Blog: David Loshin

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Minnesota, Medicare, Data Quality, and Master Data

More controversy swirls around the new Medicare regulations. According to this grand Forks Herald article, bad data associated with Medicare customer systems has resulted in a cost of over $2,000,000.00 to the state of Minnesota. According to the story, "In many cases, the massive and nationwide array of computer problems, bad data (emphasis mine) and overwhelmed Medicare and drug-plan phone lines prevented pharmacists from verifying that a customer was eligible for the deep subsidy - and sometimes unable to find out if that customer even was enrolled in a drug plan."

Two problems reported: bad data and inability to find out if that customer was enrolled.

The first problem is left unspecified, as if it is clear what makes the data bad. The second indicates a less-than reliable master customer repository, which suggests that quality expectations were not well-specified before the law changed.

What is interesting, though, is that this is a good example of cost impacts that are both quantifiable and attributable to poor data quality (whether it be the nebulous "bad data" or the more precise master data management failure). The simple costs are the gaps in coverage, such as the $2.2M that MN is paying the pharmacists for the 38,000 claims (which works out to a little less than $60.00 per claim). The more important, yet more difficult to quantify cost involves the loss of confidence in the ability to provide low-cost medication to the people who need it most.

Moreover, the confusion doesn't really end there. In this article from the Seattle Times, there is talk of failure in communication and information exchange regarding covered medications, enrollment, administration, and workflow bottlenecks.

So here is my last comment, which is intended to reflect on those who constantly ask me for examples of ROI models for data quality. Had everything gone smoothly, with no data issues, there would not have been many incurred costs other than those to ensure high-quality data, which paradoxically implies that there is no measurable return on that investment. Perhaps we should stop trying to use ROI models and consider the fact that good planning and vigilance might provide ample, yet unremarkable, rewards?

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