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ABSTRACT Bacterial infection clusters cause high mortality rates and healthcare costs due to excessive therapy and hygiene measures. The aim of this study was to develop an automated real-time PCR-based point-of-care (POC) system with a customized microfluidic chip that facilitates fast detection of bacterial cluster isolates by targeting cluster-specific single-nucleotide polymorphisms (SNPs). For cluster detection of Acinetobacter baumannii, Staphylococcus aureus, and Escherichia coli, nine TaqMan real-time PCR assays targeting cluster-specific SNPs were designed. Additionally, for DNA input control, a universal PCR amplifying the 16S rDNA was constructed. All reactions were implemented into a microfluidic chip that was analyzed by a POC prototype enabling automated sample processing, fluid handling, and signal detection. Performance of the prototype was evaluated using 45 chips loaded with defined bacterial solutions, including swab eluates. For seven PCRs, implementation into the microfluidic chip was successful, leading to correct identification of all SNPs specific for A. baumannii and E. coli cluster isolates and delineation of all non-cluster strains within 70 min. The remaining three reactions failed in the chip, which resulted in misidentification of the S. aureus cluster. Sensitivity, specificity, and accuracy of the prototype were 43%, 88%, and 55%, respectively. The detection limit was PCR dependent and ranged between 103 and 105 colony-forming units/mL. Once optimized, the microfluidic POC system for cluster detection could be applied as time-saving and easy-to-use method to complement whole-genome sequencing efforts during screening for bacterial clusters.IMPORTANCEEspecially in medical facilities, where morbid people are nursed in close distance to each other, pathogenic bacteria can accumulate and spread. To contain such infection clusters, usually time- and labor-intensive large-scale screening assays are conducted, where patients and patient-side surfaces are sampled, and PCR or whole-genome sequencing analyses are conducted to confirm or deny cluster affiliation of cultivated bacteria. Hence, fast solutions with easy application are required to complement the current state-of-the-art technology for cluster surveillance. Here, we developed a fully automated microfluidic point-of-care prototype that identified bacterial cluster isolates within 70 min from bacterial solutions, including swab eluates. The system requires only low hands-on time and can be applied apart from laboratory infrastructures near the patient, which considerably reduces the time from sampling to result. This ensures fast implementation of hygiene measures and quick containment of the infection cluster, which would enhance patients’ safety and outcome.