Identifying and categorizing spurious weight data in electronic medical records

Spurious weights compromise the validity of summary measures, such as averages and trends. Even rare errors in weight records can undermine the utility of electronic medical record (EMR) data.

We sought to estimate the prevalence of spurious weight values in a large EMR, to ascertain the likely causes, and to develop and test straightforward algorithms for identifying spurious weight data.

Using EMR data from 10,000 randomly selected patients aged ≥65 y in the VA system, we examined the percentage of weight change across various time intervals, from 1 to 3000 d. We examined descriptive results and developed 3 algorithms to categorize degree of weight change over time. On the basis of distributions, we identified cases that were most likely spurious. We manually reviewed these and categorized the type of error.

The data followed the expected distributions. The algorithms reliably identified spurious weight. Approximately 0.8% of all weights in the record appeared to be spurious and ∼1 in 5 patient charts included ≥1 spurious weight value. The most common type of error involved the misentry of a single digit (e.g., 148 for 178).

Spurious weights are common in EMRs. Straightforward algorithms can identify and remove them, and thus enhance the reliability of EMR data.