A rigorous algorithm to detect and clean inaccurate adult height records within EHR systems

Appl Clin Inform. 2014 Feb 19;5(1):118-26. doi: 10.4338/ACI-2013-09-RA-0074. eCollection 2014.


Background: Height is a critical variable for many biomedical analyses because it is an important component of Body Mass Index (BMI). Transforming EHR height measures into meaningful research-ready values is challenging and there is limited information available on methods for "cleaning" these data.

Objectives: We sought to develop an algorithm to clean adult height data extracted from EHR using only height values and associated ages.

Results: The algorithm we developed is sensitive to normal decreases in adult height associated with aging, is implemented using an open-source software tool and is thus easily modifiable, and is freely available. We checked the performance of our algorithm using data from the Northwestern biobank and a replication sample from the Marshfield Clinic biobank obtained through our participation in the eMERGE consortium. The algorithm identified 1262 erroneous values from a total of 33937 records in the Northwestern sample. Replacing erroneous height values with those identified as correct by the algorithm resulted in meaningful changes in height and BMI records; median change in recorded height after cleaning was 7.6 cm and median change in BMI was 2.9 kg/m(2). Comparison of cleaned EHR height values to observer measured values showed that 94.5% (95% C.I 93.8-% - 95.2%) of cleaned values were within 3.5 cm of observer measured values.

Conclusions: Our freely available height algorithm cleans EHR height data with only height and age inputs. Use of this algorithm will benefit groups trying to perform research with height and BMI data extracted from EHR.

Keywords: Height; body mass index; dimensional measurement accuracy; electronic health record; electronic medical record; phenotyping.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Algorithms*
  • Body Height
  • Electronic Health Records*
  • Female
  • Humans
  • Male
  • Middle Aged