A time-and-motion approach to micro-costing of high-throughput genomic assays.
BACKGROUND
Genomic technologies are increasingly used to guide clinical decision-making in cancer control. Economic evidence about the cost-effectiveness of genomic technologies is limited, in part because of a lack of published comprehensive cost estimates. In the present micro-costing study, we used a time-and-motion approach to derive cost estimates for 3 genomic assays and processes-digital gene expression profiling (gep), fluorescencein situhybridization (fish), and targeted capture sequencing, including bioinformatics analysis-in the context of lymphoma patient management.


METHODS
The setting for the study was the Department of Lymphoid Cancer Research laboratory at the BC Cancer Agency in Vancouver, British Columbia. Mean per-case hands-on time and resource measurements were determined from a series of direct observations of each assay. Per-case cost estimates were calculated using a bottom-up costing approach, with labour, capital and equipment, supplies and reagents, and overhead costs included.


RESULTS
The most labour-intensive assay was found to be fish at 258.2 minutes per case, followed by targeted capture sequencing (124.1 minutes per case) and digital gep (14.9 minutes per case). Based on a historical case throughput of 180 cases annually, the mean per-case cost (2014 Canadian dollars) was estimated to be $1,029.16 for targeted capture sequencing and bioinformatics analysis, $596.60 for fish, and $898.35 for digital gep with an 807-gene code set.


CONCLUSIONS
With the growing emphasis on personalized approaches to cancer management, the need for economic evaluations of high-throughput genomic assays is increasing. Through economic modelling and budget-impact analyses, the cost estimates presented here can be used to inform priority-setting decisions about the implementation of such assays in clinical practice.
Canadian Centre for Applied Research in Cancer Control, Vancouver, BC; Department of Cancer Control Research, BC Cancer Agency, Vancouver, BC.
S
S
Costa
Canadian Centre for Applied Research in Cancer Control, Vancouver, BC; Department of Cancer Control Research, BC Cancer Agency, Vancouver, BC; School of Population and Public Health, University of British Columbia, Vancouver, BC.
D A
DA
Regier
Centre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC.
B
B
Meissner
Canadian Centre for Applied Research in Cancer Control, Vancouver, BC; Department of Cancer Control Research, BC Cancer Agency, Vancouver, BC.
I
I
Cromwell
Centre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC.
S
S
Ben-Neriah
Centre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC.
E
E
Chavez
Centre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC.
S
S
Hung
Centre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC.
C
C
Steidl
Centre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC; Department of Medicine, University of British Columbia, Vancouver, BC.
D W
DW
Scott
Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, University of British Columbia, Vancouver, BC; Department of Medical Genetics, University of British Columbia, Vancouver, BC.
M A
MA
Marra
Canadian Centre for Applied Research in Cancer Control, Vancouver, BC; Department of Cancer Control Research, BC Cancer Agency, Vancouver, BC; Faculty of Health Sciences, Simon Fraser University, Burnaby, BC.
S J
SJ
Peacock
Centre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC; Department of Medicine, University of British Columbia, Vancouver, BC.
J M
JM
Connors
Personalized medicine; economic evaluations; genomic technology; micro-costing; time-and-motion analyses