Organizations: University of Calgary, Department of Electrical and Software Engineering, Calgary, Alberta, Canada · University of Calgary, Department of Biomedical Engineering, Calgary, Alberta, Canada
Artificial intelligence is increasingly being introduced into healthcare workflows, yet most evaluations emphasize model accuracy rather than whether adoption is economically worthwhile in real clinical settings. This study proposes FLARE, a systematic and uncertainty-aware framework for evaluating the financial and operational implications of adopting AI in healthcare. FLARE combines fuzzy logic, time-driven activity-based costing, and return on investment analysis to estimate the cost of clinical service delivery, the cost of AI development and operation, and the economic consequences of workflow integration under uncertainty. The framework was demonstrated through an early health technology assessment case study of AI-assisted large vessel occlusion detection in the CT stroke pathway for acute ischemic stroke. The case study shows how FLARE can quantify conventional pathway cost, AI-related development and recurring costs, and AI-enabled service savings within a unified activity-based model. Under expected assumptions, the analysis identified a break-even threshold of approximately 3,992 patients per year, with positive first-year return on investment at typical annual stroke volumes of about 5,000 patients. The results further show that economic benefit depends not only on algorithmic performance, but also on patient volume, verification time, infrastructure choices, and workflow design. FLARE provides a transparent and practical decision-support framework for early-stage evaluation of AI adoption in healthcare. By making uncertainty, resource use, and implementation trade-offs explicit, it helps clinicians, administrators, and policymakers determine when AI deployment is economically viable and where operational changes may improve value.
Figures & tables
Figure 1. Evolution of costing methodologies from traditional systems to fuzzy logic–enhanced approaches. Adapted from Ostadi et al. ( 25 ) Diagram tracing the progression of costing methods in four stages: traditional volume- and department-based costing, activity-based costing (ABC), time-driven activity-based costing (TDABC), and fuzzy logic-enhanced FL-TDABC. Each stage is annotated with the limitation of the preceding method that motivated it.
Figure 2. Seven-step FL-TDABC framework integrating fuzzy time estimates within the TDABC cost model. Obtained from Koster et al. ( 18 ) Flow diagram of the seven sequential FL-TDABC steps: select the medical condition or service, define the care delivery value chain, develop process maps, obtain time estimates as triangular fuzzy numbers, estimate resource costs, compute the capacity cost rate, and calculate the total fuzzy cost of care.
Figure 3. CT pathway for acute ischemic stroke patients with suspected LVO, obtained from Sangha et al. Flowchart of the conventional CT stroke pathway across three stages. In ED triage the patient is placed on a stretcher, a code stroke is called, and the stroke team performs an NIHSS assessment. In the CT suite an NCCT and then a CTA are acquired and interpreted, and IV-tPA eligibility and the presence of a large vessel occlusion are confirmed. Eligible patients are transferred to the neuro-interventional radiology suite for groin puncture and endovascular thrombectomy.
Cohort
Description
Sample size ( N )
Heidelberg training cohort
Internal data used for model training
835 (71%)
Heidelberg internal test cohort
Internal test data for validation
344 (29%)
External FAST cohort
Pseudo-prospective validation
Not reported
External UKB cohort
External pseudo-prospective validation
Not reported
Table 1. Cohorts used in Brugnara et al.
Component
Estimated Cost (CAD)
Monthly costs
AWS SageMaker infrastructure ( CAWS )
8,050.86
ML Engineer (40 hr/week @ 44.10 CAD/hr)
7,644.00
Total monthly recurring cost
15,694.86
Annualized cost (12 months)
AWS SageMaker (12 months)
96,610.32
Table 2. Monthly and annual recurring cost components for the LVO AI solution (production phase).
Figure 4. Integration of AI-assisted CTA interpretation within the CT suite. The AI replaces the manual interpretation step, while expert verification remains unchanged. Block diagram of the CT stroke pathway showing where the AI-assisted CTA interpretation module is inserted between image acquisition and expert verification, while all other workflow steps remain unchanged.
Figure 5. Cumulative ROI over five years under pessimistic, expected, and optimistic scenarios as one-time development costs are amortized. The expected case demonstrates steady positive returns, reaching approximately 44% by Year 5. The pessimistic scenario remains negative throughout, while the optimistic scenario shows substantial gains exceeding 150% by Year 5. Line chart with years one through five on the horizontal axis and cumulative ROI as a percentage on the vertical axis. Three lines are plotted. The optimistic line rises steeply to above 150 percent by Year 5, the expected line rises from about 25 percent in Year 1 to roughly 44 percent by Year 5, and the pessimistic line stays below zero across all five years.
Figure 6. ROI vs. annual patient volume across pessimistic, expected, and optimistic scenarios. Line chart with annual patient volume from 1,000 to 10,000 cases on the horizontal axis and Year 1 ROI as a percentage on the vertical axis. Three lines are plotted for the pessimistic, expected, and optimistic scenarios; all rise as volume increases. The expected line crosses zero ROI at approximately 3,992 patients per year, the optimistic line crosses zero near 3,000 patients, and the pessimistic line crosses at a higher volume.
Appendix figures & tables8 assets
Supplementary material from the paper’s appendix.
Appendix
Resource (Personnel)
Hourly Rate (CAD)
CCR (CAD/min)
ED clerk
24.50
0.41
ED technician
44.08
0.73
ED physician
130.24
2.17
ED nurse
47.38
0.79
NIR nurse
58.51
0.98
NIR technologist
44.61
0.74
Appendix
Table 3. Personnel hourly rates and capacity cost rates (CCR) used for FL–TDABC modeling. All rates are estimates for a Canadian academic hospital setting..
Material
Unit Cost (CAD)
Source
Lab tests
0
–
Contrast (CTA)
125
Mayfair Diagnostics
IV–tPA (Alteplase 100 mg)
2,746
Federal Patented Medicine Prices Review Board (PMPRB)
Stroke pack
5,500
–
Appendix
Table 4. Material cost estimates used in the CT pathway cost model (in CAD).
Activity
Sub-tasks
Personnel
Equipment
Material
a
b
c
Expected Time (min)
Expected Cost (CAD)
Cost Low
Cost High
ED Triage
ED assessment with positive stroke symptoms
Placed on stretcher
ED clerk; ED nurse
Stretcher; Monitor
–
7
12
60
26.33
31.59
8.4
72
Code stroke called by triage
ED nurse; ED physician
–
–
5
10
60
25
74
14.8
177.6
Patient monitoring
Placed on stretcher
ED nurse; ED technician
Monitor; Stretcher
–
5
10
60
25
38
7.6
91.2
Hooked up to monitor
Appendix
Table 5. Process map of the conventional CT pathway. The activity structure and workflow steps were obtained from Sangha et al. ( 31 ) , while all time estimates and cost parameters were obtained through a time–motion study conducted with a practicing radiologist in a tertiary academic stroke centre.
Personnel role
Hourly rate (CAD)
CCR (CAD/min)
PACS / IT Technician
40.12
0.67
ML Engineer
44.10
0.74
Data Scientist
45.67
0.76
Radiologist
44.61
0.74
Appendix
Table 6. Personnel hourly rates and capacity cost rates (CCR) used for AI development costing.
Activity
Personnel (CCR, CAD/min)
a (min)
b (min)
c (min)
x∗=3a+b+c
Expected Cost (CAD)
Low / High (CAD)
Data gathering
PACS/IT (0.67)
3.00
4.00
5.00
4.00
2.68
2.01 / 3.35
Data Preprocessing
ML Engineer (0.735)
1.12
1.38
1.88
1.46
1.07
0.82 / 1.38
Data Labeling
Radiologist (0.74)
17.00
22.00
27.00
22.00
16.28
12.58 / 19.98
Model training †
ML Engineer (0.735)
6.00
7.00
8.00
7
5.18
4.41 / 5.88
Total per sample
25.22
19.86 / 30.64
Appendix
Table 7. Process map for the Heidelberg training cohort ( N=835 ).
Activity
Personnel (CCR, CAD/min)
a (min)
b (min)
c (min)
x∗=3a+b+c
Expected Cost (CAD)
Low / High (CAD)
Data gathering
PACS/IT (0.67)
3.00
4.00
5.00
4.00
2.68
2.01 / 3.35
Data Preprocessing
ML Engineer (0.735)
1.12
1.38
1.88
1.46
1.08
0.83 / 1.39
Data Labeling
Radiologist (0.74)
17.00
22.00
27.00
22.00
16.28
12.58 / 19.98
Inference
ML Engineer (0.735)
0.27
0.33
0.47
0.36
0.27
0.20 / 0.35
Evaluation / review
Radiologist (0.74)
5.00
7.00
9.00
7.00
5.18
3.70 / 6.66
Total per sample (priced)
25.49
19.32 / 31.73
Appendix
Table 8. Process map for the Heidelberg internal test cohort ( N=344 ).
Decision, Risk, and Operations, Columbia Business School · Department of Statistics, Columbia University · Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University