Li Zhang1, Sascha Domröse, Andreas Mahnken. 1. Department of Diagnostic and Interventional Radiology, University Hospital Giessen and Marburg, Philipps University of Marburg, Baldinger Strasse, 35033, Marburg, Germany, zhang@med.uni-marburg.de.
Abstract
OBJECTIVES: To provide a method to calculate delay cost and examine the relationship between quality and total cost. METHODS: The total cost including capacity, supply and delay cost for running an interventional radiology suite was calculated. The capacity cost, consisting of labour, lease and overhead costs, was derived based on expenses per unit time. The supply cost was calculated according to actual procedural material use. The delay cost and marginal delay cost derived from queueing models was calculated based on waiting times of inpatients for their procedures. RESULTS: Quality improvement increased patient safety and maintained the outcome. The average daily delay costs were reduced from 1275 € to 294 €, and marginal delay costs from approximately 2000 € to 500 €, respectively. The one-time annual cost saved from the transfer of surgical to radiological procedures was approximately 130,500 €. The yearly delay cost saved was approximately 150,000 €. With increased revenue of 10,000 € in project phase 2, the yearly total cost saved was approximately 290,000 €. Optimal daily capacity of 4.2 procedures was determined. CONCLUSIONS: An approach for calculating delay cost toward optimal capacity allocation was presented. An overall quality improvement was achieved at reduced costs. KEY POINTS: • Improving quality in terms of safety, outcome, efficiency and timeliness reduces cost. • Mismatch of demand and capacity is detrimental to quality and cost. • Full system utilization with random demand results in long waiting periods and increased cost.
OBJECTIVES: To provide a method to calculate delay cost and examine the relationship between quality and total cost. METHODS: The total cost including capacity, supply and delay cost for running an interventional radiology suite was calculated. The capacity cost, consisting of labour, lease and overhead costs, was derived based on expenses per unit time. The supply cost was calculated according to actual procedural material use. The delay cost and marginal delay cost derived from queueing models was calculated based on waiting times of inpatients for their procedures. RESULTS: Quality improvement increased patient safety and maintained the outcome. The average daily delay costs were reduced from 1275 € to 294 €, and marginal delay costs from approximately 2000 € to 500 €, respectively. The one-time annual cost saved from the transfer of surgical to radiological procedures was approximately 130,500 €. The yearly delay cost saved was approximately 150,000 €. With increased revenue of 10,000 € in project phase 2, the yearly total cost saved was approximately 290,000 €. Optimal daily capacity of 4.2 procedures was determined. CONCLUSIONS: An approach for calculating delay cost toward optimal capacity allocation was presented. An overall quality improvement was achieved at reduced costs. KEY POINTS: • Improving quality in terms of safety, outcome, efficiency and timeliness reduces cost. • Mismatch of demand and capacity is detrimental to quality and cost. • Full system utilization with random demand results in long waiting periods and increased cost.