EPMS: A Framework for Large-Scale Patient Matching

The healthcare industry, through digitization, is trying to achieve interoperability, but has not been able to achieve complete Health Information Exchange (HIE). One of the major challenges in achieving this is the inability to accurately match patient data. Mismatching of patient records can lead to improper treatment which can prove to be fatal. Also, the presence of duplicate overheads has caused inaccessibility to crucial information in the time of need. Existing solutions to patient matching are both time-consuming and non-scalable. This paper proposes a framework, namely, Electronic Patient Matching System (EPMS), which attempts to overcome these barriers while achieving a good accuracy in matching patient records. The framework encodes the patient records using variational autoencoder and amalgamates them by performing locality sensitive hashing on an Apache spark cluster. This makes the process faster and highly scalable. Furthermore, a fuzzy matching of the records in each block is performed using Levenshtein distances to identify the duplicate patient records. Experimental investigations were performed on a synthetically generated dataset consisting of 44555 patient records. The proposed framework achieved a matching accuracy of 81.15% on this dataset.