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Benchmarking in pathology: development of an activity-based costing model
Leslie Burnett1, Roger Wilson, Sally Pfeffer
1PaLMS, Pathology North, Royal North Shore Hospital, St Leonards †Sydney Medical School, University of Sydney, Royal North Shore Hospital, St Leonards, NSW, Australia. leslie.burnett@sydney.edu.au
Aims:
Benchmarking in Pathology (BiP) allows pathology laboratories to determine the unit cost of all laboratory tests and procedures, and also provides organisational productivity indices allowing comparisons of performance with other BiP participants. We describe 14 years of progressive enhancement to a BiP program, including the implementation of 'avoidable costs' as the accounting basis for allocation of costs rather than previous approaches using 'total costs'.
Methods:
A hierarchical tree-structured activity-based costing model distributes 'avoidable costs' attributable to the pathology activities component of a pathology laboratory operation. The hierarchical tree model permits costs to be allocated across multiple laboratory sites and organisational structures. This has enabled benchmarking on a number of levels, including test profiles and non-testing related workload activities. The development of methods for dealing with variable cost inputs, allocation of indirect costs using imputation techniques, panels of tests, and blood-bank record keeping, have been successfully integrated into the costing model.
Results:
A variety of laboratory management reports are produced, including the 'cost per test' of each pathology 'test' output. Benchmarking comparisons may be undertaken at any and all of the 'cost per test' and 'cost per Benchmarking Complexity Unit' level, 'discipline/department' (sub-specialty) level, or overall laboratory/site and organisational levels.
Conclusions:
We have completed development of a national BiP program. An activity-based costing methodology based on avoidable costs overcomes many problems of previous benchmarking studies based on total costs. The use of benchmarking complexity adjustment permits correction for varying test-mix and diagnostic complexity between laboratories. Use of iterative communication strategies with program participants can overcome many obstacles and lead to innovations.
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