Crowdsourcing Medical Decisions — Putting Big Data To Work in Healthcare
By Richard Cramer, chief healthcare strategist, Informatica
The widespread adoption of electronic health records (EHRs) is a key objective of the Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009. With the pervasive use of EHRs, an enormous volume of clinical data will be readily accessible that has previously been locked away in paper charts. The potential value of this data to yield insights into what works in healthcare, and what doesn’t work, dwarfs the benefits of simply replacing a paper chart with an electronic system. There’s appropriate enthusiasm that this data is going to be a veritable goldmine for enterprise data warehousing, business intelligence, and comparative effectiveness research. However, there are other, equally valuable, uses for this data to enhance clinical decision-making and improve the value of healthcare spending. Simply having instant access to large volumes of data that span thousands or tens-of-thousands of physicians, hundredsof- thousands of patients and millions of encounters, offers an unparalleled opportunity to increase the quality and lower the cost of healthcare.
There’s a memorable video from a TED conference where the audience is asked to estimate the weight of a live ox on stage. Although the guesses from the audience vary wildly from unrealistically low to whacky high, the average comes within 3 pounds of the correct weight. The point here is that the collective intelligence of a crowd can answer a question with greater accuracy than an expert in ‘ox weight estimating’ could achieve individually.
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