OBJECTIVES: A lot of uncertainties exist when introducing new technologies to the healthcare market. The objective of this study is to construct different implementation scenarios of Home Brain Monitoring (HBM) in epilepsy patients and to value these scenarios by making use of real options modeling to address uncertainty. This model tries to show the (financial) consequences of different uncertainties on the total value of the HBM project to facilitate investment decisions in the future. METHODS: Individual semi-structured interviews were used to present (implementation) uncertainties to 19 epilepsy experts. Expert elicitation was used to collect beliefs regarding uncertainties of HBM and to estimate probabilities of ‘success’ of uncertainties. Scenarios were constructed, each of which describe a set of uncertainties. These uncertainties were resolved in order to obtain a production process with a certain expected value. Finally, a real options model was developed that shows the influence of uncertainties on the total value of the HBM project. Sensitivity analysis was used to demonstrate the impact of different uncertainties. RESULTS: Elicited judgments show that a promising implementation scenario of HBM in the diagnostic track of epilepsy could be the implementation of HBM after a negative routine EEG, as a substitution of subsequent recordings and with the support of a detection algorithm to detect events automatically. The probability of success was lowest for implementing HBM without making use of a detection algorithm. However, implementing HBM before the first EEG recording yields the highest value, but it is not likely to happen. CONCLUSIONS: The implementation of HBM after routine EEG seems to be preferred. The real options model provides an innovative way to represent and value uncertainty in R&D projects. This model is used for the first time in healthcare technology and provides a comprehensive, but not-too-detailed view of challenges and key criteria for success.