Analysis of plasma cell-free DNA (cfDNA) may provide important information in cancer research, though the often small fraction of DNA originating from tumor cells makes the analysis technically challenging. Digital droplet PCR (ddPCR) has been utilized extensively as sufficient technical performance is easily achieved, but analysis is restricted to few mutations. Next generation sequencing (NGS) approaches have been optimized to provide comparable technical performance, especially with the introduction of unique identifiers (UIDs). However, the parameters influencing data quality when utilizing UIDs are not fully understood. In this study, we applied a targeted NGS approach to 65 plasma samples from bladder cancer patients. Laboratory and bioinformatics parameters were found to influence data quality when using UIDs. We successfully sequenced 249 unique DNA fragments on average per genomic position of interest using a 225 kb gene panel. Validation identified 24 of 38 mutations originally identified using ddPCR across several plasma samples. In addition, four mutations detected in associated tumor samples were detected using NGS, but not using ddPCR. CfDNA analysis of consecutively collected plasma samples from a bladder cancer patient indicated earlier detection of recurrence compared to radiographic imaging. The insights presented here may further the technical advancement of NGS mediated cfDNA analysis.