Authors: Rahul Potluri, ACALM (Algorithm for Comorbidities, Associations, Length of stay and Mortality) Study Unit (UK)
In this interview we speak with the founder of the ACALM (Algorithm for Comorbidites, Associations, Length of stay and Mortality) Study Unit (UK) – Rahul Potluri – about the role big data can play in achieving personalized medicine, the development of ACALM and how this methodology could help predict cancer patient outcomes. Rahul’s unit was one of the first to use big data in healthcare and medical research and his work showed for the first time a link between high cholesterol and breast cancer. Other prominent studies include health services research, evaluating differences in death rates from weekend admission and discharge from UK hospitals.
Could you provide us with a brief overview of ACALM?
ACALM bore out of the frustration that data was not available for clinical research as expected and as a medical student exploring the plethora of medicine, there were so many unanswered questions that I wanted to explore further. All the research datasets at that time (2006–7) were collected painstakingly and I setup the ACALM algorithm to be able amalgamate completely anonymous, routinely collected data, which had been collected for other purposes into a large research dataset. Even large scale epidemiological studies at that time had a maximum of 100,000 patients and data was collected with tremendous resource and time implications. The first ACALM dataset had over a quarter of a million patients so the power of amalgamation was evident right from the very beginning. My key aim with ACALM is to ensure that all the data that was (and is) being collected can be utilized for research and patient benefit rather than administrative use alone.