Purpose or Learning Objective: To demonstrate the cost-effectiveness of artificial intelligence (AI) software to aid in the detection of intracranial vessel occlusions in stroke compared to standard care by performing early health technology assessment.
Methods or Background: We used a Markov based model from a societal perspective in a UK setting to demonstrate the potential value of an AI tool reported in expected incremental costs (IC) and effects (IE) in quality adjusted life years (QALYs). Initial population existed of patients suspected of stroke based on symptoms and exclusion of other causes as demonstrated by non-contrast cerebrum CT. Input parameters for the model were predominantly based on stroke registry data from the UK and complemented with pooled outcome data from large randomized trials. Parameters were varied to demonstrate model robustness.
Results or Findings: The AI strategy with its base-case parameters (6% missed diagnoses of intra-arterial therapy eligible patients by clinicians, $40 per AI analysis, 50% reduction of missed vessel occlusions by AI) resulted in modest cost-savings and incremental QALYs over the projected lifetime (IC: - $156, -0.23%; IE: +0.01 QALYs, +0.07%) per ischaemic stroke patient. Within a ninety-day window after treatment no financial (IC: +$60) and negligible QALY (IE: +0.0001) gain was observed. For each yearly cohort of patients in the UK this translates to a total cost saving of $11 million.
Conclusion: We showed that computer aided thrombus detection in emergency care has the potential to increase health and save costs. Results may contribute to the debate on the investments, financial accountability and reimbursement for the clinical use of AI technology.
Limitations: Parameter values of the model were based on results from previous studies.