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Zanghelini, Fernando, Ponzo, Aisling, Xydopoulos, Georgios, Fordham, Richard 
ORCID: https://orcid.org/0000-0002-5520-6255 and Khanal, Saval ORCID: https://orcid.org/0000-0001-5201-0612
(2024)
Cost-effectiveness of GaitSmart and an artificial intelligence solution for rehabilitation of patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) in older population in the United Kingdom.
Geriatrics, 9 (5).
ISSN 2308-3417
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Abstract
Background: GaitSmart (GS) is a sensor-based digital medical device that can be used with the integrated app vGym to provide a personalised rehabilitation programme for older people undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA). This study aimed to determine whether the GS intervention used in the rehabilitation of older people undergoing THA or TKA is potentially cost-effective compared to the current standard of care (SoC). Methods: Decision-analytic modelling was conducted to estimate the cost-effectiveness over a seventeen-week time horizon from an NHS perspective. UK clinical and cost data from the GaitSmart randomised clinical trial was used to obtain the input parameters, and a sensitivity analysis was performed to address uncertainties. Results: Over a seventeen-week time horizon, GS incurred cost savings of GBP 450.56 and a 0.02 gain in quality-adjusted life years (QALYs) compared to the SoC. These results indicate that GS is the dominant intervention because the device demonstrated greater effectiveness and lower costs. Probabilistic sensitivity analyses confirm the robustness of our results. Conclusions: GS appears to offer short-term efficiency benefits and demonstrates cost-effectiveness for the improvement in gait in people undergoing THA or TKA, compared to the SoC.
| Item Type: | Article |
|---|---|
| Additional Information: | Data Availability Statement: Dataset available on request from the authors. Funding information: The work was undertaken as part of the Digital health technology catalyst round 4: collaborative R&D project: Modelling and artificial intelligence using sensor data to personalise rehabilitation following joint replacement. The funder did not have any role in the design, analysis, and interpretation of the study. Author Acknowledgments: We thank Innovate UK for funding the GaitSmart randomised controlled trial, the data published from the same trial was used to calculate parameters for the model. We would also like to thank DML for providing GaitSmart data for the economic evaluation. |
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Population Health Faculty of Medicine and Health Sciences > Research Groups > Health Economics |
| Depositing User: | LivePure Connector |
| Date Deposited: | 07 Oct 2024 16:30 |
| Last Modified: | 19 Oct 2024 00:05 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/96934 |
| DOI: | 10.3390/geriatrics9050129 |
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