Rodriguez-Nava et al. present a proof-of-concept study evaluating the use of a secure large language model (LLM) approved for healthcare data for retrospective identification of a specific healthcare-associated infection (HAI)—central line-associated bloodstream infections—from real patient data for the purposes of surveillance.1 This study illustrates a promising direction for how LLMs can, at a minimum, semi-automate or streamline HAI surveillance activities.

A surveillance system for measuring patient-level antimicrobial adverse drug events (ADE) may support stewardship activities, however, design and implementation questions remain. In this national survey, stewardship experts favored simple, laboratory-based ADE definitions although there were tensions between feasibility, ability to identify attribution without chart review, and importance of specific ADE.

The COVID-19 has had major direct (e.g., deaths) and indirect (e.g., social inequities) effects in the United States. While the public health response to the epidemic featured some important successes (e.g., universal masking ,and rapid development and approval of vaccines and therapeutics), there were systemic failures (e.g., inadequate public health infrastructure) that overshadowed these successes. Key deficiency in the U.S. response were shortages of personal protective equipment (PPE) and supply chain deficiencies. Recommendations are provided for mitigating supply shortages and supply chain failures in healthcare settings in future pandemics. Some key recommendations for preventing shortages of essential components of infection control and prevention include increasing the stockpile of PPE in the U.S. National Strategic Stockpile, increased transparency of the Stockpile, invoking the Defense Production Act at an early stage, and rapid review and authorization by FDA/EPA/OSHA of non-U.S. approved products. Recommendations are also provided for mitigating shortages of diagnostic testing, medications and medical equipment.

Throughout the COVID-19 pandemic, many areas in the United States experienced healthcare personnel (HCP) shortages tied to a variety of factors. Infection prevention programs, in particular, faced increasing workload demands with little opportunity to delegate tasks to others without specific infectious diseases or infection control expertise. Shortages of clinicians providing inpatient care to critically ill patients during the early phase of the pandemic were multifactorial, largely attributed to increasing demands on hospitals to provide care to patients hospitalized with COVID-19 and furloughs.1 HCP shortages and challenges during later surges, including the Omicron variant-associated surges, were largely attributed to HCP infections and associated work restrictions during isolation periods and the need to care for family members, particularly children, with COVID-19. Additionally, the detrimental physical and mental health impact of COVID-19 on HCP has led to attrition, which further exacerbates shortages.2 Demands increased in post-acute and long-term care (PALTC) settings, which already faced critical staffing challenges difficulty with recruitment, and high rates of turnover. Although individual healthcare organizations and state and federal governments have taken actions to mitigate recurring shortages, additional work and innovation are needed to develop longer-term solutions to improve healthcare workforce resiliency. The critical role of those with specialized training in infection prevention, including healthcare epidemiologists, was well-demonstrated in pandemic preparedness and response. The COVID-19 pandemic underscored the need to support growth in these fields.3 This commentary outlines the need to develop the US healthcare workforce in preparation for future pandemics.

Throughout history, pandemics and their aftereffects have spurred society to make substantial improvements in healthcare. After the Black Death in 14th century Europe, changes were made to elevate standards of care and nutrition that resulted in improved life expectancy.1 The 1918 influenza pandemic spurred a movement that emphasized public health surveillance and detection of future outbreaks and eventually led to the creation of the World Health Organization Global Influenza Surveillance Network.2 In the present, the COVID-19 pandemic exposed many of the pre-existing problems within the US healthcare system, which included (1) a lack of capacity to manage a large influx of contagious patients while simultaneously maintaining routine and emergency care to non-COVID patients; (2) a “just in time” supply network that led to shortages and competition among hospitals, nursing homes, and other care sites for essential supplies; and (3) longstanding inequities in the distribution of healthcare and the healthcare workforce. The decades-long shift from domestic manufacturing to a reliance on global supply chains has compounded ongoing gaps in preparedness for supplies such as personal protective equipment and ventilators. Inequities in racial and socioeconomic outcomes highlighted during the pandemic have accelerated the call to focus on diversity, equity, and inclusion (DEI) within our communities. The pandemic accelerated cooperation between government entities and the healthcare system, resulting in swift implementation of mitigation measures, new therapies and vaccinations at unprecedented speeds, despite our fragmented healthcare delivery system and political divisions. Still, widespread misinformation or disinformation and political divisions contributed to eroded trust in the public health system and prevented an even uptake of mitigation measures, vaccines and therapeutics, impeding our ability to contain the spread of the virus in this country.3 Ultimately, the lessons of COVID-19 illustrate the need to better prepare for the next pandemic. Rising microbial resistance, emerging and re-emerging pathogens, increased globalization, an aging population, and climate change are all factors that increase the likelihood of another pandemic.4

The Society for Healthcare Epidemiology in America (SHEA) strongly supports modernization of data collection processes and the creation of publicly available data repositories that include a wide variety of data elements and mechanisms for securely storing both cleaned and uncleaned data sets that can be curated as clinical and research needs arise. These elements can be used for clinical research and quality monitoring and to evaluate the impacts of different policies on different outcomes. Achieving these goals will require dedicated, sustained and long-term funding to support data science teams and the creation of central data repositories that include data sets that can be “linked” via a variety of different mechanisms and also data sets that include institutional and state and local policies and procedures. A team-based approach to data science is strongly encouraged and supported to achieve the goal of a sustainable, adaptable national shared data resource.

Clinical guidelines recommend device removal for cardiovascular implantable electronic device (CIED) infection management. In this retrospective, nationwide cohort, 60.8% of CIED infections received guideline-concordant care. One-year mortality was higher among those without procedural management (25% vs 16%). Factors associated with receipt of device procedures included pocket infections and positive microbiology.

Effective de-implementation models often include replacement of an ineffective practice with an alternative. We co-developed patient education materials as a replacement strategy for inappropriate post-procedural antibiotics in cardiac device procedures. Lessons learned and developed materials may be used to promote infection prevention in other periprocedural settings.

Early in the pandemic, pre-print servers sped rapid evidence sharing. A collaborative of major medical journals supported their use to ensure equitable access to scientific advancements. In the intervening three years, we have made major advancements in the prevention and treatment of COVID-19 and learned about the benefits and limitations of pre-prints as a mechanism for sharing and disseminating scientific knowledge.

Pre-prints increase attention, citations, and ultimately impact policy, often before findings are verified. Evidence suggests that pre-prints have more spin relative to peer-reviewed publications. Clinical trial findings posted on pre-print servers do not change substantially following peer-review, but other study types (e.g., modeling and observational studies) often undergo substantial revision or are never published.

Nuanced policies about sharing results are needed to balance rapid implementation of true and important advancements with accuracy. Policies recommending immediate posting of COVID-19-related research should be re-evaluated, and standards for evaluation and sharing of unverified studies should be developed. These may include specifications about what information is included in pre-prints and requirements for certain data quality standards (e.g., automated review of images and tables); requirements for code release and sharing; and limiting early postings to methods, results, and limitations sections.

Academic publishing needs to innovate and improve, but assessments of evidence quality remains a critical part of the scientific discovery and dissemination process.

The national influenza pandemic response plan includes short-term school closures as an infection mitigation measure, based on modeling data regarding the role of pediatric populations and schools as drivers of disease spread. Modeled estimates regarding the role of children and their in-school contacts as drivers of community transmission of endemic respiratory viruses were used in part to justify prolonged school closures throughout the United States. However, disease transmission models extrapolated from endemic pathogens to novel ones may underestimate the degree to which spread is driven by population immunity and overestimate the impact of school closures as a means of reducing child contacts, particularly in the longer-term. These errors, in turn, may have caused incorrect estimations about the potential benefits of closing schools on a society level while simultaneously failing to account for the significant harms of long-term educational disruption.

Pandemic response plans need to be updated to include nuances regarding drivers of transmission such as pathogen type, population immunity, and contact patterns, and disease severity in different groups. Expected duration of impact also needs to be considered, recognizing that effectiveness of different interventions, particularly those focused on limiting social interactions, are short-lived. Additionally, future iterations should include risk–benefit assessments. Interventions that are particularly harmful to certain groups, such as school closures are on children, should be de-emphasized and time limited. Finally, pandemic responses should include ongoing and continuous policy re-evaluation and should include a clear plan for de-implementation and de-escalation.

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.

Infection surveillance is one of the cornerstones of infection prevention and control. Measurement of process metrics and clinical outcomes, such as detection of healthcare-associated infections (HAIs), can be used to support continuous quality improvement. HAI metrics are reported as part of the CMS Hospital-Acquired Conditions Program, and they influence facility reputation and financial outcomes.

The rich and complex electronic health record presents promise for expanding infection detection beyond currently covered settings of care. Here, we review the “how to” of leveraging electronic data sources to expand surveillance to settings of care and infections that have not been the traditional purview of the National Healthcare Safety Network (NHSN), including a discussion of creation of objective and reproducible infection surveillance definitions. In pursuit of a ‘fully automated’ system, we also examine the promises and pitfalls of leveraging unstructured, free-text data to support infection prevention activities and emerging technological advances that will likely affect the practice of automated infection surveillance. Finally, barriers to achieving a completely ‘automated’ infection detection system and challenges with intra- and interfacility reliability and missing data are discussed.