BLOOD PRESSURE MEASUREMENT ANALYSIS METHOD AND SYSTEM
FIG2 schematically illustrates approaches for determining whether an outlier is attributable to a testing personnel
The approach indicated by (a) above is illustrated in FIG2(a). This schematically illustrates an example distribution 32 of deviation scores of past measurements for a given tester for various subjects, the deviation scores being a deviation of each of the tester measurements from an expected measurement for the subject in question. The expected measurement may for example be an average measurement for the subject based on historical measurements for the subject. The approach indicated by (b) above is illustrated in FIG2(b). This differs from (a) simply in that the average 'error' of the tester's measurements is assessed not by comparison to an average historical measurement for each patient being tested, but by comparison of the whole set of tester measurements on a population of subjects to the set of measurements taken on the same population of subjects by other testers. FIG2(b) schematically illustrates this, illustrating an example distribution 34 of blood pressure measurements for the tester, and on the same axis an example distribution 36 of blood pressure measurements for the same population of subjects, taken by other testers. It can be seen that in this example there is a clear deviation, Δ, in the median/mean centers of the two distributions. This deviation is taken to be the average error value for the tester, as it indicates a systematic error or bias in the measured values for the tester.
schematic
14
method comprises comparing
18
determining
20
output information
34
example distribution
16
accessing
36
example distribution
32
example distribution
['1. A blood pressure measurement analysis method (10), comprising: obtaining (12) a blood pressure measurement for a subject, the measurement being associated with a known tester who performed the measurement; comparing (14) the measurement with historical measurements for the subject stored in a database, and determining based on this whether the obtained measurement is an outlier for the subject; in the case the measurement is an outlier, accessing (16) a database of contextual data for the subject, pertaining to contextual factors which have an effect on blood pressure, and determining based on this any contextual cause of the outlier measurement; at least in the case of no contextual cause, accessing (18) historical measurement data for the tester in a database, and determining based on this whether the outlier is attributable to the tester; generating (20) output information indicative of an outlier status of the measurement, and any determined contextual and/or tester attributed cause.']