A novel tool for patient data management in the ICU—Ensuring timely and accurate vital data exchange among ICU team members. (December 2020)

• Record Type: Journal Article
• Authors: Newman, Noah; Gilman, Sam; Burdumy, Matt; Yimen, Mekeleya; Lattouf, Omar
• Is Part Of: International journal of medical informatics. Volume 144(2020)
• Publication Date: 2020-12
• Subjects: Electronic medical record -- Intensive care; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104291
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: COVID-19 displaced many healthcare providers to intensive care units to meet the demand of incoming COVID-19 patients. The infrastructure and IT support costs needed to establish EMRs are barriers to underserved regions adopting EMR technology. Inexpensive implementation of this tool may allow for better patient care and data collection in certain regions. Users can manage patient information electronically with less data overload and a more intuitive user experience. Abstract: 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.