Data from medical claims has served as a source of data that has been analyzed for medical research purposes. Most of us are familiar with researchers (Dan Solomon for example) who have made major contributions to the rheumatologic literature through analysis of claims and other data. Companies such as IMS commercialize claims data, aggregating massive amounts of data and selling their analyses to pharmaceutical companies, payers, and others. No rheumatologist has ever been compensated by IMS however. Registries such as CORRONA conduct research and commercialize data as well- through participating physicians. The data entry process for registries is often somewhat laborious, and time consuming, even if reasonably compensated.
Some EMR venders, such as Practice Fusion, retain the rights to the commercial value of the chart data that users enter, in exchange for a low offering fee for their product. They sell the data and the physician who does the entry work is not compensated, and has no awareness of the value of that data.
An essential premise to grasp is that our data only has value when aggregated. Those interested in data want a polling sample that offers validity, and therefore has predictive value. A large practice with ten physicians and an active infusion suite generates and stores a substantial amount of data, but this data is worthless unless it is aggregated together with data from other physicians. A data set should include urban, suburban, and rural physicians from around the country, solo physicians, twosies and large practices.shutterstock_235150300-1
What’s in Your EMR? An enormous amount of data that can be used for multiple purposes.  As you chart clinical information, you record information that can be used to characterize outcomes. When you e-prescribe, then your data can be used to build a variety of pictures- what biologics are most often used first, how many TNFi do we use before switching, what drugs are used as monotherapy, etc.
Such data has substantial value to the pharmaceutical industry in particular. Compliance and adherence are very important to value and outcomes measurement, and can be defined with data. How are patients doing? What outcomes are we achieving? How many patients are in remission, low disease activity? Do we change treatment when markers suggest a change should be considered? We as treating physicians have all the data that claims data lacks- the actual office visit data- as generated at its source. When extracted in a HIPAA compliant fashion, with the identity of both patients and physicians hidden, and then aggregated, our data are a very powerful source of information.
As well, our practice management systems contain an enormous amount of financial data. Wedded together, we can describe what happens to clinical outcomes when payers change and formularies change. Every one of us knows what we are recording- and if we cooperatively record the key information in structured fields that can be data mapped and mined, then it is easy to ruminate on the wealth of information we possess and consider real world questions we can answer. What happens to the Vectra when a patient has cancer or an infection? Does subcutaneous methotrexate work better than oral? And on and on…

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