Managing real world data governance

Managing real world data governance

An aspirational framework to make the most of data and its role in improving patient care

Claims data, activity records, clinical registries, electronic health records: these are all examples of sources for 'real-world data' (RWD), a term used to describe information that is collected outside an experimental clinical trial setting. RWD therefore has the benefit of reflecting outcomes in routine clinical practice.

As well as the ever-improving technology which is allowing us to collect more and more data, the environment for utilising RWD to support decision-making is also changing. Regulatory flexibilities and increasing public pressure to facilitate earlier access to medicines (eg through adaptive pathways or early access schemes) means that regulators are increasingly monitoring benefits and risks throughout a medicine's life cycle. Payers are similarly being challenged to conduct earlier value assessments under greater uncertainty, and revisiting assessments as further RWD is collected. Moreover, managed entry agreements and performance-based payments require data collection alongside clinical practice. These emerging regulatory and reimbursement models all aim to make treatment provision and access more sensitive to the evolving evidence base, and all require RWD. However, beyond data collection capabilities alone, the processes that govern how data is collected, processed and shared, all impact the utility of RWD.

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RWD must become real-world evidence (RWE) through a series of activities that transform raw data into analysis and results. Robust and appropriate data governance, applied at each step, are essential in realising the value of RWD and its derivative RWE. In a study conducted by OHE Consulting, funded by Lilly, we analysed the current data governance arrangements in eight countries, and proposed an aspirational governance framework based on these analyses.

What is the role for data governance in healthcare? Information governance in healthcare has the central aim of balancing public and privacy interests - of advancing our understanding of medical treatments through evaluation and research, on the one hand, and protecting individuals' privacy, on the other. Issues arise because RWD is being utilised for purposes beyond those for which it was originally collected - to directly manage patient care. Legal frameworks are playing catch-up in order to accommodate these new secondary uses of data, which can benefit patients and society but in a different way. The resulting rules and regulations vary substantially by country, and are not always completely prescriptive. This makes a clear and transparent governance framework all the more important.

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What are the core elements of an aspirational data governance framework? Good data governance is essential for making the best use of personal health information, enabling a learning health system where knowledge flows effectively and efficiently between research and clinical care. It is also essential in fostering public trust. We break down a governance framework according to four themes which represent steps in a data value chain: collecting the raw data; cleaning and managing the data; linkage and aggregation; and access/use of the data.

Personal data is any data that contains or can be attached to personal identifying information. Patient consent has a central role to play in the authorisation and protection of data. As such, the systems and processes in place to collect data must be cognisant of the intended later uses.

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