Recently hospital re-admission rates have received national attention due to the rise in healthcare costs and poor clinical outcomes specially in case cardiac surgery patients. While there exists risk prediction model for discharging patients, few are utilized due several limitations such as poor discriminative ability, lack of discharge assessment and inadequate measurement of functional status. Further none of the current models present real-time actionable information to facilitate early identification and risk-stratification for patients. Hence in this paper we propose a Mobile Cardiopulmonary Tolerance Score System (MCATSS), which is a system that facilitates data collection using a series of automated tests that incorporate data acquisition from external sensors placed on the patient's body. MCATSS has been designed to reduce erroneous input collection, increase the ease of use for clinicians and provide detailed statistics for medical personals to assist them in determining the safe discharge of patients. Overall, MCATSS offers a platform for digital cross-talk of continuous, sensor- and input-driven data to clinicians at their fingertips and will improve patient care, communication and evidence-based practice while decreasing costs and readmission rates.