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

• Main Authors: Valenzuela Espinoza, Alexis (Author), Devos, Stefanie (Author), van Hooff, Robbert-Jan (Author), Fobelets, Maaike (Author), Dupont, Alain (Author), Moens, Maarten (Author), Hubloue, Ives (Author), Lauwaert, Door (Author), Cornu, Pieter (Author), Brouns, Raf (Author), Putman, Koen (Author)
• Format: Book
• Published: JMIR Publications, 2017-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2291-5222; 10.2196/mhealth.8288
• Keywords: Information technology; T58.5-58.64; Public aspects of medicine; RA1-1270
• Source: JMIR mHealth and uHealth, Vol 5, Iss 11, p e175 (2017)

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.