Zhang Haipeng, Dimitrov Dimitar, Simpson Lynn, Plaks Nina, Singh Balaji, Penney Stephen, Charles Jo, Sheehan Rosemary, Flammini Steven, Murphy Shawn, Landman Adam
Digital Innovation Hub, Brigham and Women's Hospital, Boston, MA, United States.
Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, United States.
JMIR Form Res. 2020 Oct 8;4(10):e19533. doi: 10.2196/19533.
As of July 17, 2020, the COVID-19 pandemic has affected over 14 million people worldwide, with over 3.68 million cases in the United States. As the number of COVID-19 cases increased in Massachusetts, the Massachusetts Department of Public Health mandated that all health care workers be screened for symptoms daily prior to entering any hospital or health care facility. We rapidly created a digital COVID-19 symptom screening tool to enable this screening for a large, academic, integrated health care delivery system, Partners HealthCare, in Boston, Massachusetts.
The aim of this study is to describe the design and development of the COVID Pass COVID-19 symptom screening application and report aggregate usage data from the first three months of its use across the organization.
Using agile principles, we designed, tested, and implemented a solution over the span of one week using progressively customized development approaches as the requirements and use case become more solidified. We developed the minimum viable product (MVP) of a mobile-responsive, web-based, self-service application using research electronic data capture (REDCap). For employees without access to a computer or mobile device to use the self-service application, we established a manual process where in-person, socially distanced screeners asked employees entering the site if they have symptoms and then manually recorded the responses in an Office 365 Form. A custom .NET Framework application solution was developed as COVID Pass was scaled. We collected log data from the .NET application, REDCap, and Microsoft Office 365 from the first three months of enterprise deployment (March 30 to June 30, 2020). Aggregate descriptive statistics, including overall employee attestations by day and site, employee attestations by application method (COVID Pass automatic screening vs manual screening), employee attestations by time of day, and percentage of employees reporting COVID-19 symptoms, were obtained.
We rapidly created the MVP and gradually deployed it across the hospitals in our organization. By the end of the first week, the screening application was being used by over 25,000 employees each weekday. After three months, 2,169,406 attestations were recorded with COVID Pass. Over this period, 1865/160,159 employees (1.2%) reported positive symptoms. 1,976,379 of the 2,169,406 attestations (91.1%) were generated from the self-service screening application. The remainder were generated either from manual attestation processes (174,865/2,169,406, 8.1%) or COVID Pass kiosks (25,133/2,169,406, 1.2%). Hospital staff continued to work 24 hours per day, with staff attestations peaking around shift changes between 7 and 8 AM, 2 and 3 PM, 4 and 6 PM, and 11 PM and midnight.
Using rapid, agile development, we quickly created and deployed a dedicated employee attestation application that gained widespread adoption and use within our health system. Further, we identified 1865 symptomatic employees who otherwise may have come to work, potentially putting others at risk. We share the story of our implementation, lessons learned, and source code (via GitHub) for other institutions who may want to implement similar solutions.
截至2020年7月17日,新型冠状病毒肺炎(COVID-19)大流行已影响全球超过1400万人,美国有超过368万例病例。随着马萨诸塞州COVID-19病例数的增加,马萨诸塞州公共卫生部规定,所有医护人员在进入任何医院或医疗保健机构之前,每天都要进行症状筛查。我们迅速创建了一个数字化的COVID-19症状筛查工具,以便为马萨诸塞州波士顿的一个大型学术性综合医疗服务系统——合作伙伴医疗保健公司进行这种筛查。
本研究的目的是描述COVID Pass COVID-19症状筛查应用程序的设计与开发,并报告其在整个组织使用的前三个月的汇总使用数据。
我们运用敏捷原则,在一周的时间内设计、测试并实施了一个解决方案,随着需求和用例逐渐明确,采用逐步定制的开发方法。我们使用研究电子数据采集(REDCap)开发了一个基于网络的、响应式移动自助服务应用程序的最小可行产品(MVP)。对于无法使用计算机或移动设备来使用自助服务应用程序的员工,我们建立了一个人工流程,即现场筛查人员在保持社交距离的情况下询问进入场所的员工是否有症状,然后在Office 365表单中手动记录回答。随着COVID Pass的扩展,开发了一个自定义的.NET框架应用程序解决方案。我们收集了企业部署前三个月(2020年3月30日至6月30日)来自.NET应用程序、REDCap和Microsoft Office 365的日志数据。获得了汇总描述性统计数据,包括按日期和地点统计的员工总体证明、按应用方法(COVID Pass自动筛查与人工筛查)统计的员工证明、按一天中的时间统计的员工证明,以及报告有COVID-19症状的员工百分比。
我们迅速创建了MVP,并逐渐在我们组织的各医院中进行部署。到第一周结束时,每个工作日有超过25000名员工使用该筛查应用程序。三个月后,COVID Pass记录了2169406次证明。在此期间,1865/160159名员工(1.2%)报告有阳性症状。在2169406次证明中,1976379次(91.1%)是通过自助筛查应用程序生成的。其余的是通过人工证明流程(174865/2169406,8.1%)或COVID Pass信息亭(25133/2169406,1.2%)生成的。医院工作人员继续每天24小时工作,员工证明在上午7点至8点、下午2点至3点、下午4点至6点以及晚上11点至午夜的轮班交接时间左右达到峰值。
通过快速、敏捷的开发,我们迅速创建并部署了一个专门的员工证明应用程序,该应用程序在我们的医疗系统中得到了广泛采用和使用。此外,我们识别出1865名有症状的员工,否则他们可能会来上班,这可能会使其他人面临风险。我们分享我们的实施情况、经验教训以及源代码(通过GitHub),供其他可能希望实施类似解决方案的机构参考。