BACKGROUND: Quality improvement is central to Infection Prevention and Control (IPC) programs. Challenges may occur when applying quality improvement methodologies like process control charts, often due to the limited exposure of typical IPs. Because of this, our team created an open-source database with a process control chart generator for IPC programs. The objectives of this report are to outline the development of the application and demonstrate application using simulated data. METHODS: We used Research Electronic Data Capture (REDCap Consortium, Vanderbilt University, Nashville, TN), R (R Foundation for Statistical Computing, Vienna, Austria), and R Studio Shiny (R Foundation for Statistical Computing) to create an open source data collection system with automated process control chart generation. We used simulated data to test and visualize both in-control and out-of-control processes for commonly used metrics in IPC programs. RESULTS: The R code for implementing the control charts and Shiny application can be found on our Web site (https://github.com/ul-research-support/spcapp). Screen captures of the workflow and simulated data indicating both common cause and special cause variation are provided. CONCLUSIONS: Process control charts can be easily developed based on individual facility needs using freely available software. Through providing our work free to all interested parties, we hope that others will be able to harness the power and ease of use of the application for improving the quality of care and patient safety in their facilities.
BACKGROUND: Quality improvement is central to Infection Prevention and Control (IPC) programs. Challenges may occur when applying quality improvement methodologies like process control charts, often due to the limited exposure of typical IPs. Because of this, our team created an open-source database with a process control chart generator for IPC programs. The objectives of this report are to outline the development of the application and demonstrate application using simulated data. METHODS: We used Research Electronic Data Capture (REDCap Consortium, Vanderbilt University, Nashville, TN), R (R Foundation for Statistical Computing, Vienna, Austria), and R Studio Shiny (R Foundation for Statistical Computing) to create an open source data collection system with automated process control chart generation. We used simulated data to test and visualize both in-control and out-of-control processes for commonly used metrics in IPC programs. RESULTS: The R code for implementing the control charts and Shiny application can be found on our Web site (https://github.com/ul-research-support/spcapp). Screen captures of the workflow and simulated data indicating both common cause and special cause variation are provided. CONCLUSIONS: Process control charts can be easily developed based on individual facility needs using freely available software. Through providing our work free to all interested parties, we hope that others will be able to harness the power and ease of use of the application for improving the quality of care and patient safety in their facilities.
Authors: Salah Haridy; Ahmed Maged; Arthur W Baker; Mohammad Shamsuzzaman; Hamdi Bashir; Min Xie Journal: Comput Ind Eng Date: 2021-03-16 Impact factor: 5.431