The right way of exploiting Big Data in healthcare is the protected way

Providing the means to exploit Big Data to improve healthcare, without neglecting the ethics behind the use of such data, is one of the main goals of AEGLE project.

Many people have concerns about their personal information being used, especially when it is medical information. They want to know why these data are useful, and what measures will protect their confidentiality. This article will explain why Big Data is useful for improving healthcare, and how we protect patient confidentiality.

What is Big Data?

Big Data involves the use of different data sources to gain usable insights. These data will be of different types (Variety), often very large amounts of data (Volume), analysed rapidly to provide a timely response (Velocity), accurate and reliable (Veracity), and providing some benefit (Value). These are the 5 Vs or pillars of Big Data.

Healthcare already generates vast amounts of data. There are many different sources and types of data, varying from digital inputs from sensors to handwritten or electronic patient clinical records to images and laboratory results etc. Usually, these are fairly reliable data (although for example patient history can be unreliable) and can be used for analysis purposes. The results of such an analysis may need to be acted on within minutes, hours, days or weeks depending on clinical risks and ability for changes to take effect.

Thus, the analysis of data can produce incremental improvements in the delivery of healthcare. It can pinpoint causes of medical errors, highlight best practice, detect unrecognised problems caused by medications, and help identify the best treatment of a particular patient, with specific disease types or processes (the so-called personalised medicine being tailored to each patient).

Patient confidentiality

Healthcare data has safeguards due to the sensitive nature of the patient’s personal details:  healthcare professionals owe their patients a duty of confidentiality whenever they handle patient data. However, valuable research can be conducted on statistical data with all patients’ personal information removed, so that the patient cannot be identified.

Many people are happy to share their healthcare data for research when appropriate safeguards are provided so that anonymous data is used. Patients, as well as ethical boards, would prefer that patients’ consent is sought whenever a patient’s health records or data are used for clinical management or research. But, this may not be always possible for practical reasons, for instance, when dealing with historical datasets.

For this reason, the provision of appropriate safeguards is vital for Big Data projects in healthcare so that patient confidentiality is provided yet at the same time, the data can be harnessed to render usefulness that may help the patients in the future.

The characteristics of applied ethical safeguards in Big Data projects like AEGLE are:

Firstly, in big data analytics, all the data are aggregated together for analysis. This means the details of one particular person cannot be identified.

Secondly, their personal information becomes simply a number of non-identifiable patient results within a large dataset. There are technical methods to ensure this rendering of personalised to non-identifiable data is possible, yet still preserving the useful material for statistical analysis.

The AEGLE project is developing a new ethical methodology for the use of Big Data in healthcare, which will enable the use of data in a way that conforms with patients’ expectations, in terms of privacy and security, whilst maximising the benefits for the wider community. This draws on recent research that has involved the public in discussions about how data can be used, which we will discuss in another blog shortly.

We are offering the opportunity for you to learn more about the latest developments of the AEGLE platform for Big Data analytics in healthcare for (clinical) research and clinical practice, joining our series of webinars (learn how HERE) or watch our videos HERE.

Digital security concept
Digital security concept