Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, United States.
Stanford University, Palo Alto, CA, United States.
J Med Internet Res. 2023 Nov 23;25:e49314. doi: 10.2196/49314.
Missingness in health care data poses significant challenges in the development and implementation of artificial intelligence (AI) and machine learning solutions. Identifying and addressing these challenges is critical to ensuring the continued growth and accuracy of these models as well as their equitable and effective use in health care settings.
This study aims to explore the challenges, opportunities, and potential solutions related to missingness in health care data for AI applications through the conduct of a digital conference and thematic analysis of conference proceedings.
A digital conference was held in September 2022, attracting 861 registered participants, with 164 (19%) attending the live event. The conference featured presentations and panel discussions by experts in AI, machine learning, and health care. Transcripts of the event were analyzed using the stepwise framework of Braun and Clark to identify key themes related to missingness in health care data.
Three principal themes-data quality and bias, human input in model development, and trust and privacy-emerged from the analysis. Topics included the accuracy of predictive models, lack of inclusion of underrepresented communities, partnership with physicians and other populations, challenges with sensitive health care data, and fostering trust with patients and the health care community.
Addressing the challenges of data quality, human input, and trust is vital when devising and using machine learning algorithms in health care. Recommendations include expanding data collection efforts to reduce gaps and biases, involving medical professionals in the development and implementation of AI models, and developing clear ethical guidelines to safeguard patient privacy. Further research and ongoing discussions are needed to ensure these conclusions remain relevant as health care and AI continue to evolve.
医疗保健数据中的缺失对人工智能（AI）和机器学习解决方案的开发和实施带来了重大挑战。识别和解决这些挑战对于确保这些模型的持续增长和准确性以及在医疗保健环境中公平有效地使用这些模型至关重要。
本研究旨在通过举办数字会议并对会议记录进行主题分析，探讨与 AI 应用相关的医疗保健数据缺失问题的挑战、机遇和潜在解决方案。
2022 年 9 月举办了一次数字会议，吸引了 861 名注册参与者，其中 164 名（19%）参加了现场活动。会议邀请了 AI、机器学习和医疗保健领域的专家进行演讲和小组讨论。使用 Braun 和 Clark 的逐步框架分析会议记录，以确定与医疗保健数据缺失相关的关键主题。
从分析中得出了三个主要主题-数据质量和偏差、模型开发中的人为投入以及信任和隐私。主题包括预测模型的准确性、代表性不足的社区缺乏包容性、与医生和其他人群的合作、敏感医疗保健数据的挑战以及与患者和医疗保健社区建立信任。
在医疗保健中设计和使用机器学习算法时，解决数据质量、人为投入和信任问题至关重要。建议包括扩大数据收集工作以减少差距和偏差，让医疗专业人员参与 AI 模型的开发和实施，并制定明确的道德准则以保护患者隐私。需要进一步研究和持续讨论，以确保这些结论在医疗保健和 AI 不断发展的情况下仍然相关。
