Projects per year
Methods: The costs and cost-effectiveness of user-testing were explored by modifying an existing probabilistic decision-analytic model. The adapted model considered administration of intravenous voriconazole to hospital inpatients by nurses. It included 11 error types, their probability of detection and level of harm. Model inputs (including costs) were derived from our previous study and other published data. Monte Carlo simulation using 20,000 samples (sufficient for convergence) was performed with a 5-year time horizon from the perspective of the 121 NHS trusts and health boards that use the IMG. Sensitivity analyses were undertaken for the risk of a medication error and other sources of uncertainty.
Results: The net monetary benefit at £20,000/quality adjusted life year was £3,190,064 (95% credible interval (CrI): −£346,709 to £8,480,665), favouring user-testing with a 96% chance of cost-effectiveness. Incremental cost-savings were £240,943 (95%CrI: £43,527 to £491,576), also favouring user-tested guidelines with a 99% chance of cost-saving. The total user testing cost was £6,317 (95%CrI: £6,012 to £6,627). These findings were robust to assumptions about a range of input parameters, but greater uncertainty was seen with a lower medication error risk.
Conclusions: User-testing of injectable medicines guidelines is a low-cost intervention that is highly likely to be cost-effective, especially for high-risk medicines.
- Guidelines as Topic
- Health professionals
- Injection preparation
- medicines information
- medicines-related errors
- administration, intravenous
- 1 Finished
1/01/18 → 31/12/19
Project: Central government, health and local authorities
Dataset for "Costs and cost-effectiveness of user-testing of health professionals’ guidelines to reduce the frequency of intravenous medicines administration errors by nurses in the United Kingdom: a probabilistic model based on voriconazole administration
Jones, M. (Creator), Franklin, B. D. (Creator), Raynor, D. K. (Creator), Thom, H. (Creator), Watson, M. (Creator) & Kandiyali, R. (Creator), University of Bath, 3 Aug 2021