[New] Paving the way for data-driven health services

There is a huge potential for using health data more efficiently to create insights that will help us improve health and healthcare services. Moving towards more data-driven and digital health services, we need effective tools and a new way of working with health data

These privacy-preserving technologies removes a major privacy issue when it comes to health data management allowing for more parties to take part in using the data. It also reduces the need for traditional privacy measures, such as anonymisation and pseudonymisation.

Every day, tremendous amounts of data are gathered from patients in hospitals and clinics worldwide. These data are used to treat individual patients and keep track of their medical history. However, aggregation of the data from large numbers of patients can provide invaluable insights and understanding of treatment efficacy as well as identify factors affecting prognosis and quality of care.

A main barrier

A main barrier to moving towards a more data-driven healthcare service is that we still operate with a traditional (and outdated) workflow for research. Traditionally, the process of going from raw data to results is set up in a step-by-step manner, involving moving data between different software to get the job done.

First, you need a database where the data is registered (these databases are often expensive and time-consuming to get set up). Then you need to order and arrange your data in a spreadsheet to get them structured and ready for analysis. Next, you need to move your data to a statistical software package for your analysis. This also requires manual organisation and structuring of the data to get the different analyses done properly. Last, you’ll probably need to move through graphical software to prepare your graphs and figures and make your results ready for presentation or publication.

The use of all these tools demands a lot of training to operate the different software, as well as the process itself being a time- consuming endeavour. Another important problem with all this movement and manual handling of raw data is that it introduces risks to data integrity. Last but not least, it’s hard to obtain the necessary data security and privacy in a process involving so many different software where the raw data needs to be exposed to the persons doing the procedures.

True collaboration within this workflow is also hard and most research and registry collaborations as of today concentrate on the data capture phase of a project where multiple parties come together to collect data in the same database, while collaboration in the other phases is restricted as only a few parties get to use the data for analysis.

Research and analysis

At Ledidi we set out to change this by thinking differently about the research process.

We’ve designed a data platform that gathers all the necessary functionalities and stages for a research project or registry within a secure research environment available in the public cloud. This allows for moving away from a traditional linear workflow to a more circular workflow whereby data can be analysed in real-time simply at the click of a button.

Analyses are done directly towards the database based on auto-transformations that removes the need for manual restructuring of raw data to prepare it for analysis. The data is structured from the minute it enters the platform and the persons performing the analyses do not even have to see the raw data.

Moving to a circular workflow also means the data is always ready to be analysed - the results are available when you need them without having to go through a lengthy process in the hands of a statistician or data scientist who are often already at capacity. The easy-to-use design of the software and intuitive analytic panel means people can engage in extracting insights from a complicated database without extensive statistical training. This is a major advantage for any project close to patient treatment, such as clinical audits or keeping track of patient outcomes which are often managed by research nurses and clinical professionals on the front line rather than data experts.

It also opens new and improved ways of working together - across institutional and national borders. The raw data entered from one institution stays protected against access from other institutions, but all parties can still collaborate on both the database design and the use of data for analysis and data visualisation.

Security

And, of course, everything happens within a secure cloud solution, representing the nextgeneration healthcare research environment. We use state-of-the-art technologies at every point where personal data are involved ensuring you can work comfortably without worrying about being compliant with privacy and data security regulations.

Paving the way for datadriven health services

In conclusion, Ledidi is paving the way for a more efficient and collaborative approach to health data analysis. By providing a secure research environment in the cloud, Ledidi enables real-time, privacy-preserving multiparty analysis of clinical data. Ledidi’s platform represents a significant step forward in the pursuit of quality data-driven healthcare aiming at improving health outcomes. L

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