Start Date
11-12-2016 12:00 AM
Description
Length of Stay (LOS) is an important metric of care quality and efficiency in hospitals that has been studied for decades. Longer stays lead to increased costs and higher burdens on patients, caregivers, clinicians and facilities. Understanding characteristics of LOS outliers is important for developing actionable steps to address LOS reduction. Our study examines clustering of inpatients using key clinical and demographic attributes to identify LOS outliers and investigates the opportunity to reduce their LOS by comparing order sequences with similar non-outliers in the same cluster. Learning from retrospective data, we develop a mathematical model and a two-stage heuristic algorithm. Results indicate that switching orders in homogeneous inpatient sub-populations within the limits of clinical guidelines may be a promising decision support strategy for LOS management. These novel data-driven insights can be offered as suggestions for clinicians to apply new evidence-based, clinical guideline-compliant opportunities for LOS reduction through healthcare analytics.
Recommended Citation
Gartner, Daniel and Padman, Rema, "Mathematical Modelling and Cluster Analysis in Healthcare Analytics - The Case of Length of Stay Management" (2016). ICIS 2016 Proceedings. 11.
https://aisel.aisnet.org/icis2016/DataScience/Presentations/11
Mathematical Modelling and Cluster Analysis in Healthcare Analytics - The Case of Length of Stay Management
Length of Stay (LOS) is an important metric of care quality and efficiency in hospitals that has been studied for decades. Longer stays lead to increased costs and higher burdens on patients, caregivers, clinicians and facilities. Understanding characteristics of LOS outliers is important for developing actionable steps to address LOS reduction. Our study examines clustering of inpatients using key clinical and demographic attributes to identify LOS outliers and investigates the opportunity to reduce their LOS by comparing order sequences with similar non-outliers in the same cluster. Learning from retrospective data, we develop a mathematical model and a two-stage heuristic algorithm. Results indicate that switching orders in homogeneous inpatient sub-populations within the limits of clinical guidelines may be a promising decision support strategy for LOS management. These novel data-driven insights can be offered as suggestions for clinicians to apply new evidence-based, clinical guideline-compliant opportunities for LOS reduction through healthcare analytics.