Big Data in Medicine
Properly used, big data could save the American health-care system $300 billion a year and the European public sector €250 billion.
Of all the industries mentioned in this piece, health care has by far the most to gain from collecting, aggregating, and analyzing data. In fact, health care providers already collect a huge amount of data each and every day–it’s called “patient records.” Unfortunately, this data exists in unusable formats isolated within silos in the hands of people who have no idea what to do with it (namely physicians; sorry, but I’ve seen the extent of epidemiological and biostatistical education in medical schools and it’s pathetic). Forcing electronic medical record (EMR) systems upon private practices will not solve this problem because most of the EMRs are incompatible and no mechanism exists to aggregate data on a large scale, which is necessary to leverage such data for quality improvement and new scientific inquiry. If we were able to aggregate large amounts of patient data (and some health systems are doing so), then we would be able to exploit variations in care processes to determine what works and what is useless. Many European countries, due to their single-payer system, already do this and continually contribute valuable research from this population-based data. The Dartmouth Atlas of Health Care is the most notable example of such data aggregation and analysis in the US. However, this project (and many others like it) largely relies on billing data which is inaccurate and limited. We need comprehensive aggregation of patient data (demographics, diagnoses, laboratory and radiological data, and discharge information/follow-up) and advanced analytical methods (such as natural language processing) to develop a more robust understanding of patient care and potential areas for improvement.