BIOMETRIC-BASED PATIENT IDENTIFICATION AND AUTHENTICATION IN DIGITAL EMERGENCY HEALTHCARE SYSTEMS

Varshini Arun, Sonaali S, Keerthan V, Poorvi L, Lavanya K, Arya J Gowda, Keerthisri Radha Rammohan, Akash D.V

Misidentification of patients in emergency healthcare is a very serious issue that is a source of diagnostic errors, medication errors, and avoidable deaths. This paper discusses the six biometric modalities, which include fingerprint recognition, facial imaging, iris scanning, palm recognition, ECG biometrics, and multimodal systems and their accuracy, authentication efficiency, emergency suitability, and compatibility with electronic health records (EHRs). A conceptual multimodal biometric emergency authentication framework is suggested through a systematic review of 22 peer-reviewed articles obtained in IEEE databases and PubMed, including AI-based matching algorithms, role-based access levels in EHR and blockchain audit logs. Results show biometric systems minimize error of identification by about 83.5%. Facial recognition was identified as the most emergency-appropriate modality as it is non-contact, whereas BAPPIS fingerprint system had 96.9% protocol adherence with no false positives. The use of multimodal systems (facial, fingerprint, and iris recognition) was shown to be more robust under adverse conditions. Some of the major issues are that the identification of patients unconsciously is impossible, biometric privacy cannot be recovered, and the lack of regulatory frameworks.

Keywords –Biometric authentication, patient identification, emergency healthcare, multimodal biometrics, facial recognition, fingerprint recognition, EHR integration, blockchain audit logs, AI matching algorithms, patient safety.