Unique patient identification within health services is an operational challenge in healthcare settings. Use of key identifiers, such as patient names, hospital identification numbers, national ID, and birth date are often inadequate for ensuring unique patient identification. In addition approximate string comparator algorithms, such as distance-based algorithms, have proven suboptimal for improving patient matching, especially in low-resource settings. Biometric approaches may improve unique patient identification. However, before implementing the technology in a given setting, such as health care, the right scanners should be rigorously tested to identify an optimal package for the implementation. This study aimed to investigate the effects of factors such as resolution, template size, and scan capture area on the matching performance of different fingerprint scanners for use within health care settings. Performance analysis of eight different scanners was tested using the demo application distributed as part of the Neurotech Verifinger SDK 6.0.