This report summarizes the discussions and conclusions of a 2-day multidisciplinary workshop that brought together researchers and practitioners in healthcare, computer science, and social sciences to explore what lessons were learned and what actions, primarily in research, could be taken. One consistent observation was that there is significant merit in thinking not only about pandemic situations, but also about peacetime advances, as many healthcare networks and communities are now in a perpetual state of crisis. Attendees discussed how the COVID-19 pandemic amplified gaps in our health and computing systems, and how current and future computing technologies could fill these gaps and improve the trajectory of the next pandemic. Three major computing themes emerged from the workshop: models, data, and infrastructure. Computational models are extremely important during pandemics, from anticipating supply needs of hospitals, to determining the care capacity of hospital and social service providers, to projecting the spread of the disease. Accurate, reliable models can save lives, and inform community leaders on policy decisions. Health system users require accurate, reliable data to achieve success when applying models. This requires data and measurement standardization across health care organizations, modernizing the data infrastructure, and methods for ensuring data remains private while shared for model development, validation, and application. Finally, many health care systems lack the data, compute, and communication infrastructures required to build models on their data, use those models in ordinary operations, or even to reliably access their data. Robust and timely computing research has the potential to better support healthcare works to save lives in times of crisis (e.g., pandemics) and today during relative peacetime.

翻译：本报告总结了一场为期两天的多学科研讨会的讨论与结论，该研讨会汇聚了医疗、计算机科学和社会科学领域的研究人员与实践者，共同探讨了在新冠疫情中汲取的教训以及可采取的研究行动。一个贯穿始终的共识是：不仅需要关注疫情情境，也应重视和平时期的进步，因为当前许多医疗网络和社区正长期处于危机状态。与会者讨论了新冠疫情如何放大了医疗与计算系统的缺陷，以及当前及未来的计算技术如何填补这些缺陷并改善下一次大流行的应对轨迹。研讨会归纳了三大计算主题：模型、数据与基础设施。计算模型在疫情期间至关重要，从预测医院物资需求、评估医院与社会服务机构的照护能力，到预估疾病传播轨迹。准确可靠的模型不仅能拯救生命，还能为社区决策者提供政策依据。医疗系统用户在应用模型时需要精确可靠的数据。这要求跨医疗机构统一数据与测量标准，推进数据基础设施现代化，并开发既能保障数据隐私又能支持模型开发、验证和应用的方法。最后，许多医疗系统缺乏构建模型所需的数据、计算和通信基础设施，无法在日常运营中使用这些模型，甚至难以稳定访问自身数据。稳健且及时的计算研究有望在危机时期（如大流行）及当前相对和平的时期，更好地支持医护人员拯救生命。