Objective

The coronavirus pandemic has highlighted the need to simplify data collection for critically-ill patients, particularly for physicians relocated to the ICU setting. Herein we present a simple, reproducible, and highly-customizable manual-entry tool to track ICU patients using new HIPAA-compliant Google Big Query technology for parsing large datasets. This innovative flow chart is useful and could be modified to serve the particular needs of different sub-specialists, particularly those that either rely heavily on hand-written notes or experience poor electronic medical record (EMR) penetration.

Methods

The tool was developed using a combination of three Google Enterprise features: Google Forms for data input, Google Sheets for data output, and Google Big Query for data parsing. Code was written in SQL. Sheets functions were used to transpose and filter parsed data. White and black box tests were performed to examine functionality.

Results

Our tool was successfully able to collect and output fictional patient data across all 57 data points specified by the intensivists and surgeons of Cardiovascular Department of Mt. Sinai Morningside Hospital.

Conclusion

The functional tests performed demonstrate use of the tool. Though originally conceived to simplify patient data collection for newly relocated physicians to the ICU, our tool also overcomes financial and technological barriers previously described in low-income countries that could dramatically improve patient care and provide data to power future studies in these regions. With the original code provided, implementers may adapt our tool to best meet the requirements of their clinical setting and protocols during this very challenging time.

中文翻译：

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ICU中用于患者数据管理的新颖工具-确保ICU团队成员之间及时准确地交换重要数据

目的

冠状病毒大流行凸显了简化重症患者尤其是重症监护病房的医生简化数据收集的需求。本文中，我们介绍了一种简单，可重现且高度可定制的手动输入工具，该工具可使用符合HIPAA的新Google大查询技术来解析大型数据集，以跟踪ICU患者。此创新流程图非常有用，可以修改以满足不同专科医生的特殊需求，尤其是那些严重依赖手写笔记或经验不佳的电子病历（EMR）渗透的专家。

方法

该工具是结合三个Google Enterprise功能开发的：用于数据输入的Google表单，用于数据输出的Google表格和用于数据解析的Google Big Query。代码是用SQL编写的。Sheets函数用于转置和过滤已解析的数据。进行了白盒和黑盒测试以检查功能。

结果

我们的工具能够成功地收集和输出虚构的患者数据，这些数据来自Mt.心血管病专科医师和外科医生指定的所有57个数据点。西奈早晨医院。

结论

进行的功能测试证明了该工具的使用。尽管最初旨在简化为新近安置到ICU的医生的患者数据收集工作，但我们的工具还克服了先前在低收入国家/地区中描述的财务和技术障碍，这些障碍可能会大大改善患者护理并为这些地区的未来研究提供数据。借助提供的原始代码，实施人员可以在非常困难的时期内调整我们的工具，使其最佳地满足其临床环境和协议的要求。