The past 10 years have brought paradigm-shifting changes to clinical microbiology. This paper explores the top 10 transformative innovations across the diagnostic spectrum, including not only state of the art technologies but also preanalytic and post-analytic advances. Clinical decision support tools have reshaped testing practices, curbing unnecessary tests. Innovations like broad-range polymerase chain reaction and metagenomic sequencing, whole genome sequencing, multiplex molecular panels, rapid phenotypic susceptibility testing, and matrix-assisted laser desorption ionization time-of-flight mass spectrometry have all expanded our diagnostic armamentarium. Rapid home-based testing has made diagnostic testing more accessible than ever. Enhancements to clinician-laboratory interfaces allow for automated stewardship interventions and education. Laboratory restructuring and consolidation efforts are reshaping the field of microbiology, presenting both opportunities and challenges for the future of clinical microbiology laboratories. Here, we review key innovations of the last decade.

Advancement in solid organ transplantation and hematopoietic stem cell transplant continues to improve the health outcomes of patients and widens the number of eligible patients who can benefit from the medical progress. Preserving the effectiveness of antimicrobials remains crucial, as otherwise transplant surgeries would be unsafe due to surgical site infections, and the risk of sepsis with neutropenia would preclude stem cell transplant. In this review, we provide updates on three previously discussed stewardship challenges: febrile neutropenia, Clostridioides difficile infection, and asymptomatic bacteriuria. We also offer insight into four new stewardship challenges: the applicability of the “shorter is better” paradigm shift to antimicrobial duration; antibiotic allergy delabeling and desensitization; colonization with multidrug-resistant gram-negative organisms; and management of cytomegalovirus infections. Specifically, data are accumulating for “shorter is better” and antibiotic allergy delabeling in transplant patients, following successes in the general population. Unique to transplant patients are the impact of multidrug-resistant organism colonization on clinical decision-making of antibiotic prophylaxis in transplant procedure and the need for antiviral stewardship in cytomegalovirus. We highlighted the expansion of antimicrobial stewardship interventions as potential solutions for these challenges, as well as gaps in knowledge and opportunities for further research.

Chat Generative Pre-trained Transformer (ChatGPT), the flagship generative artificial intelligence (AI) chatbot by OpenAI, is transforming many things in medicine, from healthcare and research to medical education. It is anticipated to integrate in many aspects of the medical industry, and we should brace for this inevitability and use it to our advantage. Here are proposed ways you can use ChatGPT in medicine with some specific use cases in antimicrobial stewardship and hospital epidemiology.

We tested 85 isolates of β-hemolytic Streptococcus spp. against trimethoprim/sulfamethoxazole (TMP/SMX), clindamycin, and doxycycline by broth microdilution (BMD) and BD Phoenix. Susceptibility rates via BMD for TMP/SMX, clindamycin, and doxycycline were 100%, 85.5%, and 56.6%, respectively. TMP/SMX is a potential monotherapy agent for β-hemolytic Streptococcus skin and soft tissue infections.

Current methods of emergency-room–based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true “Learning Healthcare System” that will support near–real-time quality improvement efforts and advance the scientific basis for the practice of infection control.

The evidence base for refraining from screening for or treating asymptomatic bacteriuria (ASB) in older adults is strong, but both practices remain prevalent. Clinical confusion over how to respond to a change from baseline, when to order a urinalysis and urine culture, and what to do with a positive urine culture fuels unnecessary antibiotic use for ASB. If the provider can take a mindful pause to apply evidenced-based assessment tools, the resulting increased clarity in how to manage the situation can reduce overtreatment of ASB.

Current guidelines do not address a recommended duration of parenteral therapy for uncomplicated urinary tract infection (uUTI) treatment in the inpatient setting. We compared a 3-day course of ceftriaxone with longer antibiotic durations for inpatients with a uUTI. Our findings indicate that a 3-day course of ceftriaxone was as efficacious as longer antibiotic courses.

In this large, retrospective cohort study, we used administrative data to evaluate nonpregnant adults with group B Streptococcus (GBS) bacteriuria. We found greater all-cause mortality in those with urinary tract infections compared to asymptomatic bacteriuria. Differences in patients’ baseline characteristics and the 1-year mortality rate raise the possibility that provider practices contribute to differences observed.

Mixed flora in urine cultures usually occur due to preanalytic contamination. In our outpatient urology clinic, we detected a high prevalence of mixed flora (46.2%), which was associated with female sex and older age. Patient education did not influence the rate of mixed flora. Future efforts should target high-risk patients.

Emergency preparedness programs have evolved over the last several decades as communities have responded to natural, intentional, and accidental disasters. This evolution has resulted in a comprehensive all-hazards approach centered around 4 fundamental phases spanning the entire disaster life cycle: mitigation, preparedness, response, and recovery. Increasing frequency of outbreaks and epidemics of emerging and reemerging infectious diseases in the last decade has emphasized the significance of healthcare emergency preparedness programs, but the coronavirus disease 2019 (COVID-19) pandemic has tested healthcare facilities’ emergency plans and exposed vulnerabilities in healthcare emergency preparedness on a scale unexperienced in recent history. We review the 4 phases of emergency management and explore the lessons to be learned from recent events in enhancing health systems capabilities and capacities to mitigate, prepare for, respond to, and recover from biological threats or events, whether it be a pandemic or a single case of an unknown infectious disease. A recurring cycle of assessing, planning, training, exercising, and revising is vital to maintaining healthcare system preparedness, even in absence of an immediate, high probability threat. Healthcare epidemiologists and infection preventionists must play a pivotal role in incorporating lessons learned from the pandemic into emergency preparedness programs and building more robust preparedness plans.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.

The extensive use of the urinalysis for screening and monitoring in diverse clinical settings usually identifies abnormal urinalysis parameters in patients with no suspicion of urinary tract infection, which in turn triggers urine cultures, inappropriate antimicrobial use, and associated harms like Clostridioides difficile infection. We highlight how urinalysis is misused, and suggest deconstructing it to better align with evolving patterns of clinical use and the differential diagnosis being targeted. Reclassifying the urinalysis components into infectious and non-infectious panels and interpreting urinalysis results in the context of individual patient’s pretest probability of disease is a novel approach to promote proper urine testing and antimicrobial stewardship, and achieve better outcomes.