Real-time management model for frequent Big Data errors : Automatic Clean Repository For Big Data (ACR)

Foodborne pathogens are a major contributor to foodborne illness worldwide. The adaptation of a more quantitative risk-based approach, with metrics such as Food safety Objectives (FSO) and Performance Objectives (PO) necessitates quantitative inputs from all stages of the food value chain. The potential exists for utilization of big data, generated through digital transformational technologies, as inputs to a dynamic risk management concept for food safety microbiology. The industrial revolution in Internet of Things (IoT) will leverage data inputs from precision agriculture, connected factories/logistics, precision healthcare, and precision food safety, to improve the dynamism of microbial risk management. Furthermore, interconnectivity of public health databases, social media, and e-commerce tools as well as technologies such as blockchain will enhance traceability for retrospective and real-time management of foodborne cases. Despite the enormous potential of data volume and velocity, some challenges remain, including data ownership, interoperability, and accessibility. This paper gives insight to the prospective use of big data for dynamic risk management from a microbiological safety perspective in the context of the International Commission on Microbiological Specifications for Foods (ICMSF) conceptual equation, and describes examples of how a dynamic risk management system (DRMS) could be used in real-time to identify hazards and control Shiga toxin-producing Escherichia coli risks related to leafy greens.

Download Full-text

Analysis of community chronic disease health management mode under the background of big data

E3S Web of Conferences ◽

10.1051/e3sconf/202127502026 ◽

2021 ◽

Vol 275 ◽

pp. 02026

Author(s):

Zehao Yao ◽

Shihua Cao

Keyword(s):

Big Data ◽

Blood Glucose ◽

Chronic Disease ◽

Disease Management ◽

Real Time ◽

Chronic Disease Management ◽

Diagnosis And Treatment ◽

Management Model ◽

Diabetic Patients ◽

Hospital Doctors

In recent years, the “Internet + medical” exploration and the country’s vigorously promoted hierarchical diagnosis and treatment system have provided an opportunity to improve the status quo of diabetes. Some scholars have proposed “one-to-one binding community nurses” (Wang Li et al., 2016) and personalized treatment based on big data (He Ting et al., 2016). New chronic disease management concepts such as an integrated chronic disease management model for the elderly based on mobile medical technology (Che Fengyuan et al., 2016). Although different names are used, the core point of view is that patients and community doctors complete the contract, the community doctors will take care of the patients, and the hospital doctors will take care of the patients. The patient’s blood glucose data can be shared with relatives and friends, community doctors, and hospital doctors in real time with the help of platform tools such as blood glucose meters, mobile apps, and cloud medical platforms. And community and hospital doctors’ feedback on patients can also be sent to patients and relatives and friends in real time, thereby realizing hierarchical diagnosis and treatment of diabetic patients when medical resources are scarce and unevenly distributed. This article refers to this model as the “family-style chronic disease management model”. The interaction between patients, relatives and friends, community doctors, and hospital doctors is shown in Figure 1.

Download Full-text