Validation of a Novel Smartphone-Based Electronic Data Capture System Following Surgical Fixation of Distal Radial Fractures.

Abstract

Large prospective institutional data provide the opportunity to conduct level II and III studies using robust methodologies and adequately powered sample-sizes, while circumventing limitations of retrospective databases. We aimed to validate a prospective data collection tool, the Orthopaedic Minimal Data Set Episode of Care (OME), implemented at a tertiary North American health care system for distal radial fracture (DRF) open reduction and internal fixation (ORIF). The first 100 DRF ORIFs performed after OME inception (February 2015) were selected for this validation study. A blinded review of the operative notes and charts was performed, and extracted data of 75 perioperative DRF ORIF procedure variables were compared with OME collected data for agreement. Outcomes included completion rates and agreement measures in OME versus electronic medical record (EMR)-based control datasets. Data counts were evaluated using raw percentages and McNemar tests. Cohen (κ) and concordance correlation coefficient analyzed categorical and numerical variable agreement, respectively. Overall, OME demonstrated superior completion and agreement parameters versus EMR-based retrospective review. Nine data points (12.0%) demonstrated significantly higher completion rates within the OME dataset (P < .05, each), and 88% (66/75) of captured variables demonstrated similar completion rates. Up to 80.0% (60/75) of variables either demonstrated an agreement proportion of ≥0.90 or were solely reported in the OME. Of 33 variables eligible for agreement analyses, 36.4% (12/33) demonstrated almost perfect agreement (κ > 0.80), and 63.6% (21/33) exhibited almost perfect or substantial agreement (κ > 0.60). The OME is a valid and accurate prospective data collection tool for DRF ORIF that is reliably able to match or supersede traditional retrospective chart review. Future investigations could use this tool for large-scale analyses investigating peri/intraoperative DRF ORIF variables.