Hostname: page-component-77c89778f8-gq7q9 Total loading time: 0 Render date: 2024-07-20T14:07:57.418Z Has data issue: false hasContentIssue false

Implementing strategies to prevent infections in acute-care settings

Published online by Cambridge University Press:  11 July 2023

Kavita K. Trivedi*
Alameda County Public Health Department, San Leandro, California
Joshua K. Schaffzin
Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, Ontario, Canada
Valerie M. Deloney
Society for Healthcare Epidemiology of America (SHEA), Arlington, Virginia
Kathy Aureden
Advocate Aurora Health, Downers Grove, Illinois
Ruth Carrico
Division of Infectious Diseases, University of Louisville School of Medicine, Louisville, Kentucky
Sylvia Garcia-Houchins
The Joint Commission, Oakbrook Terrace, Illinois
J. Hudson Garrett Jr
Division of Infectious Diseases, University of Louisville School of Medicine, Louisville, Kentucky
Janet Glowicz
Centers for Disease Control and Prevention, Atlanta, Georgia
Grace M. Lee
Stanford Children’s Health, Stanford, California
Lisa L. Maragakis
Johns Hopkins University School of Medicine, Baltimore, Maryland
Julia Moody
Clinical Services Group, HCA Healthcare, Nashville, Tennessee
Ann Marie Pettis
University of Rochester Medicine, Rochester, New York
Sanjay Saint
VA Ann Arbor Healthcare System and University of Michigan, Ann Arbor, Michigan
Marin L. Schweizer
University of Wisconsin–Madison, Madison, Wisconsin
Deborah S. Yokoe
University of California San Francisco School of Medicine, UCSF Medical Center, San Francisco, California
Sean Berenholtz
Clinical Services Group, HCA Healthcare, Nashville, Tennessee
Corresponding author: Kavita K. Trivedi, MD; Email:
Rights & Permissions [Opens in a new window]


This document introduces and explains common implementation concepts and frameworks relevant to healthcare epidemiology and infection prevention and control and can serve as a stand-alone guide or be paired with the “SHEA/IDSA/APIC Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals: 2022 Updates,” which contain technical implementation guidance for specific healthcare-associated infections. This Compendium article focuses on broad behavioral and socio-adaptive concepts and suggests ways that infection prevention and control teams, healthcare epidemiologists, infection preventionists, and specialty groups may utilize them to deliver high-quality care. Implementation concepts, frameworks, and models can help bridge the “knowing-doing” gap, a term used to describe why practices in healthcare may diverge from those recommended according to evidence. It aims to guide the reader to think about implementation and to find resources suited for a specific setting and circumstances by describing strategies for implementation, including determinants and measurement, as well as the conceptual models and frameworks: 4Es, Behavior Change Wheel, CUSP, European and Mixed Methods, Getting to Outcomes, Model for Improvement, RE-AIM, REP, and Theoretical Domains.

SHEA/IDSA/APIC Practice Recommendation
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
© The Author(s), 2023. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

Intended use

This document introduces and explains common implementation concepts and frameworks relevant to healthcare epidemiology and infection prevention and control. It focuses on broad behavioral and socioadaptive concepts and suggests ways that infection prevention and control teams, healthcare epidemiologists, infection preventionists, and specialty groups may utilize them to deliver high-quality care. This article can be used as a standalone document, or it can be paired with the manuscripts of the “Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals: 2022 Updates,” which provide technical guidance on how to implement prevention efforts for specific healthcare-associated infections (HAIs).

Implementation concepts, frameworks, and models can help bridge the “knowing–doing” gap, a term used to describe why practices in healthcare may diverge from those recommended according to evidence. It is not comprehensive; it guides the reader to think about implementation and to find resources suited for a specific setting and circumstances.

It also is not intended to be prescriptive. Implementation as a concept is broad, and success in implementing practices or interventions depends on a systematic approach matched to an organization’s context (ie, local factors, such as operational support, informatics resources, experience, willingness to change, safety culture, and others). This guidance and the HAI-specific Compendium articles’ implementation sections are meant to be a practical starting point to orient readers to concepts and ways to seek further resources. We do not comment on the success or sustainability of any method and refer the reader to resources, including tools and practical tactics, to help with implementation efforts.


This article was researched and written by representatives from each Compendium author panel as well as implementation and healthcare epidemiology subject-matter experts, Dr. Kavita Trivedi and Joshua Schaffzin. Unlike the HAI-prevention articles in the “Compendium of Strategies to Prevent HAIs in Acute Care Hospitals: 2022 Updates,” this Compendium article is not based on a systematic literature search specific to its topic. Instead, the overview of implementation and selection of models, frameworks, and resources is based on implementation articles identified through (1) the systematic literature reviews conducted for each HAI-prevention Compendium section, (2) expert opinion and consensus, (3) practical experience, and (4) published research and resources retrieved by the authors.

Rather than providing practice recommendations, a sample of implementation models and frameworks is provided, selected for their track records in published research, utility in advancing infection prevention and control goals, and/or widespread or broad-based applicability relevant to infection prevention and control aims. A glossary of terms relevant to implementation methodology is also provided.

This document was drafted via email correspondence and video conferences among the authors, and its content was approved by electronic vote. The Compendium Expert Panel of members, with broad healthcare epidemiology and infection prevention expertise, reviewed the draft manuscript. Following review by the Expert Panel, the 5 Compendium Partner organizations, professional organizations with subject-matter expertise, and CDC reviewed the document and submitted comments. After revisions by the authors, it was reviewed and approved by the SHEA Guidelines Committee, the Infectious Diseases Society of America (IDSA) Standards and Practice Guidelines Committee, the American Hospital Association (AHA), and The Joint Commission, and the Boards of SHEA, IDSA, and the Association for Professionals in Infection Control and Epidemiology (APIC).

All panel members complied with SHEA and IDSA policies on conflict-of-interest disclosure.

Rationale and statements of concern

The fields of infection prevention and healthcare epidemiology protect patients and the healthcare personnel (HCP) who care for them from HAIs and other safety risks through evidence-based best practices to improve population health and safety. Reference Gerberding1 Sustained infection prevention relies on lasting adherence to these practices to achieve desired outcomes, accountability in the process, and the application of methodologies to monitor and evaluate knowledge and performance. Regulatory authorities like the Centers for Medicaid and Medicare Services and the Occupational Safety and Health Administration, 2,3 as well as accrediting organizations like The Joint Commission 4 and DNV, 5 require implementation of organizational policies and stated practices, which they have incorporated into survey expectations. 6,7

Eccles and Mittman Reference Eccles and Mittman8 define implementation science as “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice.” Implementation science emerged in the last 20 years to improve patient outcomes and HCP safety. Reference Neta, Brownson and Chambers9,Reference Livorsi, Drainoni and Reisinger10 As a field of study initially developed for industry, its principles have been adapted to integrate evidence-based practices sustainably in healthcare settings. Implementation science identifies generalizable methods and frameworks to increase the utilization of evidence-based interventions deliberately and systematically in healthcare. Various terms have been used to describe the field of implementation science, including the ‘theory-practice gap,’ ‘knowledge transfer,’ and ‘knowledge utilization.’ Reference Saint, Howell and Krein11 Simply put, implementation science provides the tools and frameworks to help translate evidence-based interventions into everyday clinical practice.

Studies in implementation science make it clear that identifying effective interventions is a necessary first step and that transferring them into real-world settings requires an intentional process. Education and training have proven necessary but insufficient for improvement and behavior change. Implementation science directs us to evaluate contextual determinants of behavior to design more successful, customized interventions. Improvement science, a related field, focuses on the local context and provides guidance regarding how to perform trials of new practices rapidly and iteratively to improve care. Reference Leeman, Rohweder and Lee12 These two fields, while having distinct models and terminology, can be aligned and complement each other to improve healthcare services. Reference Leeman, Rohweder and Lee12

HCP and teams often are unable or unprepared to implement best practices given the idiosyncrasies and complexities of healthcare settings. Reference Houghton, Meskell and Delaney13 Identification and application of multifaceted strategies are necessary to ensure progress toward improvement. Reference Ali, Farley, Speck, Catanzaro, Wicker and Berenholtz14,Reference Wolfensberger, Meier, Clack, Schreiber and Sax15

Strategies for implementation


Foundational to any implementation effort is understanding factors that promote or hinder change. Promoting factors are called ‘facilitators’ and hindering factors are ‘barriers.’ Determinants of these factors may be individual, such as the preferences, needs, attitudes, and knowledge of HCP, hospital leaders, patients, and visitors. An individual may be a strong, engaged leader (a facilitator) or an unengaged obstructor (barrier). Determinants may include a team’s composition or ways of communicating, an organization’s culture and capacity, or a system’s policies and resources. Reference Aarons, Hurlburt and Horwitz16 Organizationally, implementation may be facilitated or impeded by expectations and allocation of time (eg, competing priorities, data collection burden, provision of time to dedicate to an effort, fast turnaround at the expense of sustained processes), resources (eg, ease of adapting the EMR, staff capacity, and turnover), and leadership support Reference Saint, Kowalski, Banaszak-Holl, Forman, Damschroder and Krein17 or follower buy-in. Reference Saint, Kowalski, Banaszak-Holl, Forman, Damschroder and Krein18

Facilitators and barriers affect implementation to differing degrees. For example, an individual practitioner may oppose a change (ie, be a barrier), but the supervisor may be able to facilitate to overcome the opposition. Alternatively, a practitioner may champion a change, but without the support of the leadership, they may be unable to initiate it. Additional influential factors include context, level of engagement, and reliability (see Table 1 for a glossary of terms).

Table 1. Glossary of Terms

Failure by HCP to adhere to a guideline or standard is a common basis for initiating an improvement project. The Cabana Framework Reference Cabana, Rand and Powe19 is a useful tool to understand how addressing real or perceived barriers can make an implementation effort successful. The framework employs 3 domains (ie, knowledge, attitude, and behavior) to understand the spectrum of barriers. The Expert Recommendations for Implementing Change (ERIC) Reference Powell, Waltz and Chinman20 is another resource to help map barriers to strategies and identify appropriate implementation models or frameworks.


Data are essential for implementation to establish baselines, identify opportunities, measure progress, and justify use of resources to organizational leaders. No single method or measure will work for all situations, and standardized measures often are not available. Different frameworks lend themselves to specific methodologies, but any chosen method must do the following:

  • Be appropriate for the question(s) it seeks to answer.

  • Adhere to the method’s rules for data collection and analysis. As with any project, it may be prudent to review the analytic plan with an expert to ensure that data collected will yield a result.

Choosing measures

There are 3 general types of measures employed in implementation 21 :

  1. 1. Outcome measure: The ultimate goal of a project, such as reduced surgical site infections or improving antimicrobial susceptibility patterns.

  2. 2. Process measure: The action taken to reach the desired outcome, such as adherence to a prevention bundle or compliance with hand hygiene standards.

  3. 3. Balancing measure: An undesired outcome that could be caused by changing a system, such as increased staff absences due to dry skin from a hand hygiene product or due to side effects from a required vaccine.

Ideally, all 3 types of measures are included in a project. For example, a project seeking to reduce ventilator-associated pneumonia (VAP, ie, outcome) seeks to increase early extubation (ie, process) but needs to ensure a rise in reintubations or unplanned intubations is not occurring (ie, balancing). At times, a balancing measure may be difficult to identify due to the rarity of an event or an indirect relationship between outcome or process and balancing measures. In the VAP example, using the rate of nonventilator hospital-acquired pneumonia (NV-HAP) could help identify patients who develop NV-HAP following extubation (implying they were extubated too early), but not every patient with NV-HAP will have been intubated prior to diagnosis.

Similarly, choosing an outcome measure may be difficult if the likelihood of an outcome is multifactorial or exceedingly rare. In the case of hand hygiene, improved adherence (process) should prevent nosocomial transmission (outcome). However, an overall HAI rate may not reflect the change because hand hygiene is one of many potential factors that affect nosocomial transmission. Also, it may not be possible to count all prevented transmissions. For example, a patient would not be counted if they experienced onset of an upper respiratory infection following discharge but did not require readmission. In the case of antimicrobial resistance, improved contact precaution adherence for multidrug-resistant organism (MDRO) patients (ie, process) and improved antimicrobial stewardship (ie, process) should decrease morbidity and mortality due to antimicrobial resistance (ie, outcome) but may be difficult to demonstrate in a single center or short period. Reference Livorsi, Drainoni and Reisinger10 In these examples, the focus of the project might be the process and balancing measures, with attention to but not reliance on the outcome.

Often, measures that are standardized and utilized broadly are referred to as ‘benchmarks.’ Measures also may be developed locally and used in a combination with benchmark measures. For instance, a facility may start with NHSN event definitions 22 and adapt them as definitions change over time or as needed based on the suitability to their setting (eg, pediatric, long-term care, home healthcare). Reference Advani, Murray, Murdzek, Aniskiewicz and Bizzarro23 As another example, facilities typically use the WHO 5 Moments process measures to identify occasions when HCP should perform hand hygiene during patient care, but the methods of measurement of adherence can vary. A facility may measure adherence to all moments, adherence to a specific moment, or the amount of hand hygiene product used. 24 When possible, it is important to use the least resource-intensive means of data collection because resources are needed to feed data back to those who were monitored. Monitoring in combination with feedback has been shown to influence change and be more effective when delivered at a high frequency. Reference Jamtvedt, Young, Kristoffersen, O’Brien and Oxman25

Choosing a method

Table 2 provides a nonexhaustive list of methodologies commonly used for implementation measurement. For a research-focused overview, readers are encouraged to review the 2016 SHEA series on research methods in healthcare epidemiology and antimicrobial stewardship Reference Morgan, Safdar, Milstone and Anderson26 and Livorsi et al. Reference Livorsi, Drainoni and Reisinger10

Table 2. Methods for Measurement

Conceptual models and frameworks

The choice of implementation methods or frameworks for any given initiative relies on the context, local knowledge and experience with implementation science, and the resources available to support the effort. Numerous frameworks combine implementation principles and tools to help organizations facilitate sustainable improvements (see Table 3 for published uses and associated resources for the models and frameworks described in this document). An organization may utilize a particular implementation framework for its relevance to a specific intervention, setting, and/or need, and another for a different initiative. As a starting point when choosing a framework, an organization may review published evidence to understand what and how framework(s) were used successfully and compare them to their local context. The following additional tools can help guide selection of framework(s):

  • The Consolidated Framework for Implementation Research (CFIR), which provides a repository of constructs that have been associated with effective implementation 27

  • The Expert Recommendations for Implementing Change (ERIC) process Reference Powell, Waltz and Chinman20 (see Table 4 for additional resources).

Table 3. Implementation Frameworks

Table 4. Other Resources

Frameworks may be combined or used on their own and are meant to help guide improvements through a systematic approach derived from behavioral and organizational science and research. Damschroder et al Reference Damschroder, Aron, Keith, Kirsh, Alexander and Lowery28 describe using CFIR to guide formative evaluations and build the implementation knowledge base across multiple studies and settings, distinguishing between descriptive implementation models and action-oriented ones.

The following models and frameworks are used in healthcare, share purposeful experimentation and evaluation to achieve sustainable change, Reference Pennathur and Herwaldt29 and illustrate the variety of ways organizations may approach a problem. These models and frameworks are listed alphabetically.

The 4 “Es”

Pronovost et al articulated the 4 Es (Engage, Educate, Execute, and Evaluate), which may be the most pervasive model used in US healthcare epidemiology. Reference Pronovost, Berenholtz and Needham30 This model is well suited for large-scale projects that include multiple sites. Its cyclical nature allows for formative work and feedback to drive modifications and adaptations, and it provides a guide for resolving knowledge gaps through education. However, it does not include targeted strategies to address multilevel barriers that hinder putting knowledge into practice.

The following 4 E strategies guide organizational change efforts:

  1. 1. Engagement: To motivate key working partners to take ownership and support the proposed interventions.

  2. 2. Education: To ensure key working partners understand why the proposed interventions are important.

  3. 3. Execution: To embed the intervention into standardized care processes.

  4. 4. Evaluation: To understand whether the intervention is successful.

The 4Es guide improvement teams in planning to address key partners for the implementation process: senior hospital leaders, improvement team leaders, and frontline staff. Planning for and utilization of multifaceted interventions that address the 4Es, coupled with explicit efforts to improve teamwork and safety culture, Reference Sexton, Berenholtz and Goeschel31 have been associated with reductions in HAIs Reference Berenholtz, Lubomski and Weeks32Reference Pronovost, Watson, Goeschel, Hyzy and Berenholtz34 and mortality Reference Lipitz-Snyderman, Steinwachs, Needham, Colantuoni, Morlock and Pronovost35 and increased cost savings. Reference Waters, Korn and Colantuoni36

Behavior Change Wheel

The Behavior Change Wheel (BCW) is the result of an effort to link interventions with targeted behaviors more directly. It was developed by Michie et al, Reference Michie, van Stralen and West37 who evaluated 19 existing behavior change frameworks for comprehensiveness (ie, applicability to any intervention), coherence, and link to a behavioral model. The result was a 3-layered tool:

  1. 1. A behavior system composed of capability, opportunity, and motivation (COM-B)

  2. 2. Nine intervention functions that can be used to affect behavioral change

  3. 3. Seven policy categories that enable or support interventions to enact the desired behavior change.

One strength of the model is its nonlinearity, meaning that >1 behavioral system component, intervention function, and policy category can apply to an effort to affect change. Additionally, the model attempts to incorporate contextual influences on behavior, which the authors refer to as ‘automatic’ functions such as emotions and impulses that arise from associative learning and/or innate dispositions, as opposed to more reflective processes involving evaluations and plans. The BCW has been used widely in health promotion efforts such as smoking cessation Reference Gould, Bar-Zeev and Bovill38 and obesity and sedentary behavior reduction, Reference Ojo, Bailey, Brierley, Hewson and Chater39Reference Munir, Biddle and Davies41 and COM-B has been used to investigate hand hygiene adherence Reference Schmidtke and Drinkwater42,Reference Lambe, Lydon and Madden43 and antibiotic prescribing. Reference Tomsic, Ebadi and Gosse44,Reference Courtenay, Rowbotham, Lim, Peters, Yates and Chater45

Comprehensive Unit-based Safety Program (CUSP)

CUSP focuses broadly on the idea of safety culture by empowering HCP to take responsibility for safety in their area, rather than defining specific domains of impact. As described by Pronovost et al Reference Pronovost, Weast and Rosenstein46 at Johns Hopkins, who developed and validated the CUSP model in intensive care settings in 2005, the program is composed of 8 steps:

  1. 1. Culture of safety assessment

  2. 2. Sciences of safety education

  3. 3. Staff identification of safety concerns

  4. 4. Senior executive adoption of a unit

  5. 5. Improvements implemented from safety concerns

  6. 6. Documentation and analysis of efforts

  7. 7. Sharing of results

  8. 8. Culture reassessment.

The US Agency for Healthcare Research & Quality (AHRQ), the federal agency that provided funding for the development of CUSP, maintains an updated version of this framework on its website. Reference Pronovost, Needham and Berenholtz33,47 The AHRQ also funded “On the CUSP: Stop CAUTI,” a national program to reduce the incidence of CAUTI through technical and hospital culture adaptations rooted in the CUSP model. 48 This program focused on culture change. Reference Meddings, Chopra and Saint49 CUSP has also been used in the ambulatory setting, specifically in the AHRQ “Safety Program for Improving Antibiotic Use,” a program derived from CUSP model concepts, designed to reduce overprescribing of antibiotics in primary care. Reference Keller, Caballero and Tamma50 The AHRQ has further extended CUSP into ambulatory surgery, providing a full toolkit for the prevention of surgical-site infection on their website. 51 Investigators and implementation scientists have continued to use the CUSP approach in a variety of clinical settings with mixed results. Although CUSP has shown success in preventing HAIs, such as CLABSI in US intensive care units Reference Berenholtz, Lubomski and Weeks32,Reference Miller, Briody and Casey52 and CAUTI on medical-surgical floors in acute-care hospitals Reference Saint, Greene and Krein53 and in nursing homes, Reference Mody, Greene and Meddings54 not all interventions using a CUSP-based approach have been successful. 55

European and Mixed Methods

The European and Mixed-Methods framework derives from the CFIR 27 and originated as the ‘InDepth’ work package, Reference Sax, Clack and Touveneau56 a longitudinal qualitative comparative case study within the Prevention of Hospital Infections by Intervention and Training (PROHIBIT) study. Reference van der Kooi, Sax and Pittet57,Reference van der Kooi, Sax and Grundmann58 Specifically, InDepth sought to identify the role contextual factors play in barriers and facilitators to successful implementation. Reference Clack, Zingg and Saint59 The framework defines 3 qualitative measures of implementation success:

  1. 1. Acceptability: Satisfaction with the intervention.

  2. 2. Intervention fidelity: Local implementation matched with the stated goals of the multicenter trial.

  3. 3. Adaptation: Local efforts to match the intervention with local context.

The framework has not been applied beyond the PROHIBIT outcomes of CLABSI rates and hand hygiene adherence, Reference van der Kooi, Sax and Pittet57,Reference van der Kooi, Sax and Grundmann58 but the reported results may be used to inform other approaches.

Getting to Outcomes (GTO)®

Getting to Outcomes (GTO)® is a means of planning, implementing, and evaluating programs and initiatives developed for community settings. GTO® seeks to build capacity for self-efficacy, attitudes, and behaviors to yield effective prevention practices. Reference Chinman, Hunter and Ebener60 The process involves 10 steps:

  • Steps 1–5: Assess and evaluate needs, goals, and feasibility of a proposed program.

  • Step 6: Plan and deliver the program.

  • Steps 7–10: Evaluate, improve, and sustain successes.

GTO® has been utilized for numerous community-based initiatives, such as evidence-based sexual health promotion, Reference Chinman, Acosta, Ebener, Malone and Slaughter61 a dual-disorder treatment program for veterans, Reference Chinman, McCarthy, Hannah, Byrne and Smelson62 and development of casework models for child welfare services. Reference Barbee, Antle, Wandersman and Cahn63 Additionally, the RAND Corporation has published a guide to develop community emergency preparedness programs. This guide breaks down each of the 10 GTO® steps, provides materials and examples, Reference Ebener, Hunter, Adams, Eisenman, Acosta and Chinman64 and may facilitate the use of GTO® in implementing infection prevention interventions.

Model for Improvement

The Model for Improvement Reference Langley, Moen, Nolan, Nolan, Norman and Provost65 was developed by the Associates for Process Improvement based on Deming’s System of Profound Knowledge. Reference Deming66 It has since been adopted widely, perhaps most notably by the Institute for Healthcare Improvement (IHI) in its 100,000 and 5 Million Lives campaigns of the early 2000s. 67 The model has been used to accelerate change in a variety of healthcare and public health settings, Reference Lannon and Peterson68Reference Harrison, Shook, Harris, Lea, Cornett and Randolph70 and subspecialists have created primers focused on their practice areas. Reference Guo, Fortin, Mayo, Robinson and Lo71Reference Gaudreault-Tremblay, McQuillan, Parekh and Noone74 The Model for Improvement begins with 3 questions:

  1. 1. What are we trying to accomplish?

  2. 2. How will we know that a change is an improvement?

  3. 3. What change can we make that will result in improvement?

Once those questions are answered, the identified changes or interventions are tested using plan–do–study–act (PDSA) cycles. Individual tests include the following:

  • Plan: Predictions of outcome.

  • Do: Executed according to plan.

  • Study: Analysis and evaluation.

  • Act: Decision whether to keep, abandon, or modify the intervention.

PDSA findings and decisions then guide planning the next experiment, starting a new PDSA cycle. Multiple cycles are done in series called ‘ramps.’ The Model for Improvement is designed for team-driven projects, relies heavily on data analysis and interpretation, and requires training (much of which can be self-directed online).

Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM)

Reach, effectiveness, adoption, implementation, and maintenance make up the 5 dimensions of the planning and evaluation framework RE-AIM, developed to address the failures and delays in translating scientific evidence into policy and practice. Reference Nhim, Gruss and Porterfield75,Reference Glasgow and Estabrooks76 By utilizing these 5 dimensions at individual and ecological levels, teams can better understand the effectiveness of programs as they are implemented in real-world community settings. Reference Trivedi, Lewis and Deloney77,Reference Glasgow, Vogt and Boles78

RE-AIM is useful for planning an intervention, the outcomes that will be measured, and evaluating whether the intervention has met its goals. Reference Smith and Harden79 All 5 dimensions are not always addressed. In recent years there has been greater emphasis on pragmatic application of the framework to determine which dimensions an organization should prioritize. Reference Holtrop, Estabrooks and Gaglio80 It also provides ideas for quantitative measurements of outcomes.

RE-AIM was utilized to evaluate an antimicrobial stewardship program in an ICU in South Africa, Reference Nkosi and Sibanda81 dissemination and implementation of clinical practice guidelines for sexually transmitted infections, Reference Jeong, Jo, Oh and Oh82 and promotion of vaccination via digital technology. Reference Stephens, Wynn and Stockwell83 Recently, to better understand the implementation of contact tracing for emerging infectious diseases, RE-AIM was used to evaluate individual and systems-level predictors of success of an emergency volunteer COVID-19 contact tracing program in Connecticut. Reference Glasgow and Estabrooks76,Reference Glasgow, Vogt and Boles78,Reference Shelby, Schenck and Weeks84 Investigators concluded that the program fell short of CDC benchmarks for time and yield, largely due to difficulty collecting the information necessary for outreach.

Replicating Effective Practices (REP)

The Replicating Effective Programs (REP) framework may be used to balance needs of the target population with the core elements of successfully implemented interventions 85 and to maximize fidelity to core interventions that have been rigorously tested and have produced statistically significant positive results. 86

There are 4 phases of REP Reference Kilbourne, Neumann, Pincus, Bauer and Stall87 :

  1. 1. Preconditions (ie, identification of needs)

  2. 2. Preimplementation (eg, community input)

  3. 3. Implementation (eg, training)

  4. 4. Maintenance (eg, preparing for sustainability).

REP may be useful when adapting interventions for a specified target audience within healthcare. It also may be applied across the continuum of care (eg, acute care to long-term care) or in multifacility systems when local institutional culture dictate the need for adaptation. When used to disseminate evidence-based HIV prevention interventions to community-based organizations, the application of REP to packaging, HCP training, and technical assistance resulted in more effective uptake than dissemination alone. Reference Kilbourne, Glasgow and Chambers88

Theoretical domains

The Theoretical Domains Framework (TDF) was initially developed to conduct research on the behavior of HCP as it relates to implementing evidence-based practices. Reference Atkins, Francis and Islam89 The initial organization of TDF Reference Michie, Johnston and Abraham90 was modified following a formal validation exercise, Reference Cane, O’Connor and Michie91 which yielded 14 domains to identify relevant cognitive, affective, social, and environmental influences on behavior. Reference Atkins, Francis and Islam89 TDF has been used widely to understand and influence HCP, Reference Squires, Linklater and Grimshaw92 patient, and population behaviors, most commonly with qualitative methods (eg, surveys, interviews, and/or focus groups). Reference Atkins, Francis and Islam89 One salient example is a patient safety effort to properly place nasogastric tubes. A team utilized a validated TDF-based questionnaire to identify relevant domains that were then explored in focus groups to help connect theory to techniques to change behavior. Reference Taylor, Lawton, Slater and Foy93 More recently, TDF was used to develop the Choosing Wisely De-Implementation Framework Reference Grimshaw, Patey and Kirkham94 that proposes to reduce low-value care, defined as a test or treatment for which there is no evidence of patient benefit or where there is evidence of more harm than benefit. Reference Brownlee, Chalkidou and Doust95 A guide to TDF use Reference Atkins, Francis and Islam89 and a 4-step systematic approach to using TDF Reference French, Green and O’Connor96 were published to help teams design and follow through with an intervention. TDF has been linked to the COM-B model (used in the aforementioned BCW) and has been used in combination with other frameworks when the time necessary to complete interviews and focus groups was limited. Reference Atkins, Hunkeler and Jensen97

Future needs

Models for underperforming hospitals and units

Allthough national implementation studies have succeeded in preventing several different HAIs, investigators have not seen the same success when focused on facilities most in need of help—hospitals underperforming with respect to HAI prevention. The CDC-funded national prospective, interventional, quality improvement program, CDC STRIVE (States Targeting Reduction in Infections Via Engagement), focused on reducing CLABSIs, CAUTIs, Clostridioides difficile infections, and methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections in hospitals with a disproportionately high burden of HAIs. Reference Popovich, Calfee and Patel98 Although nearly 400 US hospitals participated in this multimodal, multifaceted, partner-facilitated program, they did not see significantly reduced rates of CLABSI, Reference Patel, Greene and Jones99 CAUTI, Reference Meddings, Manojlovich and Ameling100 C difficile infection, Reference Dubberke, Rohde and Saint101 or MRSA bloodstream infection. Reference Calfee, Davila and Chopra102 More recently, the Agency for Healthcare Research and Quality (AHRQ) funded a national program that invited US hospitals that had at least 1 adult ICU with elevated CLABSI or CAUTI rates to participate in an externally facilitated program implemented by a national project team and state hospital associations using the Comprehensive Unit-based Safety Program (CUSP) framework. Reference Meddings, Greene and Ratz103 Results from the first 2 cohorts (366 recruited ICUs from 220 hospitals in 16 states and Puerto Rico) revealed no statistically significant reductions in CLABSI, CAUTI, or catheter utilization in the 280 ICUs that completed the program. Reference Meddings, Greene and Ratz103 These researchers cite a number of possible factors contributing to the disappointing result, including underutilization of training and coaching resources, lack of infrastructure, and a different selection process for participants (eg, identifying low-performing units or those that had been unsuccessful to date versus asking for volunteers for earlier CUSP work, which may have selected for early adopters and high performers). Investigations in this and similar cohorts could help elucidate why hospitals with disproportionately high HAI rates have not yet seen significant reductions in HAIs despite broad-based efforts. Increased focus on the development, adaptation, and utilization of implementation models and frameworks to infection prevention and control may help identify implementation gaps that contribute to lack of improvement and guide their closure.

Sustaining system change

A long-term goal of any implementation effort is to sustain and advance short-term gains. Ideally, sustaining gains occurs with less intensity than initial efforts, maintaining gains or improving at a slower rate and allowing resources to be directed to another effort. Characteristics of successfully sustained interventions have included those that are incorporated into the standard workflow, have effective champions to shepherd the effort and re-engage when necessary, can be modified over time, fit with an organization’s mission and procedures, provide easily perceived benefits to staff members and/or clients, and are supported by partner organizations. Reference Scheirer104 It can be difficult to meet those criteria in healthcare, where changes in workflows and staff are frequent. Reference Fowler, Krein, Ratz, Zawol and Saint105 Demonstrating successfully sustained implementation should include evidence of (1) sustainment, that is, sustained use of an evidence-based intervention (process measure), and (2) sustainability, that is, sustained benefits of an evidence-based intervention (outcome measure). Reference Moullin, Sklar and Green106

Linking ongoing process data to ongoing outcome data can prove challenging. In one study, CLABSI reduction in ICUs was sustained for a decade, but process measurement was not performed. Reference Pronovost, Watson, Goeschel, Hyzy and Berenholtz34 A study on CAUTI prevention at a Veterans’ Affairs (VA) hospital found that, 8 years after implementation, appropriateness of urinary catheters remained high and stable. Reference Fowler, Krein, Ratz, Zawol and Saint105 Catheter use decreased, but the facility was unable to report outcome data. Reference Fowler, Krein, Ratz, Zawol and Saint105 These researchers hypothesized that success was due to a 3-component, evidence-based intervention, institutionalization of the interventions (ie, standardizing nursing assessments and handoffs that included the intervention), and effective champions who re-engaged when necessary. Additionally, a study on hand hygiene on 2 hospital units in Italy found that adherence dropped after 4 years (from 84.2% to 71%) despite maintaining champions and processes. Reference Lieber, Mantengoli and Saint107 Recent proposals for standard definitions Reference Moullin, Sklar and Green106,Reference Moore, Mascarenhas, Bain and Straus108 and modified ERIC strategies to account for sustainment and identify interventions that yield short-term and sustained improvements Reference Nathan, Powell and Shelton109 can form a basis for future research and understanding.


It is increasingly evident that implementation is essential to ensuring that evidence-based interventions are performed to generate desired outcomes and to meet infection prevention and control and antimicrobial stewardship goals. Reference Livorsi, Drainoni and Reisinger10 Furthermore, a detailed implementation plan in a specific healthcare setting for a given intervention is necessary for success, as the implementation approach in one facility may not be reproducible, with the desired effect, in another. In this article we have provided an overview of implementation in a general sense, with a glossary of terms, broader discussion of key methods, models, and frameworks, possible future areas of study, as well as links to resources readers can use to initiate or continue their implementation journey.

Supplementary material

The supplementary material for this article can be found at


The authors thank Cara C. Lewis, PhD, Kaiser Permanente Washington Health Research Institute, Seattle, WA and Lynn Hadaway, MEd, RN, Lynn Hadaway Associates Inc (Milner, GA) for their contributions to SHEA’s efforts to advance infection prevention and healthcare epidemiology through implementation science, and for the contributions of their subject matter expertise to this article.

The findings and conclusions in this report are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Financial support

SHEA funded the development and publication of this manuscript.

Competing interests

The following disclosures reflect what has been reported to SHEA. To provide thorough transparency, SHEA requires full disclosure of all relationships, regardless of relevancy to the guideline topic. Such relationships as potential conflicts of interest are evaluated in a review process that includes assessment by the SHEA Conflict of Interest Committee and may include the Board of Trustees and Editor of Infection Control and Hospital Epidemiology. The assessment of disclosed relationships for possible conflicts of interest has been based on the relative weight of the financial relationship (ie, monetary amount) and the relevance of the relationship (ie, the degree to which an association might reasonably be interpreted by an independent observer as related to the topic or recommendation of consideration). K.K.T. is the owner of the consulting company Trivedi Consults. V.M.D. is the owner of the consulting company Youngtree Communications. R.C. has consulting relationships with Moderna, Novavax, and Pfizer (speakers bureau, research contract) and Sanofi (speakers bureau). M.L.S. has grant funding from 3M and PDI for nasal decolonization. All other authors report no conflicts of interest related to this article.



Authors of equal contribution.


Gerberding, JL. Hospital-onset infections: a patient safety issue. Ann Intern Med 2002;137:665670.CrossRefGoogle ScholarPubMed
Personal protective equipment standards and documents. Occupational Safety and Health Administration website. Accessed June 1, 2023.Google Scholar
Medicare state operations manual: appendix A—survey protocol, regulations and interpretive guidelines for hospitals, 2013. Centers for Medicare and Medicaid Services website. Published 2013. Accessed June 1, 2023.Google Scholar
NIAHO accreditation requirements, interpretive guidelines and surveyor guidance, 2023. DNV website. Accessed January 2023.Google Scholar
Condition of participation: infection prevention and control and antibiotic stewardship programs. Code of Federeal Regulations website. Amended May 11, 2023. Accessed June 1, 2023.Google Scholar
Eccles, MP, Mittman, BS. Welcome to implementation science. Implement Sci 2006;1:1.CrossRefGoogle Scholar
Neta, G, Brownson, RC, Chambers, DA. Opportunities for epidemiologists in implementation science: a primer. Am J Epidemiol 2018;187:899910.CrossRefGoogle ScholarPubMed
Livorsi, DJ, Drainoni, ML, Reisinger, HS, et al. Leveraging implementation science to advance antibiotic stewardship practice and research. Infect Control Hosp Epidemiol 2022;43:139146.CrossRefGoogle ScholarPubMed
Saint, S, Howell, JD, Krein, SL. Implementation science: how to jump-start infection prevention. Infect Control Hosp Epidemiol 2010;31 suppl 1:S14S17.CrossRefGoogle Scholar
Leeman, J, Rohweder, C, Lee, M, et al. Aligning implementation science with improvement practice: a call to action. Implement Sci Commun 2021;2:99.CrossRefGoogle Scholar
Houghton, C, Meskell, P, Delaney, H, et al. Barriers and facilitators to healthcare workers’ adherence with infection prevention and control (IPC) guidelines for respiratory infectious diseases: a rapid qualitative evidence synthesis. Cochrane Database Syst Rev 2020;4:CD013582.Google ScholarPubMed
Ali, KJ, Farley, DO, Speck, K, Catanzaro, M, Wicker, KG, Berenholtz, SM. Measurement of implementation components and contextual factors in a two-state healthcare quality initiative to reduce ventilator-associated pneumonia. Infect Control Hosp Epidemiol 2014;35 suppl 3:S116S123.CrossRefGoogle Scholar
Wolfensberger, A, Meier, MT, Clack, L, Schreiber, PW, Sax, H. Preventing ventilator-associated pneumonia-a mixed-method study to find behavioral leverage for better protocol adherence. Infect Control Hosp Epidemiol 2018;39:12221229.CrossRefGoogle ScholarPubMed
Aarons, GA, Hurlburt, M, Horwitz, SM. Advancing a conceptual model of evidence-based practice implementation in public service sectors. Adm Policy Ment Health 2011;38:423.CrossRefGoogle ScholarPubMed
Saint, S, Kowalski, CP, Banaszak-Holl, J, Forman, J, Damschroder, L, Krein, SL. The importance of leadership in preventing healthcare-associated infection: results of a multisite qualitative study. Infect Control Hosp Epidemiol 2010;31:901907.CrossRefGoogle ScholarPubMed
Saint, S, Kowalski, CP, Banaszak-Holl, J, Forman, J, Damschroder, L, Krein, SL. How active resisters and organizational constipators affect healthcare-acquired infection prevention efforts. Jt Comm J Qual Pat Saf 2009;35:239246.Google Scholar
Cabana, MD, Rand, CS, Powe, NR, et al. Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA 1999;282:14581465.CrossRefGoogle ScholarPubMed
Powell, BJ, Waltz, TJ, Chinman, MJ, et al. A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. Implement Sci 2015;10:21.CrossRefGoogle ScholarPubMed
Science of Improvement: establishing measures, 2023. Institute for Healthcare Improvement website. Accessed June 1, 2023.Google Scholar
National Healthcare Safety Network (NHSN). Patient Safety Component Manual. Centers for Disease Control and Prevention website. Published January 2023. Accessed June 1, 2023.Google Scholar
Advani, SD, Murray, TS, Murdzek, CM, Aniskiewicz, MJ, Bizzarro, MJ. Shifting focus toward healthcare-associated bloodstream infections: the need for neonatal intensive care unit-specific NHSN definitions. Infect Control Hosp Epidemiol 2020;41:181186.Google ScholarPubMed
Measuring hand hygiene adherence: overcoming the challenges. The Joint Commission website. Available from: Published 2009. Accessed June 1, 2023.Google Scholar
Jamtvedt, G, Young, JM, Kristoffersen, DT, O’Brien, MA, Oxman, AD. Does telling people what they have been doing change what they do? A systematic review of the effects of audit and feedback. Qual Saf Health Care 2006;15:433436.CrossRefGoogle ScholarPubMed
Morgan, DJ, Safdar, N, Milstone, AM, Anderson, DJ. Research methods in healthcare epidemiology and antimicrobial stewardship. Infect Control Hosp Epidemiol 2016;37:627628.10.1017/ice.2016.91CrossRefGoogle ScholarPubMed
Consolidated Framework for Implementation Research website. Updated 2022. Accessed June 1, 2023.Google Scholar
Damschroder, LJ, Aron, DC, Keith, RE, Kirsh, SR, Alexander, JA, Lowery, JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci 2009;4:50.CrossRefGoogle Scholar
Pennathur, PR, Herwaldt, LA. Role of human factors engineering in infection prevention: gaps and opportunities. Curr Treat Options Infect Dis 2017;9:230249.CrossRefGoogle ScholarPubMed
Pronovost, PJ, Berenholtz, SM, Needham, DM. Translating evidence into practice: a model for large scale knowledge translation. BMJ 2008;337:a1714.CrossRefGoogle Scholar
Sexton, JB, Berenholtz, SM, Goeschel, CA, et al. Assessing and improving safety climate in a large cohort of intensive care units. Crit Care Med 2011;39:934939.CrossRefGoogle Scholar
Berenholtz, SM, Lubomski, LH, Weeks, K, et al. Eliminating central-line–associated bloodstream infections: a national patient safety imperative. Infect Control Hosp Epidemiol 2014;35:5662.CrossRefGoogle ScholarPubMed
Pronovost, P, Needham, D, Berenholtz, S, et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med 2006;355:27252732.CrossRefGoogle ScholarPubMed
Pronovost, PJ, Watson, SR, Goeschel, CA, Hyzy, RC, Berenholtz, SM. Sustaining reductions in central-line–associated bloodstream infections in Michigan intensive care units: a 10-year analysis. Am J Med Qual 2016;31:197202.CrossRefGoogle ScholarPubMed
Lipitz-Snyderman, A, Steinwachs, D, Needham, DM, Colantuoni, E, Morlock, LL, Pronovost, PJ. Impact of a statewide intensive care unit quality improvement initiative on hospital mortality and length of stay: retrospective comparative analysis. BMJ 2011;342:d219.CrossRefGoogle ScholarPubMed
Waters, HR, Korn, R Jr, Colantuoni, E, et al. The business case for quality: economic analysis of the Michigan Keystone Patient Safety Program in ICUs. Am J Med Qual 2011;26:333339.CrossRefGoogle ScholarPubMed
Michie, S, van Stralen, MM, West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci 2011;6:42.CrossRefGoogle ScholarPubMed
Gould, GS, Bar-Zeev, Y, Bovill, M, et al. Designing an implementation intervention with the Behaviour Change Wheel for health provider smoking cessation care for Australian Indigenous pregnant women. Implement Sci 2017;12:114.CrossRefGoogle ScholarPubMed
Ojo, SO, Bailey, DP, Brierley, ML, Hewson, DJ, Chater, AM. Breaking barriers: using the behavior change wheel to develop a tailored intervention to overcome workplace inhibitors to breaking up sitting time. BMC Public Health 2019;19:1126.CrossRefGoogle ScholarPubMed
Atkins, L, Michie, S. Designing interventions to change eating behaviours. Proc Nutr Soc 2015;74:164170.CrossRefGoogle ScholarPubMed
Munir, F, Biddle, SJH, Davies, MJ, et al. Stand More AT Work (SMArT Work): using the behaviour change wheel to develop an intervention to reduce sitting time in the workplace. BMC Public Health 2018;18:319.CrossRefGoogle ScholarPubMed
Schmidtke, KA, Drinkwater, KG. A cross-sectional survey assessing the influence of theoretically informed behavioural factors on hand hygiene across seven countries during the COVID-19 pandemic. BMC Public Health 2021;21:1432.CrossRefGoogle ScholarPubMed
Lambe, K, Lydon, S, Madden, C, et al. Understanding hand hygiene behaviour in the intensive care unit to inform interventions: an interview study. BMC Health Serv Res 2020;20:353.CrossRefGoogle ScholarPubMed
Tomsic, I, Ebadi, E, Gosse, F, et al. Determinants of orthopedic physicians’ self-reported compliance with surgical site infection prevention: results of the WACH-trial’s pilot survey on COM-B factors in a German university hospital. Antimicrob Resist Infect Control 2021;10:67.CrossRefGoogle ScholarPubMed
Courtenay, M, Rowbotham, S, Lim, R, Peters, S, Yates, K, Chater, A. Examining influences on antibiotic prescribing by nurse and pharmacist prescribers: a qualitative study using the Theoretical Domains Framework and COM-B. BMJ Open 2019;9:e029177.10.1136/bmjopen-2019-029177CrossRefGoogle ScholarPubMed
Pronovost, P, Weast, B, Rosenstein, B. Implementing and validating a comprehensive unit-based safety program. J Pat Saf Infect Control 2005;1:3340.Google Scholar
The CUSP Toolkit 2018. Agency for Healthcare Research and Quality website. Accessed February 15, 2022.Google Scholar
Eliminating catheter-associated urinary tract infections. Hospitals in Pursuit of Excellence website. Published July 2013. Accessed June 1, 2023.Google Scholar
Meddings, J, Chopra, V, Saint, S. Preventing Hospital Infections: Real-World Problems, Realistic Solutions. New York: Oxford University Press; 2021.CrossRefGoogle Scholar
Keller, SC, Caballero, TM, Tamma, PD, et al. Assessment of changes in visits and antibiotic prescribing during the Agency for Healthcare Research and Quality safety program for improving antibiotic use and the COVID-19 pandemic. JAMA Netw Open 2022;5:e2220512.CrossRefGoogle ScholarPubMed
Tool kit to improve safety in ambulatory surgery centers. Agency for Healthcare Research and Quality website. Updated May 2017. Accessed June 1, 2023.CrossRefGoogle Scholar
Miller, K, Briody, C, Casey, D, et al. Using the Comprehensive Unit-based Safety Program model for sustained reduction in hospital infections. Am J Infect Control 2016;44:969976.CrossRefGoogle ScholarPubMed
Saint, S, Greene, MT, Krein, SL, et al. A program to prevent catheter-associated urinary tract infection in acute care. N Engl J Med 2016;374:21112119.CrossRefGoogle ScholarPubMed
Mody, L, Greene, MT, Meddings, J, et al. A national implementation project to prevent catheter-associated urinary tract infection in nursing home residents. JAMA Intern Med 2017;177:11541162.CrossRefGoogle ScholarPubMed
The National Academies Press. Peer review of a report on strategies to improve patient safety, 2021. Published February 17, 2022. Accessed June 1, 2023.Google Scholar
Sax, H, Clack, L, Touveneau, S, et al. Implementation of infection control best practice in intensive care units throughout Europe: a mixed-method evaluation study. Implement Sci 2013;8:24.CrossRefGoogle ScholarPubMed
van der Kooi, T, Sax, H, Pittet, D, et al. Prevention of hospital infections by intervention and training (PROHIBIT): results of a pan-European cluster-randomized multicentre study to reduce central venous catheter-related bloodstream infections. Intensive Care Med 2018;44:4860.CrossRefGoogle ScholarPubMed
van der Kooi, T, Sax, H, Grundmann, H, et al. Hand hygiene improvement of individual healthcare workers: results of the multicentre PROHIBIT study. Antimicrob Resist Infect Control 2022;11:123.CrossRefGoogle ScholarPubMed
Clack, L, Zingg, W, Saint, S, et al. Implementing infection prevention practices across European hospitals: an in-depth qualitative assessment. BMJ Qual Saf 2018;27:771780.CrossRefGoogle ScholarPubMed
Chinman, M, Hunter, SB, Ebener, P, et al. The getting to outcomes demonstration and evaluation: an illustration of the prevention support system. Am J Community Psychol 2008;41:206224.CrossRefGoogle ScholarPubMed
Chinman, M, Acosta, J, Ebener, P, Malone, PS, Slaughter, ME. Can implementation support help community-based settings better deliver evidence-based sexual health promotion programs? A randomized trial of Getting To Outcomes. Implement Sci 2016;11:78.CrossRefGoogle ScholarPubMed
Chinman, M, McCarthy, S, Hannah, G, Byrne, TH, Smelson, DA. Using Getting To Outcomes to facilitate the use of an evidence-based practice in VA homeless programs: a cluster-randomized trial of an implementation support strategy. Implement Sci 2017;12:34.CrossRefGoogle ScholarPubMed
Barbee, AP CD, Antle, B, Wandersman, A, Cahn, K. Successful adoption and implementation of a comprehensive casework practice model in a public child welfare agency: Application of the Getting to Outcomes (GTO) model. Child Youth Serv Rev 2011;33:622633.CrossRefGoogle Scholar
Ebener, PA, Hunter, Sarah B., Adams, RM, Eisenman, D, Acosta, JD, Chinman, M. Getting To Outcomes: Guide for Community Emergency Preparedness. Santa Monica, CA: RAND Corporation; 2017.CrossRefGoogle Scholar
Langley, G, Moen, R, Nolan, K, Nolan, T, Norman, C, Provost, L. The Improvement Guide: A Practical Approach to Enhancing Organizational Performance, 2 nd edition. San Francisco: Jossey-Bass; 2009.Google Scholar
Deming, WE. The New Economics for Industry, Government, Education, 2 nd edition. Cambridge, MA: MIT Press; 1994.Google Scholar
5 Million Lives Campaign. Excerpted from Dr. Berwick’s plenary address at the IHI 16th Annual National Forum on Quality Improvement in Health Care (December 2004). Institute for Healthcare Improvement website. Accessed June 1, 2023.Google Scholar
Lannon, CM, Peterson, LE. Pediatric collaborative improvement networks: background and overview. Pediatrics 2013;131 suppl 4:S189S195.CrossRefGoogle Scholar
Hennessy, KA, Dynan, J. Improving compliance with personal protective equipment use through the model for improvement and staff champions. Clin J Oncol Nurs 2014;18:497500.CrossRefGoogle ScholarPubMed
Harrison, LM, Shook, ED, Harris, G, Lea, CS, Cornett, A, Randolph, GD. Applying the model for improvement in a local health department: quality improvement as an effective approach in navigating the changing landscape of public health practice in Buncombe County, North Carolina. J Public Health Manag Pract 2012;18:1926.CrossRefGoogle Scholar
Guo, M, Fortin, C, Mayo, AL, Robinson, LR, Lo, A. Quality improvement in rehabilitation: a primer for physical medicine and rehabilitation specialists. PM R 2019;11:771778.CrossRefGoogle ScholarPubMed
Kapadia, M, Lehmann, L, Auletta, J, et al. Quality improvement in hematopoietic stem cell transplant and cellular therapy: using the Model for Improvement to impact outcomes. Transplant Cell Ther 2022;28:233241.CrossRefGoogle ScholarPubMed
Crowl, A, Sharma, A, Sorge, L, Sorensen, T. Accelerating quality improvement within your organization: applying the Model for Improvement. J Am Pharm Assoc (2003) 2015;55:e364e374.CrossRefGoogle ScholarPubMed
Gaudreault-Tremblay, MM, McQuillan, RF, Parekh, RS, Noone, D. Quality improvement in pediatric nephrology-a practical guide. Pediatr Nephrol 2020;35:199211.CrossRefGoogle ScholarPubMed
Nhim, K, Gruss, SM, Porterfield, DS, et al. Using a RE-AIM framework to identify promising practices in National Diabetes Prevention Program implementation. Implement Sci 2019;14:81.CrossRefGoogle ScholarPubMed
Glasgow, RE, Estabrooks, PE. Pragmatic applications of RE-AIM for healthcare initiatives in community and clinical settings. Prev Chronic Dis 2018;15:E02.CrossRefGoogle Scholar
Trivedi, K, Lewis, C, Deloney, V. SHEA/CDC Outbreak Response Training Program (ORTP) tool kits: dissemination and implementation frameworks. Published 2018. Accessed June 1, 2023.Google Scholar
Glasgow, RE, Vogt, TM, Boles, SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health 1999;89:13221327.CrossRefGoogle ScholarPubMed
Smith, ML, Harden, SM. Full comprehension of theories, models, and frameworks improves application: a focus on RE-AIM. Front Public Health 2021;9:599975.CrossRefGoogle ScholarPubMed
Holtrop, JA-O, Estabrooks, PA, Gaglio, B, et al. Understanding and applying the RE-AIM framework: clarifications and resources. J Clin Transl Sci 2021;5:e126.CrossRefGoogle ScholarPubMed
Nkosi, BE, Sibanda, S. Evaluating an antimicrobial stewardship programme implemented in an intensive care unit of a large academic hospital, using the RE-AIM framework. S Afr Med J 2021;111:777782.CrossRefGoogle Scholar
Jeong, HJ, Jo, HS, Oh, MK, Oh, HW. Applying the RE-AIM framework to evaluate the dissemination and implementation of clinical practice guidelines for sexually transmitted infections. J Korean Med Sci 2015;30:847852.CrossRefGoogle ScholarPubMed
Stephens, AB, Wynn, CS, Stockwell, MS. Understanding the use of digital technology to promote human papillomavirus vaccination—A RE-AIM framework approach. Hum Vaccin Immunother 2019;15:15491561.CrossRefGoogle ScholarPubMed
Shelby, T, Schenck, C, Weeks, B, et al. Lessons learned from COVID-19 contact tracing during a public health emergency: a prospective implementation study. Front Public Health 2021;9:721952.10.3389/fpubh.2021.721952CrossRefGoogle ScholarPubMed
Replicating Effective Programs (REP) project. Centers for Disease Control and Prevention website. Updated November 2019. Accessed June 1, 2023.Google Scholar
Compendium of HIV prevention interventions with evidence of effectiveness, November 1999. Centers for Disease Control and Prevention website. Accessed June 1, 2023.Google Scholar
Kilbourne, AM, Neumann, MS, Pincus, HA, Bauer, MS, Stall, R. Implementing evidence-based interventions in health care: application of the replicating effective programs framework. Implement Sci 2007;2:42.CrossRefGoogle ScholarPubMed
Kilbourne, AM, Glasgow, RE, Chambers, DA. What can implementation science do for you? Key success stories from the field. J Gen Intern Med 2020;35:783787.CrossRefGoogle ScholarPubMed
Atkins, L, Francis, J, Islam, R, et al. A guide to using the theoretical domains framework of behaviour change to investigate implementation problems. Implement Sci 2017;12:77.CrossRefGoogle ScholarPubMed
Michie, S, Johnston, M, Abraham, C, et al. Making psychological theory useful for implementing evidence based practice: a consensus approach. Qual Saf Health Care 2005;14:2633.CrossRefGoogle ScholarPubMed
Cane, J, O’Connor, D, Michie, S. Validation of the theoretical domains framework for use in behaviour change and implementation research. Implement Sci 2012;7:37.CrossRefGoogle ScholarPubMed
Squires, JE, Linklater, S, Grimshaw, JM, et al. Understanding practice: factors that influence physician hand hygiene compliance. Infect Control Hosp Epidemiol 2014;35:15111520.CrossRefGoogle ScholarPubMed
Taylor, N, Lawton, R, Slater, B, Foy, R. The demonstration of a theory-based approach to the design of localized patient safety interventions. Implement Sci 2013;8:123.CrossRefGoogle Scholar
Grimshaw, JM, Patey, AM, Kirkham, KR, et al. De-implementing wisely: developing the evidence base to reduce low-value care. BMJ Qual Saf 2020;29:409417.CrossRefGoogle ScholarPubMed
Brownlee, S, Chalkidou, K, Doust, J, et al. Evidence for overuse of medical services around the world. Lancet 2017;390.Google ScholarPubMed
French, SD, Green, SE, O’Connor, DA, et al. Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework. Implement Sci 2012;7:38.CrossRefGoogle Scholar
Atkins, L, Hunkeler, EM, Jensen, CD, et al. Factors influencing variation in physician adenoma detection rates: a theory-based approach for performance improvement. Gastrointest Endosc 2016;83:617626.CrossRefGoogle Scholar
Popovich, KJ, Calfee, DP, Patel, PK, et al. The Centers for Disease Control and Prevention STRIVE initiative: construction of a national program to reduce health care-associated infections at the local level. Ann Intern Med 2019;171:S2S6.CrossRefGoogle ScholarPubMed
Patel, PK, Greene, MT, Jones, K, et al. Quantitative results of a national intervention to prevent central line-associated bloodstream infection: a pre–post observational study. Ann Intern Med 2019;171:S23S29.CrossRefGoogle ScholarPubMed
Meddings, J, Manojlovich, M, Ameling, JM, et al. Quantitative results of a national intervention to prevent hospital-acquired catheter-associated urinary tract infection: a pre–post observational study. Ann Intern Med 2019;171:S38S44.CrossRefGoogle ScholarPubMed
Dubberke, ER, Rohde, JM, Saint, S, et al. Quantitative results of a national intervention to prevent Clostridioides difficile infection: a pre–post observational study. Ann Intern Med 2019;171:S52S58.CrossRefGoogle ScholarPubMed
Calfee, DP, Davila, S, Chopra, V, et al. Quantitative results of a national intervention to prevent hospital-onset methicillin-resistant Staphylococcus aureus bloodstream infection: a pre–post observational study. Ann Intern Med 2019;171:S66S72.CrossRefGoogle ScholarPubMed
Meddings, J, Greene, MT, Ratz, D, et al. Multistate programme to reduce catheter-associated infections in intensive care units with elevated infection rates. BMJ Qual Saf 2020;29:418429.CrossRefGoogle ScholarPubMed
Scheirer, MA. Is Sustainability possible? A review and commentary on empirical studies of program sustainability. Am J Eval 2005;26:320347.CrossRefGoogle Scholar
Fowler, KE, Krein, SL, Ratz, D, Zawol, D, Saint, S. Sustainability of a program to reduce unnecessary urethral catheter use at a Veterans’ Affairs hospital. Infect Control Hosp Epidemiol 2021;42:14971499.CrossRefGoogle Scholar
Moullin, JC, Sklar, M, Green, A, et al. Advancing the pragmatic measurement of sustainment: a narrative review of measures. Implement Sci Commun 2020;1:76.CrossRefGoogle ScholarPubMed
Lieber, SR, Mantengoli, E, Saint, S, et al. The effect of leadership on hand hygiene: assessing hand hygiene adherence prior to patient contact in 2 infectious disease units in Tuscany. Infect Control Hosp Epidemiol 2014;35:313316.CrossRefGoogle ScholarPubMed
Moore, JE, Mascarenhas, A, Bain, J, Straus, SE. Developing a comprehensive definition of sustainability. Implement Science 2017;12:110.CrossRefGoogle ScholarPubMed
Nathan, N, Powell, BJ, Shelton, RC, et al. Do the Expert Recommendations for Implementing Change (ERIC) strategies adequately address sustainment? Front Health Serv 2022;2.CrossRefGoogle Scholar
Communication and dissemination strategies to facilitate the use of health-related evidence. Agency for Healthcare Research and Quality website. Published July 3, 2012. Accessed December 20, 2022.Google Scholar
Riley, WJ, Moran, JW, Corso, LC, Beitsch, LM, Bialek, R, Cofsky, A. Defining quality improvement in public health. J Public Health Manag Pract 2010;16:57.CrossRefGoogle ScholarPubMed
Topics: Quality improvement. Agency for Healthcare Research and Quality website. Accessed June 1, 2023.Google Scholar
Gurses, AP, Seidl, KL, Vaidya, V, et al. Systems ambiguity and guideline compliance: a qualitative study of how intensive care units follow evidence-based guidelines to reduce healthcare-associated infections. Qual Saf Health Care 2008;17:351359.CrossRefGoogle ScholarPubMed
Crandall, KM, Sten, MB, Almuhanna, A, Fahey, L, Shah, RK. Improving apparent cause analysis reliability: a quality improvement initiative. Pediatr Qual Saf 2017;2:e025.CrossRefGoogle ScholarPubMed
Khan, R, Al-Dorzi, HM, Al-Attas, K, et al. The impact of implementing multifaceted interventions on the prevention of ventilator-associated pneumonia. Am J Infect Control 2016;44:320326.CrossRefGoogle ScholarPubMed
Robinson, C, Hoze, M, Hevener, S, Nichols, AA. Development of an RN champion model to improve the outcomes of ventilator-associated pneumonia patients in the intensive care unit. J Nurs Adm 2018;48:7984.CrossRefGoogle Scholar
Damschroder, LJ, Banaszak-Holl, J, Kowalski, CP, Forman, J, Saint, S, Krein, SL. The role of the champion in infection prevention: results from a multisite qualitative study. Qual Saf Health Care 2009;18:434440.CrossRefGoogle ScholarPubMed
Hatler, CW, Mast, D, Corderella, J, et al. Using evidence and process improvement strategies to enhance healthcare outcomes for the critically ill: a pilot project. Am J Crit Care 2006;15:549555.CrossRefGoogle ScholarPubMed
Khan, RM, Al-Juaid, M, Al-Mutairi, H, et al. Implementing the comprehensive unit-based safety program model to improve the management of mechanically ventilated patients in Saudi Arabia. Am J Infect Control 2019;47:5158.CrossRefGoogle ScholarPubMed
Craven, DE. Preventing ventilator-associated pneumonia in adults: sowing seeds of change. Chest 2006;130:251260.CrossRefGoogle ScholarPubMed
Grimshaw, J, Eccles, M, Thomas, R, et al. Toward evidence-based quality improvement. Evidence (and its limitations) of the effectiveness of guideline dissemination and implementation strategies 1966–1998. J Gen Intern Med 2006;21 suppl 2:S14S20.Google ScholarPubMed
Talbot, TR, Carr, D, Lee Parmley, C, et al. Sustained reduction of ventilator-associated pneumonia rates using real-time course correction with a ventilator bundle compliance dashboard. Infect Control Hosp Epidemiol 2015;36:12611267.CrossRefGoogle ScholarPubMed
Alvarez-Lerma, F, Palomar-Martinez, M, Sanchez-Garcia, M, et al. Prevention of ventilator-associated pneumonia: the multimodal approach of the Spanish ICU “pneumonia zero” program. Crit Care Med 2018;46:181188.CrossRefGoogle ScholarPubMed
Pileggi, C, Mascaro, V, Bianco, A, Nobile, CGA, Pavia, M. Ventilator bundle and its effects on mortality among ICU patients: a meta-analysis. Crit Care Med 2018;46:11671174.CrossRefGoogle ScholarPubMed
Jansson, M, Kaariainen, M, Kyngas, H. Effectiveness of educational programmes in preventing ventilator-associated pneumonia: a systematic review. J Hosp Infect 2013;84:206214.CrossRefGoogle ScholarPubMed
Klompas, M, Anderson, D, Trick, W, et al. The preventability of ventilator-associated events. Am J Respir Crit Care Med 2015;191:292301.CrossRefGoogle ScholarPubMed
Kellie, SP, Scott, MJ, Cavallazzi, R, et al. Procedural and educational interventions to reduce ventilator-associated pneumonia rate and central-line–associated bloodstream infection rate. J Intensive Care Med 2014;29:165174.CrossRefGoogle Scholar
Pun, BT, Balas, MC, Barnes-Daly, MA, et al. Caring for critically ill patients with the ABCDEF bundle: results of the ICU liberation collaborative in over 15,000 adults. Crit Care Med 2019;47:314.CrossRefGoogle ScholarPubMed
Bouadma, L, Mourvillier, B, Deiler, V, et al. Changes in knowledge, beliefs, and perceptions throughout a multifaceted behavioral program aimed at preventing ventilator-associated pneumonia. Intensive Care Med 2010;36:13411347.CrossRefGoogle ScholarPubMed
Hawe, CS, Ellis, KS, Cairns, CJ, Longmate, A. Reduction of ventilator-associated pneumonia: active versus passive guideline implementation. Intensive Care Med 2009;35:11801186.CrossRefGoogle ScholarPubMed
Rello, J, Afonso, E, Lisboa, T, et al. A care bundle approach for prevention of ventilator-associated pneumonia. Clin Microbiol Infect 2013;19:363369.CrossRefGoogle ScholarPubMed
Lim, KP, Kuo, SW, Ko, WJ, et al. Efficacy of ventilator-associated pneumonia care bundle for prevention of ventilator-associated pneumonia in the surgical intensive care units of a medical center. J Microbiol Immunol Infect 2015;48:316321.CrossRefGoogle ScholarPubMed
Parisi, M, Gerovasili, V, Dimopoulos, S, et al. Use of ventilator bundle and staff education to decrease ventilator-associated pneumonia in intensive care patients. Crit Care Nurse 2016;36:e1e7.CrossRefGoogle ScholarPubMed
Bassi, GL, Ferrer, M, Saucedo, LM, Torres, A. Do guidelines change outcomes in ventilator-associated pneumonia? Curr Opin Infect Dis 2010;23:171177.CrossRefGoogle ScholarPubMed
Cusack, L, Del Mar, CB, Chalmers, I, Gibson, E, Hoffmann, TC. Educational interventions to improve people’s understanding of key concepts in assessing the effects of health interventions: a systematic review. Syst Rev 2018;7:68.CrossRefGoogle ScholarPubMed
Laschinger, S, Heather, K. A theoretical approach to studying work empowerment in nursing: a review of studies testing Kanter’s theory of structural power in organizations. Nurs Admin Qtrly 1996;20:2541.CrossRefGoogle Scholar
CDC Workplace Health Resource Center (WHRC). Engaging Employees to Bring Their Best to Work 2020. Centers for Disease Control and Prevention website. Accessed June 1, 2023.Google Scholar
Pinto, A, Burnett, S, Benn, J, et al. Improving reliability of clinical care practices for ventilated patients in the context of a patient safety improvement initiative. J Eval Clin Pract 2011;17:180187.CrossRefGoogle ScholarPubMed
Rawat, N, Yang, T, Ali, KJ, et al. Two-state collaborative study of a multifaceted intervention to decrease ventilator-associated events. Crit Care Med 2017;45:12081215.CrossRefGoogle ScholarPubMed
Westwell, S. Implementing a ventilator care bundle in an adult intensive care unit. Nurs Crit Care 2008;13:203207.CrossRefGoogle Scholar
Omrane, R, Eid, J, Perreault, MM, et al. Impact of a protocol for prevention of ventilator-associated pneumonia. Ann Pharmacother 2007;41:13901396.CrossRefGoogle ScholarPubMed
Eom, JS, Lee, MS, Chun, HK, et al. The impact of a ventilator bundle on preventing ventilator-associated pneumonia: a multicenter study. Am J Infect Control 2014;42:3437.CrossRefGoogle ScholarPubMed
Holden, RJ, Carayon, P, Gurses, AP, et al. SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients. Ergonomics 2013;56:16691686.CrossRefGoogle ScholarPubMed
Berenholtz, SM, Pham, JC, Thompson, DA, et al. Collaborative cohort study of an intervention to reduce ventilator-associated pneumonia in the intensive care unit. Infect Control Hosp Epidemiol 2011;32:305314.CrossRefGoogle ScholarPubMed
Bloos, F, Muller, S, Harz, A, et al. Effects of staff training on the care of mechanically ventilated patients: a prospective cohort study. Br J Anaesth 2009;103:232237.CrossRefGoogle ScholarPubMed
Burns, SM, Earven, S, Fisher, C, et al. Implementation of an institutional program to improve clinical and financial outcomes of mechanically ventilated patients: one-year outcomes and lessons learned. Crit Care Med 2003;31:27522763.CrossRefGoogle ScholarPubMed
Weireter, LJ Jr, Collins, JN, Britt, RC, Reed, SF, Novosel, TJ, Britt, LD. Impact of a monitored program of care on incidence of ventilator-associated pneumonia: results of a longterm performance-improvement project. J Am Coll Surg 2009;208:700774.CrossRefGoogle ScholarPubMed
Johnson, V, Mangram, A, Mitchell, C, Lorenzo, M, Howard, D, Dunn, E. Is there a benefit to multidisciplinary rounds in an open trauma intensive care unit regarding ventilator-associated pneumonia? Am Surgeon 2009;75:11711174.Google Scholar
Rello, J, Ramirez-Estrada, S, Romero, A, et al. Factors associated with ventilator-associated events: an international multicenter prospective cohort study. Eur J Clin Microbiol Infect Dis 2019;38:16931699.CrossRefGoogle ScholarPubMed
Heimes, J, Braxton, C, Nazir, N, et al. Implementation and enforcement of ventilator-associated pneumonia prevention strategies in trauma patients. Surg Infect (Larchmt) 2011;12:99103.CrossRefGoogle ScholarPubMed
Danckers, M, Grosu, H, Jean, R, et al. Nurse-driven, protocol-directed weaning from mechanical ventilation improves clinical outcomes and is well accepted by intensive care unit physicians. J Crit Care 2013;28:433441.CrossRefGoogle ScholarPubMed
Bigham, MT, Amato, R, Bondurrant, P, et al. Ventilator-associated pneumonia in the pediatric intensive care unit: characterizing the problem and implementing a sustainable solution. J Pediatr 2009;154:582587.CrossRefGoogle ScholarPubMed
Chumpia, MM, Ganz, DA, Chang, ET, de Peralta, SS. Reducing the rare event: lessons from the implementation of a ventilator bundle. BMJ Open Qual 2019;8:e000426.CrossRefGoogle ScholarPubMed
Rosenthal, VD, Desse, J, Maurizi, DM, et al. Impact of the International Nosocomial Infection Control Consortium’s multidimensional approach on rates of ventilator-associated pneumonia in 14 intensive care units in 11 hospitals of 5 cities within Argentina. Am J Infect Control 2018;46:674679.CrossRefGoogle ScholarPubMed
Al-Abdely, HM, Khidir Mohammed, Y, Rosenthal, VD, et al. Impact of the International Nosocomial Infection Control Consortium (INICC)’s multidimensional approach on rates of ventilator-associated pneumonia in intensive care units in 22 hospitals of 14 cities of the Kingdom of Saudi Arabia. J Infect Public Health 2018;11:677684.CrossRefGoogle ScholarPubMed
Balas, MC, Vasilevskis, EE, Olsen, KM, et al. Effectiveness and safety of the awakening and breathing coordination, delirium monitoring/management, and early exercise/mobility bundle. Crit Care Med 2014;42:10241036.CrossRefGoogle ScholarPubMed
Shea, G, Smith, W, Koffarnus, K, Knobloch, MJ, Safdar, N. Kamishibai cards to sustain evidence-based practices to reduce healthcare-associated infections. Am J Infect Control 2019;47:358365.CrossRefGoogle ScholarPubMed
Ormsby, JA, Cronin, J, Carpenter, J, et al. Central venous catheter bundle adherence: Kamishibai card (K-card) rounding for central-line–associated bloodstream infection (CLABSI) prevention. Infect Control Hosp Epidemiol 2020;41:10581063.CrossRefGoogle ScholarPubMed
Mangino, JE, Peyrani, P, Ford, KD, et al. Development and implementation of a performance improvement project in adult intensive care units: overview of the Improving Medicine Through Pathway Assessment of Critical Therapy in Hospital-Acquired Pneumonia (IMPACT-HAP) study. Crit Care 2011;15:R38.CrossRefGoogle ScholarPubMed
Scales, DC, Dainty, K, Hales, B, et al. A multifaceted intervention for quality improvement in a network of intensive care units: a cluster randomized trial. JAMA 2011;305:363372.CrossRefGoogle Scholar
Wooldridge, AR, Carayon, P, Hundt, AS, Hoonakker, PLT. SEIPS-based process modeling in primary care. Appl Ergon 2017;60:240254.CrossRefGoogle ScholarPubMed
Ofosu, B, Ofori, D, Ntumy, M, Asah-Opoku, K, Boafor, T. Assessing the functionality of an emergency obstetric referral system and continuum of care among public healthcare facilities in a low resource setting: an application of process mapping approach. BMC Health Serv Res 2021;21:402.CrossRefGoogle Scholar
Youngquist, P, Carroll, M, Farber, M, et al. Implementing a ventilator bundle in a community hospital. Jt Comm J Qual Patient Saf 2007;33:219225.Google Scholar
Sinuff, T, Muscedere, J, Cook, D, Dodek, P, Heyland, D. Ventilator-associated pneumonia: Improving outcomes through guideline implementation. J Crit Care 2008;23:118125.CrossRefGoogle ScholarPubMed
Sinuff, T, Muscedere, J, Cook, DJ, et al. Implementation of clinical practice guidelines for ventilator-associated pneumonia: a multicenter prospective study. Crit Care Med 2013;41:1523.CrossRefGoogle ScholarPubMed
Zaydfudim, V, Dossett, LA, Starmer, JM, et al. Implementation of a real-time compliance dashboard to help reduce SICU ventilator-associated pneumonia with the ventilator bundle. Arch Surg 2009;144:656662.CrossRefGoogle ScholarPubMed
Teixeira, PG, Inaba, K, Dubose, J, et al. Measurable outcomes of quality improvement using a daily quality rounds checklist: two-year prospective analysis of sustainability in a surgical intensive care unit. J Trauma Acute Care Surg 2013;75:717721.CrossRefGoogle Scholar
Green, LA, Nease, D Jr, Klinkman, MS. Clinical reminders designed and implemented using cognitive and organizational science principles decrease reminder fatigue. J Am Board Fam Med 2015;28:351359.CrossRefGoogle ScholarPubMed
Shojania, KG, Jennings, A, Mayhew, A, Ramsay, CR, Eccles, MP, Grimshaw, J. The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database Syst Rev 2009:CD001096.Google ScholarPubMed
Demakis, JG, Beauchamp, C, Cull, WL, et al. Improving residents’ compliance with standards of ambulatory care: results from the VA Cooperative Study on Computerized Reminders. JAMA 2000;284:14111416.CrossRefGoogle Scholar
Krimsky, WS, Mroz, IB, McIlwaine, JK, et al. A model for increasing patient safety in the intensive care unit: increasing the implementation rates of proven safety measures. Qual Saf Health Care 2009;18:7480.CrossRefGoogle Scholar
Kim, MM, Barnato, AE, Angus, DC, Fleisher, LA, Kahn, JM. The effect of multidisciplinary care teams on intensive care unit mortality. Arch Intern Med 2010;170:369376.Google ScholarPubMed
Stone, ME Jr, Snetman, D, O’Neill, A, et al. Daily multidisciplinary rounds to implement the ventilator bundle decreases ventilator-associated pneumonia in trauma patients: but does it affect outcome? Surg Infect (Larchmt) 2011;12:373378.CrossRefGoogle ScholarPubMed
Weled, BJ, Adzhigirey, LA, Hodgman, TM, et al. Critical care delivery: the importance of process of care and ICU structure to improved outcomes: an update from the American College of Critical Care Medicine Task Force on Models of Critical Care. Crit Care Med 2015;43:15201525.CrossRefGoogle ScholarPubMed
PH 101 Series. Introduction to public health surveillance 2018. Centers for Disease Control and Prevention website. Accessed June 1, 2023.Google Scholar
Mortimer, F, Isherwood, J, Wilkinson, A, Vaux, E. Sustainability in quality improvement: redefining value. Future Healthc J 2018;5:8893.CrossRefGoogle ScholarPubMed
US Department of Health & Human Services. About translation updated September 2022. National Center for Advancing Translational Sciences webstite. Accessed June 1, 2023.Google Scholar
Straus, SE, Tetroe, J, Graham, I. Defining knowledge translation. CMAJ 2009;181:165168.CrossRefGoogle ScholarPubMed
Rawson, TM, Moore, LSP, Tivey, AM, et al. Behaviour change interventions to influence antimicrobial prescribing: a cross-sectional analysis of reports from UK state-of-the-art scientific conferences. Antimicrob Resist Infect Control 2017;6:11.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Glossary of Terms

Figure 1

Table 2. Methods for Measurement

Figure 2

Table 3. Implementation Frameworks

Figure 3

Table 4. Other Resources

Supplementary material: File

Trivedi et al. supplementary material

Trivedi et al. supplementary material

Download Trivedi et al. supplementary material(File)
File 17.6 KB