Medical Big Data Risk Management: A Systematic Management Approach Based on Bayesian Belief Networks

Table 1

Dimensional system of MBDR.

Major dimension	Subdimension	Explanation	Author, year
Customer risk (risk dimension 1)	Marginalisation of special populations	Those who lack the knowledge or capacity to use digital resources are at risk of digital exclusion	[20]
	The rejection of digital technology	Digital health services are not accessible to the elderly, people with low social status and low education levels	[20]
	Customer privacy issues	Risk of data leakage of patient medical information use of data for discriminatory or other harmful purposes	[28]; [33]; [23]
	Deepening the digital divide	No equal access to digital health services	[21]
	Distrust of information	Patients raise concerns about the reliability of health information	[22]; [20]
Financial risk (risk dimension 2)	Customer growth costs	As patient data grows leading to dramatic cost increases	[24]
	Pay extra fees and premiums	Insurers and other healthcare providers must have sophisticated analytical systems to identify cost overruns due to fraud errors	[25]; [26]
	Settlement management risk	Lack of standardized accounting, poor refund system, difficulties in data reconciliation, etc.	[31]
	Require huge investment	The application of big data technology in hospitals requires significant investment	[24]
	Data processing costs	Obtaining and cleaning data is expensive and time-consuming	[31]; [27]
External environmental risk (risk dimension 3)	The dynamic nature of the law	Regulatory policy for big data healthcare is changing	[32]; [26]
	Privacy laws are missing	Lack of legislation to effectively protect the privacy of patients or users	[28]
	Big data healthcare policy issues	This includes issues of the structure of the form of big data healthcare policy, the responsibility and power of policy subjects, and the lack of detailed policies	[34]; [29]
	Lack of medical safety supervision	There is no uniform and authoritative national standard for the regulation of smart medical safety, and there is no special review mechanism and review organization for the listing of smart medical devices	[26]
Medical quality management risk (risk dimension 4)	Risk of drug administration	Lack of real-time, accurate and non-repudiation implementation records for business processes in drug implementation chain	[31]
	Incorrect diagnosis	Smart healthcare has the potential for malpractice, misdiagnosis and omission	[33]
	Complexity of medical services	The standard of care provided has a variety of rules and regulations that require different medical services depending on the patient	[33]
	Workflow risks	May cause disruptions or slowdowns in workflow and productivity	[32]
Information system risk (risk dimension 5)	Limited infrastructure	Lack of organised infrastructure to receive incoming data	[23]
	Data sharing issues	A national data center has not yet been established and is incapable of interoperability and sharing of medical data transmission	[35]
	Data quality issues	Poor data quality due to poorly managed medical information systems, large confusing and complex data	[36]
	Data acquisition issues	Difficulties in obtaining patient data	[32]
	Medical data transmission issues	Data encryption lacks a complete transmission protocol, and data may be modified or lost during transmission	[37]; [28]
	Hacking problem	Hackers illegally break into medical information systems to steal data due to profit motive	[29]