Diagnostic research on routine care data: prospects and problems.

Abstract

A diagnosis in practice is a sequential process starting with a patient with a particular set of signs and symptoms. To serve practice, diagnostic research should aim to quantify the added value of a test to clinical information that is commonly available before the test will be applied. Routine care databases commonly include all documented patient information, and therefore seem to be suitable to quantify a tests' added value to prior information. It is well known, however, that retrospective use of routine care data in diagnostic research may cause various methodologic problems. But, given the increased attention of electronic patient records including data from routine patient care, we believe it is time to reconsider these problems. We discuss four problems related to routine care databases. First, most databases do not label patients by their symptoms or signs but by their final diagnosis. Second, in routine care the diagnostic workup of a patient is by definition determined by previous diagnostic (test) results. Therefore, routinely documented data are subject to so-called workup bias. Third, in practice, the reference test is always interpreted with knowledge of the preceding test information, such that in scientific studies using routine data the diagnostic value of a test under evaluation is commonly overestimated. Fourth, routinely documented databases are likely to suffer from missing data. Per problem we discuss methods that are presently available and may (partly) overcome each problem. All this could contribute to more frequent and appropriate use of routine care data in diagnostic research. The discussed methods to overcome the above problems may well be similarly useful to prospective diagnostic studies.