Looking for to perform a longitudinal study on Multiple Sclerosis (MS) patients, a well-respected data analytics organization knew they had an uphill battle on their hands. Their researchers wanted to collect longitudinal data, but this is a slow, expensive process with many logistical difficulties. They knew the information they wanted already exists at various clinics, but access to raw information for secondary purposes is impossible. They partnered with these clinics to receive de-identified data. But they needed assurance that their data sets were sufficiently de-identified. Using a consultant, a risk assessment was performed and the data sets were judged to be safe to use.
Without this assurance, the clinical data on MS patients would not be used. Patients would need to be recruited independently, the corresponding tests duplicated. Their study would be significantly more difficult to conduct–if it could be done at all. Luckily the data are available just as hope is growing for treatments that will not only halt the progress of this devastating disease affecting millions of people around the world, but actually reversing nerve damage leading to paralysis.
This insurance association processes more than 80% of all claims from 96% of hospitals nationwide. But while the sheer volume of data represents terabytes of rich information for analysis, the association couldn’t fully leverage this critical asset to allow its members to benchmark service delivery. For the association, the main culprit preventing the sharing of more granular, patient-level data was traditional de-identification methods that protected.