Joshi Divya, Jalali Ali, Whipple Todd, Rehman Mohamed, Ahumada Luis M
All Children's Specialty Physicians, Johns Hopkins All Children's Hospital, St. Petersburg, Florida, USA.
Core of Predictive Analytics, Johns Hopkins All Children's Hospital, St. Petersburg, Florida, USA.
JAMIA Open. 2021 Apr 28;4(2):ooab016. doi: 10.1093/jamiaopen/ooab016. eCollection 2021 Apr.
To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic.
Using data from 27 866 cases (May 1 2018-May 1 2020) stored in the Johns Hopkins All Children's data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs.
The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios.
Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can use this tool to overcome patient backlog safely.
开发一种预测分析工具，以帮助评估在2019冠状病毒病大流行期间外科患者积压病例清除的不同情况和多个变量。
利用存储在约翰·霍普金斯全儿童医院数据仓库中的27866例病例（2018年5月1日至2020年5月1日）的数据以及30个基于手术的变量的输入，我们建立了以下数学模型：（1）清除病例积压的时间；（2）个人防护装备（PPE）的使用情况；（3）加班需求评估。
该工具使我们能够预测所需变量，包括清除患者积压病例所需的天数、所需的个人防护装备、所需的工作人员/加班时间以及不同积压病例减少方案的成本。
预测分析、机器学习以及多个变量输入，再加上灵活的情景创建和用户友好的可视化，帮助我们确定手术室人员的最有效部署。全球的手术室都可以使用此工具来安全地克服患者积压问题。
