This study aims to demonstrate how Big Data can be leveraged to enhance administrative and clinical decision-making in healthcare delivery in Quality 4.0.
Using the Nationwide Readmissions Database (NRD), this study applies Statistical Process Control (SPC) to monitor the length of stay (LOS) for Total Hip Arthroplasty (THA) and Total Knee Arthroplasty (TKA) patients over time.
I-MR and EWMA charts identify statistically out-of-control LOS values, suggesting potential variations in treatment and care delivery. These charts facilitate root cause analysis to uncover key causes of these out-of-control points.
This study advances the current body of knowledge by applying SPC methodologies to a national healthcare dataset and highlights the importance of stratifying patient groups to reduce false alarms in control charts, providing a methodological framework that can be expanded to other clinical conditions.
The findings underscore the value of integrating SPC tools into healthcare performance monitoring initiatives. By harnessing Big Data, healthcare systems can continuously monitor THA/TKA outcomes, enabling clinicians and administrators to detect and address special causes of variation. The integration of SPC and Big Data supports the broader digital transformation in healthcare, shifting organizations from reactive care to proactive, data-driven decision-making through real-time data analysis and predictive modeling in Quality 4.0 and Healthcare 4.0 eras.
As Industry 4.0 and Quality 4.0 reshape industries through digital technologies, this study exemplifies how Big Data analytics can empower healthcare decision-makers in the era of Healthcare 4.0 and Quality 4.0.
