Transforming Healthcare Access: Empowering Chinese Patients Through Generative AI Across City Tiers and Age Groups

Author(s)

Li A1, Woo A2
1Cerner Enviza, Shanghai, Shanghai, China, 2Oracle Life Sciences, Singapore, 05, Singapore

OBJECTIVES: China's healthcare landscape, characterized by its vast population and diverse demographics, presents unique challenges and opportunities in ensuring equitable access to medical information and services. This abstract highlighted the transformative role of generative artificial intelligence (AI) in revolutionizing patient access to healthcare across different city tiers and age groups in China.

METHODS: The primary research for this study utilized data from the 2023 update of the DLP survey, a long-term annual tracking survey developed by Cerner Enviza China since 2012. The patient component of the study involved analyzing responses from a diverse sample of 2,000 patients (diagnosed with respiratory conditions, cardiovascular malformations, diabetes, osteoporosis, autoimmune diseases, emotional or mental conditions, infectious diseases, or cancer) representing 200 cities. Descriptive statistics (distribution frequencies for categorical variables, means, standard deviations, medians, and ranges) were calculated for variables.

RESULTS: The results indicated that a significant portion of patients, 42%, had utilized generative AI to access medical information and services focusing on disease knowledge, disease initial assessment, and evaluating disease risk or high-risk factors. Specifically, 88% of patients exhibited high levels of trust in the information provided by generative AI, particularly among individuals from lower city tiers and the younger generation. Furthermore, 53% of patients had interacted with Chatbots during their online consultations. However, patient satisfaction with Chatbot services remained relatively low, with only 48% reporting satisfaction. Additionally, 38% of patients preferred switching to human customer service when encountering issues with Chatbots. Furthermore, 47% of patients lacked experience with Chatbots, leading to lower expectations for their utility.

CONCLUSIONS: These findings highlighted the potential and challenges associated with incorporating generative AI and Chatbots in healthcare interactions among Chinese patients. Addressing the Chatbots competence and enhancing patient satisfaction would be crucial in improving the overall patient experience and maximizing the benefits of AI-driven solutions in healthcare.

Conference/Value in Health Info

2024-11, ISPOR Europe 2024, Barcelona, Spain

Value in Health, Volume 27, Issue 12, S2 (December 2024)

Code

HSD112

Topic

Medical Technologies, Methodological & Statistical Research, Patient-Centered Research

Topic Subcategory

Patient Behavior and Incentives, Survey Methods

Disease

Cardiovascular Disorders (including MI, Stroke, Circulatory), Diabetes/Endocrine/Metabolic Disorders (including obesity), Infectious Disease (non-vaccine), Mental Health (including addition), Oncology

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