods.
  • K Health and Cedars-Sinai: Provides patients with direct access to healthcare professionals through a virtual care platform, enhancing patient intake and reducing administrative burdens.
  • University of Rochester Medical Center: Built a model with ChatGPT-4 and Microsoft Azure to appropriately triage messages to doctors, nurses or staff members. The model is in the testing phase and has continuously proven reliable and accurate.
  • Mayo Clinic: Leads the world with innovative multimodal generative AI models to quickly and accurately predict Rheumatoid Arthritis treatment effectiveness with individual patient’s genetic profiles.
  • Amazon and Hurone AI: Healthcare US-based start-up focussed on overcoming oncologists’ shortage in Low- and Middle- Income Countries (LMIC) with generative AI offering cancer-related insights to patients and doctors, which saved 75% time of doctors on specific tasks.
  • Microsoft and Epic Systems: Implement generative AI to respond to patient inquiries, improving efficiency and patient experience.

Conclusion

The potential of generative AI to reshape the patient care experience is vast, offering a pathway to address longstanding healthcare challenges. However, successful implementation will require concerted efforts from all stakeholders to build trust, enhance data quality, and ensure equitable access to these innovations. By embracing these recommendations, the healthcare industry can harness the power of generative AI to foster a more patient-centered approach to care.

 


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