Healthcare data is mainly collected and stored in the following three separate data pools:

1. Clinical data
2. Financial data
3. Research data

Because the responsibility for collecting, storing and utilising this data rests with different individuals or institutions (clinical – hospitals, clinics; financial – managers, governments; research – universities, pharmaceutical companies), they remain largely isolated from each other with little interconnectivity. The disparate systems on which these datasets are located are typically unsuitable for complex integrative analysis. This disconnect between healthcare data can be detrimental to the early identification of important healthcare trends or adverse events, as highlighted by the withdrawal of a popular pain relief drug rofecoxib in 2004.

Rofecoxib was approved by US Food and Drug Administration in 1999 and gained widespread acceptance amongst physicians worldwide who prescribed it to over 80 million people worldwide. In 2004, a California-based integrated managed-care consortium Kaiser Permanente connected clinical and financial data to compare the risk of adverse cardiovascular events for users of rofecoxib against a similar drug; it found that rofecoxib might have been responsible for more than 27,000 avoidable myocardial infarction (heart attack) and sudden cardiac deaths between 1999 and 2003. This study led to a voluntary withdrawal of the drug from the market. Interestingly between 1999 and 2004, similar conclusions were suggested by a number of small scale studies, however none was considered large enough to raise sufficient concerns. The simple act of combining clinical and financial data provided the crucial research dataset that was required to trigger one of the largest medication withdrawals in history.

Although the above example is a powerful indicator of the potential benefits of having an integrated approach on healthcare data, large scale implementation of such approaches have proven to be challenging and therefore they has remained underutilised. In the UK, implementation of Payments by Results (PbR) in National Health Service to integrate clinical outcomes with financial remuneration has produced mixed results. Such approaches usually require a fundamental reorganisation of the industry processes and support by technology appropriate innovations in policy.

One such example is the recently launched government funded Secure Unified Research Environment (SURE) project in Australia, which aims to overcome such limitations by providing a central datacenter where researchers can form connections between data sources and access the necessary computing power required to perform such analysis. In its short span of active operation, researchers using this integrated database have been able to confirm the intuitive beliefs that the older Australians are more likely to have higher consistency of care, and that lower consistency of care is associated with geographical remoteness. It also led to a counter-intuitive discovery that wealthier and more highly educated Australians have a lower consistency of care.

It is important to note that although researchers were able to test intuitive beliefs using more complex and time consuming methods before the existence of SURE database, counter-intuitive discoveries would not have been possible by looking at a single dataset alone.¹The content of this post was later included in a chapter in the following publication: Tyagi, H. (2013). Health data technologies: the current challenges. In NEXUS STRATEGIC PARTNERSHIPS (Ed.), Commonwealth Health Partnerships. London: Nexus Strategic Partnerships for the Commonwealth Secretariat.

Also published on Medium.