Cloud Based EMRs: Better Post-FDA-Approval Research

A recently closed longitudinal study of a medication to boost “good” HDL cholesterol concluded: “… that the HDL-boosting drug niacin failed to cut the risk for heart attacks and strokes.” The study was designed to track patients for 4 to 6 years but was terminated 18 months early based on the results to that point. The cost: $52.7 million or $15,500 per person for the 3,400 study participants. Fully networked, cloud based, electronic medical records (EMR) appear to offer a better solution.

The topic of this post is research about medications that have received FDA approval and are being prescribed for general use. The subject of a research study could be a new medication being tracked for purposes of risk management among patients who were not fully represented in the limited sample used to obtain FDA approval. It could also be an established medication where adverse events are suggesting that more needs to be learned or where there is reason to believe it is not significantly effective to justify continued sale. In this case, it was a matter of both effectiveness and risk.

The key to a better solution is the evolution of fully networked, cloud based EHRs that create a database that is large enough to provide meaningful statistics about specific occurrences. As an example, Practice Fusion now hosts electronic records created by 90,000 medical providers in a single database that has more than 12 million patient records and is growing. The data is being collected as part of physicians’ normal practice—the electronic version of historically hand written notes.

There are operational advantages:

• Data collection is conducted to serve the day-to-day needs of the physician and their staff so they have established procedures and a vested interest in quality.
• The use of the data is totally independent of its collection so there is no bias in the data collection process or the data; neither the doctor nor the patients are even aware of how the data may be used: a totally blind process.
• Separation of data collection and use remove any presumption of undue influence by the sponsor of the study.
• Data is uploaded by physicians daily so it can be made available in near real-time for use at checkpoints in the study.
• If an area of particular interest is discovered, e.g., women over 60 who are more than 20 pounds overweight, additional participants with those characteristics can be identified and added to provide a larger, more reliable sample of that group.
• The study can provide information about risks and effectiveness, increased levels of HDL, and continued use, i.e., prescription renewal.
• In many cases, patient history related to their disease including prior medications is available and information will be available about patients who stop taking the medication or change to a different medication.
• The same database can be used to create a control group of patients that are not taking the medication and have essentially the same medical conditions and demographics as those who are; if a member of the control group begins taking the medication they can be moved to the study group and replaced in the control group—there is no need to deny patients the opportunity to take the medication just to protect the integrity of the data.

The largest benefit is that there is no marginal cost for data collection. The data is already being collected. There are, of course, charges for extraction of the specific data required for the study, for HIPAA compliant de-identification of the data, and for the use of the data. The study costs cited in the introduction, $15,500 per participant, include costs in addition to data collection, but data collection is a major part of that cost and could be dramatically reduced through the use of fully networked, cloud based, electronic health records.

Better data for the reasons noted above at lower cost translate to better healthcare at lower cost.

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