JMIR mHealth and uHealth (Nov 2017)

Time Gain Needed for In-Ambulance Telemedicine: Cost-Utility Model

  • Valenzuela Espinoza, Alexis,
  • Devos, Stefanie,
  • van Hooff, Robbert-Jan,
  • Fobelets, Maaike,
  • Dupont, Alain,
  • Moens, Maarten,
  • Hubloue, Ives,
  • Lauwaert, Door,
  • Cornu, Pieter,
  • Brouns, Raf,
  • Putman, Koen

DOI
https://doi.org/10.2196/mhealth.8288
Journal volume & issue
Vol. 5, no. 11
p. e175

Abstract

Read online

BackgroundStroke is a very time-sensitive pathology, and many new solutions target the optimization of prehospital stroke care to improve the stroke management process. In-ambulance telemedicine, defined by live bidirectional audio-video between a patient and a neurologist in a moving ambulance and the automated transfer of vital parameters, is a promising new approach to speed up and improve the quality of acute stroke care. Currently, no evidence exists on the cost effectiveness of in-ambulance telemedicine. ObjectiveWe aim to develop a first cost effectiveness model for in-ambulance telemedicine and use this model to estimate the time savings needed before in-ambulance telemedicine becomes cost effective. MethodsCurrent standard stroke care is compared with current standard stroke care supplemented with in-ambulance telemedicine using a cost-utility model measuring costs and quality-adjusted life-years (QALYs) from a health care perspective. We combine a decision tree with a Markov model. Data from the UZ Brussel Stroke Registry (2282 stroke patients) and linked hospital claims data at individual level are combined with literature data to populate the model. A 2-way sensitivity analysis varying both implementation costs and time gain is performed to map the different cost-effective combinations and identify the time gain needed for cost effectiveness and dominance. For several modeled time gains, the cost-effectiveness acceptability curve is calculated and mapped in 1 figure. ResultsUnder the base-case scenario (implementation cost of US $159,425) and taking a lifetime horizon into account, in-ambulance telemedicine is a cost-effective strategy compared to standard stroke care alone starting from a time gain of 6 minutes. After 12 minutes, in-ambulance telemedicine becomes dominant, and this results in a mean decrease of costs by US –$30 (95% CI –$32 to –$29) per patient with 0.00456 (95% CI 0.00448 to 0.00463) QALYs on average gained per patient. In over 82% of all probabilistic simulations, in-ambulance telemedicine remains under the cost-effectiveness threshold of US $47,747. ConclusionsOur model suggests that in-ambulance telemedicine can be cost effective starting from a time gain of 6 minutes and becomes a dominant strategy after approximately 15 minutes. This indicates that in-ambulance telemedicine has the potential to become a cost-effective intervention assuming time gains in clinical implementations are realized in the future.