Using Community Detection Techniques to Identify Themes in COVID-19-Related Patient Safety Event Reports

Abstract

Objectives: The COVID-19 pandemic has transformed how healthcare is delivered to patients. As the pandemic progresses and healthcare systems continue to adapt, it is important to understand how these changes in care have changed patient care. This study aims to use community detection techniques to identify and facilitate analysis of themes in patient safety event (PSE) reports to better understand COVID-19 pandemic's impact on patient safety. With this approach, we also seek to understand how community detection techniques can be used to better identify themes and extract information from PSE reports.

Methods: We used community detection techniques to group 2082 PSE reports from January 1, 2020, to January 31, 2021, that mentioned COVID-19 into 65 communities. We then grouped these communities into 8 clinically relevant themes for analysis.

Results: We found the COVID-19 pandemic is associated with the following clinically relevant themes: (1) errors due to new and unknown COVID-19 protocols/workflows; (2) COVID-19 patients developing pressure ulcers; (3) unsuccessful/incomplete COVID-19 testing; (4) inadequate isolation of COVID-19 patients; (5) inappropriate/inadequate care for COVID-19 patients; (6) COVID-19 patient falls; (7) delays or errors communicating COVID-19 test results; and (8) COVID-19 patients developing venous thromboembolism.

Conclusions: Our study begins the long process of understanding new challenges created by the pandemic and highlights how machine learning methods can be used to understand these and similar challenges. Using community detection techniques to analyze PSE reports and identify themes within them can help give healthcare systems the necessary information to improve patient safety and the quality of care they deliver.