Abstract

Background: Effective healthcare-associated infection (HAI) prevention requires multiple complex interventions (e.g., hand hygiene, environmental cleaning, personal protective equipment use, and evidence-based patient procedure protocols such as central line maintenance). The COVID-19 pandemic’s effects on healthcare (e.g., staffing shortages, supply chain disruptions, modified protocols to reduce exposure) also impacted HAI prevention. Retrospective analyses have found differential impacts on HAI rates during the pandemic, with some HAIs increasing and others decreasing. However, there has not yet been an assessment of how the COVID-19 pandemic specifically impacted guideline-concordant HAI prevention practices. Significance: This pilot project directly addresses HSR&D’s research priority to enhance the Quality and Safety of Health Care by gathering critical information regarding the implementation of HAI prevention practices during the COVID-19 pandemic. Information gathered here will inform the effective implementation of HAI prevention activities, including resuming guideline-concordant HAI prevention practices, to reduce HAIs across VA and increase the quality and safety of the healthcare received by Veterans. Innovation and Impact: We will conduct a work systems analysis using the Systems Engineering Initiative for Patient Safety (SEIPS) framework, which allows for the assessment of barriers and facilitators within and between each individual work system element (people, tools and technology, task, organization, and environment). This granular analysis allows for identifying and addressing specific barriers to implementation, which can be difficult to identify in complex work systems like those in HAI prevention. By addressing these barriers, we can develop implementation strategies to support the resuming of guideline-concordant HAI prevention practices in work systems that were or are continuing to be affected by COVID-19-related impacts. Specific Aims: In this pilot project, we aim to: 1. Conduct a work systems analysis of HAI prevention during the COVID-19 pandemic among acute inpatient care facilities in VISN12 to identify specific impacts of the pandemic on HAI prevention and HCW-reported needs for improved HAI prevention during the pandemic. 2. Identify barriers and facilitators to resuming IPC best practices in the COVID endemic era. Methodology: We will conduct semi-structured interviews with 1) 1-2 HAI prevention stakeholders and 2) up to 5 frontline nursing personnel at each of the 8 VA medical centers in VISN12. We will use interview guides structured around the CDC’s multidrug-resistant organism prevention strategies and the SEIPS framework to probe pandemic-related changes to HAI prevention practices as well as reasons behind these changes and barriers to resuming practices. We will use a rapid qualitative inquiry approach to analyze interview data, ultimately producing 1) a specific list of HAI prevention practices that were substantially modified during the COVID-19 pandemic and require additional implementation support to resuming practices; 2) work system element barriers and facilitators to conducting these practices during the pandemic; and 3) a logic model guiding the development of a larger research project on priority HAI prevention practices. Next Steps/Implementation: This pilot project will identify specific HAI prevention practices requiring additional implementation support, leading directly to a larger research project to gather VA-wide data on the implementation of these practices. We will also then develop and test implementation strategies to maximize effectiveness of the practice in reducing HAIs while minimizing burden on healthcare workers. This work will thus improve VA’s ability to respond to major disruptions to resources and processes (as COVID-19 was) while maintaining the safety and quality of care for Veterans.

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DRA: Health Systems, Infectious Diseases

DRE: TRL - Applied/Translational

Keywords: Best Practices, Practice Patterns/Trends

MeSH Terms: None at this time.