Real-time genomics using nanopore sequencing offers a promising approach for rapid and accurate detection of antibiotic resistance in clinical settings. This study demonstrates how real-time genomics can enhance the accuracy of antibiotic resistance profiling in complex infections, particularly in identifying low-abundance plasmid-mediated resistance that conventional methods often miss. The research focuses on a case of multi-drug resistant Klebsiella pneumoniae infection, where real-time genomics detected a novel antibiotic resistance gene variant on low-abundance plasmids, which was not identified by traditional diagnostics. This finding has significant implications for clinical decision-making and patient outcomes. The World Health Organization has identified antibiotic resistance as one of the top global health threats, with resistant infections leading to higher mortality and morbidity due to delayed or inappropriate therapy. Real-time genomics, powered by nanopore sequencing, can expedite pathogen identification and antibiotic resistance profiling directly within clinical settings. However, its accuracy in predicting antibiotic resistance must be compared with established diagnostic methods to ensure its integration into routine clinical practice. The study compared the performance of clinically established diagnostics with real-time genomics-based predictions using bacterial isolates from the same infection case. Traditional methods, such as MALDI-TOF MS and VITEK2, identified a carbapenem-resistant K. pneumoniae isolate with KPC as the resistance mechanism. However, real-time genomics detected a novel KPC variant, blaKPC-14, which conferred resistance to Ceftazidime-Avibactam (CAZ-AVI), leading to a change in treatment. Despite this, the patient's condition deteriorated, and the patient ultimately passed away. Real-time genomics enabled the detection of low-abundance resistance genes, such as blaKPC-14, which were not identified by traditional methods. This highlights the potential of real-time genomics to improve clinical decision-making and patient outcomes by providing accurate and rapid resistance profiles. The study also demonstrated the ability of nanopore sequencing to produce long reads, enabling the creation of highly accurate genome assemblies for strain-level identification and de novo detection of bacterial pathogens and their resistance profiles. The integration of real-time genomics into clinical practice has the potential to transform the management of complex infections, particularly in low- and middle-income settings where advanced diagnostic equipment may not be readily available. The study underscores the importance of combining real-time genomics with clinically established approaches to enhance the accuracy of antibiotic resistance prediction and improve patient care.Real-time genomics using nanopore sequencing offers a promising approach for rapid and accurate detection of antibiotic resistance in clinical settings. This study demonstrates how real-time genomics can enhance the accuracy of antibiotic resistance profiling in complex infections, particularly in identifying low-abundance plasmid-mediated resistance that conventional methods often miss. The research focuses on a case of multi-drug resistant Klebsiella pneumoniae infection, where real-time genomics detected a novel antibiotic resistance gene variant on low-abundance plasmids, which was not identified by traditional diagnostics. This finding has significant implications for clinical decision-making and patient outcomes. The World Health Organization has identified antibiotic resistance as one of the top global health threats, with resistant infections leading to higher mortality and morbidity due to delayed or inappropriate therapy. Real-time genomics, powered by nanopore sequencing, can expedite pathogen identification and antibiotic resistance profiling directly within clinical settings. However, its accuracy in predicting antibiotic resistance must be compared with established diagnostic methods to ensure its integration into routine clinical practice. The study compared the performance of clinically established diagnostics with real-time genomics-based predictions using bacterial isolates from the same infection case. Traditional methods, such as MALDI-TOF MS and VITEK2, identified a carbapenem-resistant K. pneumoniae isolate with KPC as the resistance mechanism. However, real-time genomics detected a novel KPC variant, blaKPC-14, which conferred resistance to Ceftazidime-Avibactam (CAZ-AVI), leading to a change in treatment. Despite this, the patient's condition deteriorated, and the patient ultimately passed away. Real-time genomics enabled the detection of low-abundance resistance genes, such as blaKPC-14, which were not identified by traditional methods. This highlights the potential of real-time genomics to improve clinical decision-making and patient outcomes by providing accurate and rapid resistance profiles. The study also demonstrated the ability of nanopore sequencing to produce long reads, enabling the creation of highly accurate genome assemblies for strain-level identification and de novo detection of bacterial pathogens and their resistance profiles. The integration of real-time genomics into clinical practice has the potential to transform the management of complex infections, particularly in low- and middle-income settings where advanced diagnostic equipment may not be readily available. The study underscores the importance of combining real-time genomics with clinically established approaches to enhance the accuracy of antibiotic resistance prediction and improve patient care.