Enhancing healthcare data integrity through biometric identification systems: adoption and utilisation in primary healthcare facilities in Lusaka, Zambia

Background Biometric identification systems, which rely on unique physical or behavioural traits such as fingerprints and facial recognition, have emerged as transformative tools for enhancing data accuracy in healthcare. These technologies aim to reduce duplicate medical records, improve patient identification, and ensure timely access to accurate health histories. Despite these benefits, implementation in low-resource settings faces significant hurdles, including technical limitations, infrastructure gaps, and social resistance. This study explored the adoption and utilisation of biometric identification systems in primary healthcare facilities in Lusaka, Zambia, through the lens of the Technology Acceptance Model (TAM), while proposing an extension to reflect health system realities better. This study also extended the Technology Acceptance Model (TAM) by incorporating contextual health system dimensions such as infrastructure, governance, and training support. Methods A qualitative, exploratory, phenomenological design was employed across four health facilities: Chawama, Chilenje, Kanyama First Level Hospitals, and Railways Urban Clinic. Using purposive sampling, 42 in-depth interviews (IDIs) and 4 focus group discussions (FGDs) were conducted with healthcare workers, administrative staff, Ministry of Health officials, insurance service providers, and end-users (patients) with direct experience using biometric systems. Audio recordings were transcribed verbatim and analysed thematically using NVivo 12. Themes focused on workflow efficiency, healthcare delivery, user experience, and implementation challenges. Results Biometric systems enhanced the accuracy of patient identification, sped up medical record retrieval, and decreased administrative errors, thereby streamlining service delivery. Participants observed improvements in data security and a decline in duplicate records. Patients experienced shorter waiting times and better service quality. However, issues such as insufficient infrastructure, technical outages, digital literacy challenges, and cultural misconceptions were recognised. Stakeholders stressed the importance of phased implementations, organised training, and strong data protection frameworks. Based on these insights, the study recommends a revised Consolidated Technology Acceptance Model (TAM) that includes a health systems dimension (infrastructure, capacity building, policy, governance, and inclusiveness), addressing limitations of the traditional TAM framework. Conclusion This study demonstrates that biometric systems can significantly enhance healthcare data integrity and service efficiency in primary healthcare settings. However, for successful implementation, strategic investments in infrastructure, digital literacy, and governance are essential. The extended TAM framework offers a more holistic approach to understanding technology adoption in health systems, particularly in resource-constrained environments.