BACKGROUND

The Enhanced Recovery After Surgery (ERAS) Interactive Audit System (EIAS) is a retrospective database containing information about the pre-, intra-, and post-operative components of surgical patient care. EIAS was created to allow centers that have adopted ERAS protocols to assess their performance. To have confidence in the data collected by EIAS, its completeness, accuracy and validity must be assessed. This study aims to assess the validity of the Alberta EIAS when compared to the gold standard measurement for patient data, the patient electronic medical record (EMR).

METHODS

Four sites that implemented ERAS across Alberta were included, with 20 to 60 patient EMRs pulled from each site. Data on 12 pre-specified ERAS elements and three outcome variables was abstracted from patient EMRs and compared to the corresponding variables from EIAS. Validation criteria included (I) accuracy (agreement between EMR and EIAS) and (II) missingness (percent of data that was missing in patients EMR and EIAS). The estimates of accuracy were compared to estimates of accuracy from two other EIAS validation studies using meta-analysis.

RESULTS

A total of 113 patient charts were reviewed across four sites. The mean agreement between chart review and EIAS was 73.6% (standard deviation, SD = 14.5) with a mean sensitivity of 70.3 (SD = 32.8) and mean specificity of 50.1 (SD = 42.5). Agreement between chart review and EIAS was better among outcomes (agreement for re-operation was 93.7%) than it was for accuracy of documentation of the ERAS elements (mean agreement = 73.6%). Agreement varied by site (68.5% to 94.4%) and reviewer (68.0% to 96.6%). Across all 12 ERAS elements and three outcome variables, a mean of 11.4% of data were missing, with re-operation having the greatest proportion of missing data (15.9%) and termination of drains and early mobilization with the lowest proportion of missing data (9.7%). Estimates of accuracy were not different between studies (I = 56.4%, p = 0.101).

CONCLUSIONS

In Alberta, the accuracy and completeness of EIAS data is similar to that of Sweden and Switzerland, but is varied. This study found that data abstractors that are medically trained, and trained in standardized data abstraction are important determinants of generating high quality data, highlighting the need for adequate resources for data collection.