Using Discrete-Event Simulation to Promote Quality Improvement and Efficiency in a Radiation Oncology Treatment Center. Issue 4 (October 2017)

• Record Type: Journal Article
• Authors: Famiglietti, Robin M.; Norboge, Emily C.; Boving, Valentine; Langabeer, James R.; Buchholz, Thomas A.; Mikhail, Osama
• Is Part Of: Quality management in health care. Volume 26:Issue 4(2017)
• Publication Date: 2017-10
• Subjects: discrete-event simulation -- patient throughput -- radiation oncology; Medical care -- Quality control -- Periodicals; Total quality management -- Periodicals; Health services administration -- Periodicals; 362.1068
• DOI: 10.1097/QMH.0000000000000145
• Languages: English
• ISSNs: 1063-8628

Abstract

Abstract : Background: To meet demand for radiation oncology services and ensure patient-centered safe care, management in an academic radiation oncology department initiated quality improvement efforts using discrete-event simulation (DES). Although the long-term goal was testing and deploying solutions, the primary aim at the outset was characterizing and validating a computer simulation model of existing operations to identify targets for improvement. Methods: The adoption and validation of a DES model of processes and procedures affecting patient flow and satisfaction, employee experience, and efficiency were undertaken in 2012-2013. Multiple sources were tapped for data, including direct observation, equipment logs, timekeeping, and electronic health records. Results: During their treatment visits, patients averaged 50.4 minutes in the treatment center, of which 38% was spent in the treatment room. Patients with appointments between 10 AM and 2 PM experienced the longest delays before entering the treatment room, and those in the clinic in the day's first and last hours, the shortest (<5 minutes). Despite staffed for 14.5 hours daily, the clinic registered only 20% of patients after 2:30 PM. Utilization of equipment averaged 58%, and utilization of staff, 56%. Conclusion: The DES modeling quantified operations, identifying evidence-based targets for next-phase remediation and providing data to justify initiatives.