Derivation and Validation of a Record Linkage Algorithm between EMS and the Emergency Department

Background: Linking EMS electronic patient care reports (ePCRs) to ED records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR - ED record linkage have had limited success.Objective: To derive and validate an automated record linkage algorithm between EMS ePCR9s and ED records using supervised machine learning.Methods: All consecutive ePCR9s from a single EMS provider between June 2013 and June 2015 were included. A primary reviewer matched ePCR9s to a list of ED patients to create a gold standard. Age, gender, last name, first name, social security number (SSN), and date of birth (DOB) were extracted. Data was randomly split into 80%/20% training and test data sets. We derived missing indicators, identical indicators, edit distances, and percent differences. A multivariate logistic regression model was trained using 5k fold cross-validation, using label k-fold, L2 regularization, and class re-weighting.Results: A total of 14,032 ePCRs were included in the study. Inter-rater reliability between the primary and secondary reviewer had a Kappa of 0.9. The algorithm had a sensitivity of 99.4%, a PPV of 99.9% and AUC of 0.99 in both the training and test sets. DOB match had the highest odd ratio of 16.9, followed by last name match (10.6). SSN match had an odds ratio of 3.8.Conclusions: We were able to successfully derive and validate a probabilistic record linkage algorithm from a single EMS ePCR provider to our hospital EMR.