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Big Data, a key tool to guarantee the security and confidentiality of clinical data in the healthcare sector

Big Data, a key tool to guarantee the security and confidentiality of clinical data in the healthcare sector

Healthcare organizations should be aware that harnessing the enormous potential of using Big Data requires ensuring that data related to citizen health is not abused. There must also be a concerted effort to ensure the security of health data in order to be useful for the benefit of the patient and proactively in creating awareness and dissemination campaigns so that citizens know their rights and know the benefits. and the possible disadvantages of sharing your medical data.

These are some of the main findings of the report “Big Data in Healthcare. Opportunities, Emerging Risks and Potential Liabilities ”recently published by Willis Towers Watson, which provides a comprehensive analysis of the state of play of the inclusion of Big Data in healthcare environments globally, its main contributions and the possible risks of its incorrect implementation and / or use.

Applied to the healthcare context, Big Data – transferring huge volumes of data through sophisticated computer processing and analysis applications – has a multidimensional meaning that involves a wide variety of data types and original sources. (public and private medical centers, pharmacies, population and census registers, insurance companies, bank details, research centers, various public administration institutions at central and regional level, NGOs, etc.) as well as the networking and efficient analysis of this data in real time.

“The model, key to quickly facilitating actionable information and enabling informed decision-making in a very complex network with many participants (re) using and linking datasets, involves an inherent tension between great community benefits and possible individual damage, including risk to privacy and possible misuse of sensitive data. A close example is the controversy on the international scene generated by data exchange initiatives and the ethical challenges of contact tracing applications linked to Covid-19 ”, explains Diego de la Torre, expert Willis Health Towers Watson Spain

Main challenges of using Big Data in the healthcare environment

Willis Towers Watson’s analysis highlights 8 main challenges when implementing a Big Data model applied to the healthcare sector:

Interoperability: It is necessary that the systems guarantee accurate and high-quality data and their safe interoperability between different health systems and the agents involved in their management and extraction. Data management: Policies and procedures should protect health information and align with regulations and laws regarding how the devices through which this data can be used and accessed is managed, in each country and in each country. International organisations. Data storage: the volume of health data is massive, its growth exponential and its accessibility increasingly wide across different devices. Traditional physical storage systems are not enough, they are vulnerable, difficult to expand and very expensive to maintain. Accessibility to data and skills on mobile devices: the downfall of the system or the slowness of access to health data from different devices jeopardizes the care of patients. It is essential to deploy a risk management and mitigation strategy, and cloud storage is emerging as a great ally, scalable and able to serve concurrent users without compromising security, even when using many mobile devices. Data Ownership: Medical records, whether printed or electronic, have levels of ownership. Individual data (vital signs, laboratory tests, diagnostics, radiological images, etc.) are generally the property of the patient. But the medium on which the information is recorded and stored is generally considered to be the property of the organization that owns the system (legal custodian who has an obligation to protect this information). Big Data thus generates a model that can be considered as shared property. Responsibility for Data Extraction: The standard of health care continually and necessarily changes. In a big data environment, it must be considered that specialized knowledge is necessary to establish the criteria for extracting data and covers the rest of the chain of their management and extraction. Analytical talent in health data management: The healthcare system needs data scientists and IT staff with health knowledge to be able to perform meaningful analysis. Health risk managers must recognize the new and different professional profiles that are emerging as a result of the transformative influence of big data. On the other hand, medical and administrative staff must obtain clinically relevant information, and not others, for their diagnoses and procedures to be effective. Cybersecurity: there are multiple cybercrimes which, in the case of health data, will be extremely serious (malware / phishing, ransomware, etc.) and the multiplicity of fixed and mobile devices through which data is accessed, each with varying levels of different security, requires the implementation of very high security policies and first level risk management.

Responsibility for the reuse of health data

Governments are increasingly concerned about the possible commercial exploitation of citizens’ medical data, and there are regulations and laws (international and national) that attempt to prevent it or keep it under control. In the case of the European Union, regulations are governed by the General Data Protection Regulation (GDPR).

Some standards rely on de-identification as the primary mechanism for protecting patient privacy, but with rapid advances in re-identification techniques and data link models, existing rules and regulations are not even sufficient to protect patient privacy. those data. exploitation for commercial purposes.

On the other hand, many citizens are not aware, or do not fully understand, the positive and negative of their health data managed and shared with a Big Data model. This limited understanding must be addressed through education campaigns for a transparent system that encourages consent.

informed. In fact, not all countries require such informed consent for the use of secondary data. For example, although the GDPR sets a high threshold for obtaining consent, this is not an absolute requirement and there are several legal bases for exceptions. There is great potential for abuse and misuse of patient data.

As Diego de la Torre explains, “Big Data applied to health brings very promising opportunities and benefits, but it also opens the way for certain parties interested in the commercial exploitation of citizens’ medical data to benefit from it. without their knowledge or consent. . Once again, and fittingly, the individual is at the center of the health data ecosystem. “

Thus, giving priority to the opinion of the patient makes it possible to develop policies for the reuse of data that better reconcile the interests of citizens and limit any friction generated by the extraction, exchange and linking of this data. Added to this is greater transparency on participation

pave the way for legal, equitable, sustainable and community-centered secondary data use strategies.

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