Telerehabilitation (TeleRehab) is the modern innovation used for rehabilitation service. Evidence in favor of readiness among healthcare personnel in adopting TeleRehab is limited. Since “readiness” is a crucial prerequisite to the successful implementation of an innovation, studying the healthcare personnel readiness for TeleRehab is mandatory to gain a better understanding of the relationships among the factors. The main aim of this paper is to determine the relationship among the readiness factors of healthcare personnel and to identify the most influential factors from the recommended readiness list with the help of ISM approach. The study has been conducted in three different phases: the identification of readiness factors from reviewing the literature, interviews with personnel healthcare, and determining the relationship among the readiness factors and its most influential factor. Twelve (12) relevant readiness factors have been identified from reviewing the literature and interviews with experts. Through the use of ISM, five (5) factors have been identified as driver factors; another five (5) factors have been identified as the linkage factors and two (2) factors have been identified as the dependence factors. No factor has been identified as autonomous factor. Out of which, one (1) factor has been identified as top-level factor and one (1) bottom level factor. Clear understanding of these readiness factors will help healthcare institutions to better prioritize and manage their human resource, healthcare personnel in an efficient and effective way to adopt TeleRehab. The proposed structured model developed will help to understand relationship of the readiness factors.

An effective rehabilitation procedure is required to successfully manage the disabilities caused by diseases such as stroke, spinal cord injury (SCI), traumatic brain injury (TBI), and cerebral palsy (CP). In this regard, Medical Interactive Recovery Assistant (MIRA) platform proffers virtual rehabilitation through exergames. Virtual reality therapy (VRT) has recently gained attention for upper limb rehabilitation due to its positive impacts on patients’ performance. VRT is a modern interactive application that integrates computer software with hardware devices to create an interactive virtual environment when playing different types of games and exercises (exergames). The output of playing the game generates statistical features (parameters) reflecting the patients’ performance. However, physiotherapists who manage the input settings of exergames according to specific movements cannot easily predict the future performance of the patients based on their observations. Thus, this study proposes a decision tree model to predict MIRA patients’ future performance for three difficulty levels (easy, medium and hard) with respect to their previous/last session records. Patients’ data in the previous/last session are used to determine the prediction values according to the proposed model. This helps physiotherapists to monitor and also predict their patients’ progress using certain prediction values. Results prove the efficiency of the proposed decision tree-based statistical tool for prediction in medical monitoring applications. © 2021, Springer Nature Switzerland AG.