Healthcare Analytics: Emergency Preparedness for Covid-19

The first COVID-19 case in the US was reported on January 20, 2020. As the first cases were being reported in the US, Washington State became a reliable source not just for hospital bed demand based on incidence and community spread but also for modeling the impact of skilled nursing facilities and assisted living facilities on hospital bed demand. Various hospital bed demand modeling efforts began in earnest across the United States in university settings, private consulting and health systems. Nationally, the University of Washington Institute of Health Metrics and Evaluation seemed to gain a footing and was adopted as a source for many states for its ability to predict the epidemiological curve by state, including the peak.

This book therefore addresses a compelling need for documenting what has been learned by the academic and professional healthcare communities in healthcare analytics and disaster preparedness to this point in the pandemic. What is clear, at least from the US perspective, is that the healthcare system was unprepared and uncoordinated from an analytics perspective. Learning from this experience will only better prepare all healthcare systems and leaders for future crisis.

Both prospectively, from a modeling perspective and retrospectively from a root cause analysis perspective, analytics provide clarity and help explain causation and data relationships. A more structured approach to teaching healthcare analytics to students, using the pandemic and the rich dataset that has been developed, provides a ready-made case study from which to learn and inform disaster planning and preparedness. The pandemic has strained the healthcare and public health systems. Researchers and practitioners must learn from this crisis to better prepare our processes for future pandemics, at minimum. Finally, government officials and policy makers can use this data to decide how best to assist the healthcare and public health systems in crisis.

美國於2020年1月20日報告了第一例COVID-19病例。隨著美國開始報告首批病例，華盛頓州不僅成為醫院床位需求（基於發病率和社區傳播）的可靠來源，還成為對醫院床位需求進行建模的技術議題，特別是對技術議題中的護理院和助養院對醫院床位需求的影響進行建模。在美國，各大學、私人咨詢公司和醫療系統開始積極進行醫院床位需求建模工作。在全國范圍內，華盛頓大學健康指標與評估研究所似乎取得了一席之地，被許多州採用，因為它能夠預測各州的流行病曲線，包括高峰期。

因此，本書旨在記錄學術界和專業醫療社區在醫療分析和災難應對方面在疫情爆發至今所學到的知識，以滿足迫切需求。至少從美國的角度來看，顯然醫療系統在分析方面毫無準備和協調。從這次經驗中學習只會更好地為所有醫療系統和領導者準備未來的危機。

從建模角度來看，無論是前瞻性還是從根本原因分析的角度來看，分析都提供了清晰度，幫助解釋因果關系和數據關系。使用疫情和已開發的豐富數據集來更有結構地教授醫療分析給學生，提供了一個現成的案例研究，可以從中學習並為災難計劃和應對提供信息。疫情對醫療和公共衛生系統造成了壓力。研究人員和從業人員必須從這次危機中學習，以更好地為未來的疫情做好準備，至少如此。最后，政府官員和政策制定者可以利用這些數據來決定如何最好地協助醫療和公共衛生系統應對危機。

Edward M. Rafalski is Adjunct Assistant Professor at University of Illinois School of Public Health, and an Affiliate Associate Professor at the University of South Florida, College of Public Health. He holds qualifications from the University of Chicago and Yale University School of Public Health.

Edward M. Rafalski是伊利諾伊大學公共衛生學院的兼職助理教授，也是南佛羅里達大學公共衛生學院的聯合副教授。他擁有芝加哥大學和耶魯大學公共衛生學院的學歷。