Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Dec 19, 2019
Date Accepted: Mar 1, 2020
Date Submitted to PubMed: Apr 29, 2020
Requirements of Health Data Management Systems for Biomedical Care and Research: An Analysis
ABSTRACT
Background:
Over the last century, disruptive incidents in clinical and biomedical research fields yielded to a tremendous change in the health data management system. This is due to the breakthrough in the medical field, and the need for big data analytics and Internet of Things (IoT) incorporated in a real-time smart health information management system. In addition, the requirements of the patient care have evolved over time for more accurate prognosis and diagnosis. In this paper, we discuss the temporal evolution of the health data management systems and capture the requirements that led to the development of given system over a certain period of time. Consequently, we provide insights on those systems and give suggestions and research directions on how these systems can be improved for a better healthcare system.
Objective:
To deduce the characteristics and the limitations of the current health data management systems. Therefore, we developed a taxonomy of health data management systems and map the existing systems to the requirements which are revealed from the state of the art, for better patents care.
Methods:
To study the evolution and requirements of the health data management systems developed over the years, research articles, medical lawsuits, and health regulations and acts were considered. These materials were obtained from IEEE, ACM, Elsevier, Medline, and PubMed databases.
Results:
The health data management system has gone through disruptive transformations over the years from paper to computer, web, cloud, IoT, big data analytics and finally to the blockchain. We analyze the temporal evolution of the reforming definitions of a health data management, and the supporting systems. We then present a taxonomy of the requirements for a health data management system, divided into the following categories: 1) medical record data, 2) real-time data access, 3) patient participation, 4) data sharing, 5) data security, 6) patient privacy and 7) public insights. We provide insights and research directions for the integration and implementation of a smart health data management system to support better healthcare, prognosis and diagnosis.
Conclusions:
There is a need for a comprehensive real-time and secure health data management system that allows physicians, patients and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis/diagnosis of the diseases and their predictions. The prediction results will help in the development of an effective prevention plan.
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Copyright
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