One of the many promises of EHRs is that, in fairly short order, they’re going to make an ever-growing amount of data available in the quest for better population health management.
But how realistic is that promise?
As this academic sees it, “there is sometimes unbridled enthusiasm that the data captured in clinical systems, perhaps combined with research data such as gene sequencing, will effortlessly provide us knowledge of what works in healthcare and how new treatments can be developed. . . . I honestly share in this enthusiasm, but I also realize that it needs to be tempered, or at least given a dose of reality. In particular, we must remember that our great data analytics and algorithms will only get us so far. If we have poor underlying data, the analyses may end up misleading us. We must be careful for problems of data incompleteness and incorrectness.”
From there he goes on to cite a number of reasons for poor data capture. “Probably the main one,” he says, “is that those who enter data, i.e., physicians and other clinicians, are usually doing so for reasons other than data analysis.”
Adding to the list, he says, “I also know of many clinicians whose enthusiasm for entering correct and complete data is tempered by their view of the entry of it as a data black hole. That is, they enter data in but never derive out its benefits. . . . (A) common complaint I hear from clinicians is that data capture priorities are more driven by the hospital or clinic trying to maximize their reimbursement than to aid clinicians in providing better patient care.”