A Scalable Cloud-Based Medical Adherence System with Data Analytic for Enabling Home Hospitalization

Abstract

Medication non-adherence is one of the most significant concerns in managing chronic diseases which has inevitable consequences. While various technologies and research have been developed and carried out to monitor medical adherence for patients, their approaches lack in terms of the assurance of medicine consumption and the cost effectiveness of their solutions. This paper provides a cloudbased medical adherence system that can track patients' medicine intake based on the physical effects of the medicine on their bodies by tracking their vital signs. A machine learning model is trained to classify the patient health status and this data is used to determine whether their bodies are responding to the medicine, which is used to alert doctors to enable home hospitalization. The use of this system is proposed to serve as a secondary decision support provider to compliment and ease the decision-making process done by doctors.