A Novel QR Code–Based Solution for Secure Electronic Health Record Transfer in Venous Thromboembolism Home Rehabilitation Management: Algorithm Development and Validation

Abstract

Background: Venous thromboembolism (VTE) is a common vascular disorder requiring extended anticoagulation therapy postdischarge to reduce recurrence risk. Home rehabilitation management systems that use electronic health records from hospital care provide opportunities for continuous patient monitoring. However, transferring medical data from clinical to home settings raises significant concerns about privacy and security. Conventional methods such as manual data entry, optical character recognition, and dedicated data transmission lines face notable technical and operational challenges. Objective: This study aims to develop a QR code–based security transmission algorithm using Avro and byte pair encoding (BPE). The algorithm supports the secure creation and transfer of out-of-hospital health records by enabling patients to scan QR codes via a dedicated mobile app, ensuring data security and user privacy. Methods: Between January and October 2024, 300 hospitalized patients with VTE were recruited at the Sixth Medical Center of the Chinese PLA General Hospital. Post discharge, participants used a home rehabilitation app tailored for VTE management. The QR code–based security transmission algorithm was developed to securely transfer in-hospital electronic health records to the out-of-hospital app. It uses BPE, Avro, and Gzip for optimized data compression and uses ChaCha20 and BLAKE3 for encryption and authentication. Specifically, BPE tokenizes medical text, while Avro serializes JSON (JavaScript Object Notation) objects, contributing to data encryption. A proprietary tokenizer was trained, and compression efficiency was evaluated using a “Performance Benchmark Dataset.” Comparative analyses were conducted to assess the compression efficiency of JSON serialization methods (Avro and ASN.1 [Abstract Syntax Notation One]), and tokenization algorithms (BPE and unigram). Results: The dataset consisted of JSON files from 300 patients, averaging 240.1 fields per file (range 89‐623) and 7095 bytes in size (range 2748‐17,425 bytes). Using the BPE + Avro + Gzip algorithm, the average file size was reduced to 1048 bytes, achieving a compression ratio of 6.67. This was 1.82 times more efficient than traditional Gzip compression (average file size: 1907 bytes; compression ratio: 3.66; P