TY - GEN
T1 - Mutichain Enabled EHR Management System and Predictive Analytics
AU - Nagori, Meghana
AU - Patil, Aditya
AU - Deshmukh, Saurabh
AU - Vaidya, Gauri
AU - Rahangdale, Mayur
AU - Kulkarni, Chinmay
AU - Kshirsagar, Vivek
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - One of the challenges in biomedical research and clinical practice is that we need to consolidate tremendous efforts in order to use all kinds of medical data for improving work processes, to increase capacity while lessening costs and enhancing efficiencies. Very few medical centers in India have digitized their patient records. Because of less interoperability among themselves, they end up having scattered and incomplete data. Health data is proprietary and being a personal asset of the patient, its distribution or use should be accomplished only with the patient’s consent and for a specific duration. This research proposes multichain as a secure, decentralized network for storing Electronic Health Records. The architecture provides users with a holistic, transparent view of their medical history by disintermediation of trust while insuring data integrity among medical facilities. This will open up new horizons of vital trends and insights for research, innovation, and development through robust analysis. The platform focuses on an interactive dashboard containing year, month, and season wise statistics of various diseases which are used to notify the users and the medical authorities on a timely basis. Prediction of epidemics using machine learning techniques will facilitate users by providing personalized care and the medical institutions for managing inventory and procuring medicines. Vital insights like patient to doctor ratio, infant mortality rates, and prior knowledge of the forthcoming epidemics will help government institutions to analyze and plan infrastructural requirements and services to be provided.
AB - One of the challenges in biomedical research and clinical practice is that we need to consolidate tremendous efforts in order to use all kinds of medical data for improving work processes, to increase capacity while lessening costs and enhancing efficiencies. Very few medical centers in India have digitized their patient records. Because of less interoperability among themselves, they end up having scattered and incomplete data. Health data is proprietary and being a personal asset of the patient, its distribution or use should be accomplished only with the patient’s consent and for a specific duration. This research proposes multichain as a secure, decentralized network for storing Electronic Health Records. The architecture provides users with a holistic, transparent view of their medical history by disintermediation of trust while insuring data integrity among medical facilities. This will open up new horizons of vital trends and insights for research, innovation, and development through robust analysis. The platform focuses on an interactive dashboard containing year, month, and season wise statistics of various diseases which are used to notify the users and the medical authorities on a timely basis. Prediction of epidemics using machine learning techniques will facilitate users by providing personalized care and the medical institutions for managing inventory and procuring medicines. Vital insights like patient to doctor ratio, infant mortality rates, and prior knowledge of the forthcoming epidemics will help government institutions to analyze and plan infrastructural requirements and services to be provided.
KW - Data security
KW - Decentralization
KW - Electronic health record
KW - Multichain
KW - Predictive analysis
UR - http://www.scopus.com/inward/record.url?scp=85076949159&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-0077-0_19
DO - 10.1007/978-981-15-0077-0_19
M3 - Conference contribution
AN - SCOPUS:85076949159
SN - 9789811500763
T3 - Smart Innovation, Systems and Technologies
SP - 179
EP - 187
BT - Smart Trends in Computing and Communications - Proceedings of SmartCom 2019
A2 - Zhang, Yu-Dong
A2 - Mandal, Jyotsna Kumar
A2 - So-In, Chakchai
A2 - Thakur, Nileshsingh V.
PB - Springer
T2 - 3rd International Conference on Smart Trends for Information Technology and Computer Communications, SmartCom 2019
Y2 - 24 January 2019 through 25 January 2019
ER -