Risk Management Framework to Improve Associated Risk of Information Exchange Between Users of Health Information Systems in Resource-Constrained Hospitals

Thangeda, Amarendar Rao; Coleman, Alfred

Information exchange, privacy and security in the healthcare sector is a problem of greater significance. Healthcare Information frameworks capture, store, handle and transmit information identified with the health of the patient. However, risk management in a hospital is complex, as it includes assessing, identifying and averting risks in essentially each area of the healthcare system. In this paper, Octave Allegro based Deep Learning algorithm for a risk management framework to improve the associated risk of information exchange between users of health information systems in resource-constrained hospitals has been proposed. The experimental results show that the proposed algorithm OADLA has potential benefits for patients, organizations, health care providers, and the public during secure information exchange. The proposed Octave Allegro based Deep Learning algorithm which has higher performance when compared with existing Fuzzy based Healthcare Risk Management (FHRM).