Novel Artificial Intelligence Tool for Real-time Patient Identification to Prevent Misidentification in Health Care

Abstract

Purpose: Errors in the identification of true patients in a health-care facility may result in the wrong dose or dosage being given to the wrong patient at the wrong site during radiotherapy sessions, radiopharmaceutical administration, radiological scans, etc. The aim of this article is to reduce the error in the identification of correct patients by implementation of the Python deep learning-based real-time patient identification program. Materials and Methods: The authors utilized and installed Anaconda Prompt (miniconda 3), Python (version 3.9.12), and Visual Studio Code (version 1.71.0) for the design of the patient identification program. In the field of view, the area of interest is merely face detection. The overall performance of the developed program is accomplished over three steps, namely image data collection, data transfer, and data analysis, respectively. The patient identification tool was developed using the OpenCV library for face recognition. Results: This program provides real-time patient identification information, together with the other preset parameters such as disease site, with a precision of 0.92%, recall rate of 0.80%, and specificity of 0.90%. Furthermore, the accuracy of the program was found to be 0.84%. The output of the in-house developed program as “Unknown” is provided if a patient’s relative or an unknown person is found in restricted region. Interpretation and Conclusions: This Python-based program is beneficial for confirming the patient’s identity, without manual interventions, just before therapy, administering medications, and starting other medical procedures, among other things, to prevent unintended medical and health-related complications that may arise as a result of misidentification.