Advances in the Prevention and Control of HAIs
Development and Implementation of a Consensus Algorithm to Optimize Preoperative Antimicrobial Prophylaxis and Decrease Gram-Positive Surgical Site Infections for Cardiac and Orthopedic Procedures
Table of Contents
For Cardiac and Orthopedic Procedures
Barbara I. Braun, Loreen Herwaldt, Marin Schweizer, Joanne M. Hafner, Julia Moody, Cheryl L. Richards, Melissa A. Ward, Jason Hickok, Eli Perencevich, Ed Septimus
Surgical site infections (SSIs) are serious adverse events for patients. Rates after cardiac and orthopedic procedures range from 0.4 percent to 5 percent. Over 40 percent are caused by gram-positive organisms, particularly Staphylococcus aureus, about half of which are resistant to antimicrobials. Current guidelines and practices for preventing SSIs among high-risk patients vary widely. The aim of the Study to Optimally Prevent Surgical Site Infections (STOP SSIs) was to systematically evaluate existing research, guidelines, and current practice; recommend an evidenced-based bundle of practices (algorithm) for screening, decolonization, and optimizing preoperative antimicrobial selection; and implement the algorithm as a quality improvement initiative in a group of community hospitals to assess its efficacy. A 12-member expert panel advised the project and addressed gaps in the literature and guidelines. The algorithm was implemented into the usual care processes of 20 hospitals in a large national health care system, under the leadership of the corporate infection prevention staff. Lessons learned from implementation included that teams need to allot sufficient time and resources for (1) information technology health care personnel (HCP) to make necessary changes in the electronic health record (EHR) system for standardized data collection and monitoring compliance; (2) educators to develop and conduct programs for initial and ongoing training of all HCP who will use the protocol, including HCP on all shifts, temporary staff, and new staff; (3) project liaisons to develop systems that allow HCP in different service lines to coordinate new activities; and (4) providers to consider and adopt practice changes. Facilitating factors included a strong centralized infrastructure with a common EHR, active involvement of health system leaders and physician champions, and sharing of strategies and solutions among sites to overcome challenges.
Surgical site infections (SSIs) are serious adverse events for patients; SSI rates after cardiac and orthopedic procedures range from 0.4 percent to 5 percent.1,2 Data from over 5,000 SSIs reported to the National Healthcare Safety Network (NHSN) in 2006–2007 revealed that over 40 percent were caused by gram-positive organisms, particularly Staphylococcus aureus (S. aureus) organisms or coagulase-negative staphylococci, and about half of the isolates were resistant to antimicrobials (e.g., methicillin-resistant S. aureus [MRSA]).3 Recent studies also found that the percentage of SSIs caused by resistant organisms is increasing.4,5 A 2010 survey conducted by the Infectious Diseases Society of America Emerging Infections Network found that practices regarding preoperative nares screening for colonization, decolonization, and choice of antimicrobial prophylaxis agents varied dramatically.6 This practice variation most likely relates to variation in the results of studies assessing ways to identify and treat patients colonized with S. aureus and other patients at high risk of SSI before their operations. The Study to Optimally Prevent Surgical Site Infections, known as "STOP SSIs," was designed to determine if screening for S. aureus colonization, decolonizing carriers, and providing MRSA carriers with vancomycin and cefazolin for perioperative prophylaxis would decrease gram-positive SSIs (go to Appendix A for the list of participants).
The primary aim of the project's first phase was to systematically evaluate existing research, guidelines, and current practice and to recommend an evidenced-based bundle of practices (algorithm) for screening, decolonization, and optimizing preoperative antibiotic selection. The primary aim of the second phase was to implement the algorithm as a quality improvement initiative in a group of community hospitals and assess whether implementation of the algorithm was associated with reduced rates of S. aureus deep incisional and organ space SSIs after select cardiac and orthopedic operations. These procedures were chosen because gram-positive organisms are the most important pathogens causing SSIs in these operations, and these infections can be catastrophic.7–10 If health care workers understand the factors that influence algorithm implementation, they can create systems and tools that facilitate rapid translation of the evidence into practice. Thus, this project also aimed to identify factors that facilitated or impeded algorithm implementation. This paper reports on the methodology for algorithm development and implementation. The efficacy of the algorithm will be described elsewhere.
Phase I (August 2010 to August 2011) comprised three concurrent activities: a systematic literature review and meta-analysis; a review of existing preoperative prophylaxis guidelines; and a "call for algorithms" to identify examples of current practices. Phase II (September 2011 to August 2013) activities included site recruitment, training, preparation, and implementation of the evidence-based algorithm in a diverse group of community hospitals. The project leadership team included the principal investigator and co-investigators from an academic medical center, staff members from the coordination center, a quality improvement-related organization, and clinical leadership from the corporate offices of a large national health care system. A 12-member technical expert panel (TEP) composed of nationally recognized cardiovascular and orthopedic surgeons, anesthesiologists, infectious disease specialists, hospital epidemiologists, and medical quality improvement experts advised the investigative team.
Methodology for Algorithm Development
The methodology for conducting the systematic literature review and meta-analysis and associated results was recently published.11 The investigative team searched the literature using the following data sources to identify relevant studies: PubMed (1995–2011), the Cochrane Database of Systematic Reviews, CINAHL, EMBASE, and clinicaltrials.gov. TEP members were queried to identify relevant English-language guidelines from the United States and Europe. A team member created a summary table of guidelines describing recommendations for preoperative screening, preoperative bathing, nasal decolonization, and selection, dosing, and administration of perioperative antibiotics for prophylaxis relevant to general, orthopedic, and cardiothoracic procedures.
The investigative team disseminated the call for algorithms via Web sites, e-newsletters, letters, blogs, and message boards at professional society meetings from November 2010 through February 2011. The call for algorithms stated that we were seeking "…examples of algorithms, protocols, pathways, policies and procedures, and standing orders that address selection and administration of antimicrobial prophylaxis for cardiac and orthopedic surgery patients. If your organization routinely screens preoperative patients for MRSA, it would be helpful to also include screening and de-colonization algorithms and protocols." Partners in disseminating the call included several medical and nursing surgical and infectious disease-related professional associations.
Investigative team members summarized the responses to the call for algorithms and the results of the systematic literature review as an algorithm addressing preoperative screening for S. aureus, decolonization of S. aureus carriers, and perioperative prophylaxis. They reviewed the algorithm with the TEP and revised it based on TEP members' recommendations (Figure 1).
Methodology for Implementing the Algorithm and Evaluating Its Efficacy
The investigative team designed Phase II of the project to assess the effectiveness of the algorithm under the conditions of usual practice as a quality improvement initiative rather than as a tightly controlled clinical trial. The team initially proposed to implement the algorithm in a sample of unrelated volunteer sites. However, the infrastructure needed for consistent laboratory testing, dispensing inpatient and outpatient medications and supplies, data collection, and infection surveillance exceeded the original project budget. A team member suggested an alternative of implementing the algorithm in a large national health care system with a centralized institutional review board (IRB), robust computerized information systems and electronic health records (EHRs), and standardized SSI surveillance systems. A partnership with such a system would allow the investigative team to undertake a quasi-experimental study within the time and budgetary parameters outlined in the original contract.
A health care system agreed to implement the algorithm in conjunction with the study because it was planning to launch an enterprise-wide initiative to reduce SSIs and had implemented an initiative for screening nares for MRSA colonization.12 The health care system's infection prevention leadership, together with team members from the academic medical center and from the coordination center, developed an informational webinar and flyers to introduce the project to over 80 hospitals that performed the procedures of interest. The following criteria were used to determine whether a hospital was eligible to participate: (1) the hospital performed the procedures of interest; (2) the hospital had not already incorporated into its practice all algorithm elements—screening for S. aureus carriage, decolonizing carriers with mupirocin and chlorhexidine, and giving MRSA carriers cefazolin and vancomycin for perioperative prophylaxis; hospitals that provided decolonization to both MRSA- and methicillin-susceptible S. aureus (MSSA)-positive patients were excluded, but hospitals that provided decolonization only to MRSA-positive patients were eligible for inclusion; and (3) the hospital was able to provide data on SSIs for at least the 2 years preceding implementation. Health care personnel from interested sites completed an electronic survey, sent by the health care system, about current surgical infection prevention practices. A priori power calculations indicated that at least 20 sites would be needed to ensure that the power of the study would be adequate to detect statistically significant differences between the pre- and post-intervention periods.The investigative team trained project liaisons (hospital study coordinators) during a 1½-day in-person meeting in April 2012. The meeting objectives were to share detailed protocol information and to build enthusiasm and relationships among sites and the leadership team. At the meeting, the liaisons developed implementation plans for the multidisciplinary teams at their sites. Training on the second day coincided with a meeting of the investigative team and the TEP, which allowed both liaisons and experts the opportunity to interact face-to-face and to discuss questions that arose during the first day of training. Liaisons and participating hospitals did not receive any direct compensation for their involvement in this project, but they were reimbursed for costs associated with attending training.
The investigative team used several mechanisms to provide ongoing training for health care personnel (HCP) at the sites and to ensure communication with the participants. Representatives from the health care system and the coordinating center developed a modular electronic procedure manual centralized on the health care system's intranet, a site familiar to the liaisons. The modular format allowed team members to update sections as needed. The investigative team conducted frequent (biweekly, then monthly) interactive "coaching call" webinars to answer questions, disseminate study updates, allow sites to discuss challenges and share facilitating strategies, and promote rapid implementation and "hardwiring" of the algorithm. Also, project staff periodically distributed a frequently asked questions (FAQ) document and supported a central electronic mailbox as an additional method of communication.
Implementation of the algorithm required (1) preparatory activities, such as establishing processes for identifying eligible patients, obtaining necessary equipment and supplies, and ensuring local medical staff approval; (2) implementation activities, such as educating patients and obtaining swabs; and (3) maintenance activities, such as ongoing monitoring to ensure consistent adherence to the algorithm. The date of implementation (the "go-live" date) was the date each hospital reported that its systems and processes were operational. During maintenance activities, liaisons were asked to complete a monthly structured review of 10–15 eligible cases to determine if the algorithm was being consistently applied and documented in the EHR. This self-audit was used to identify potential issues and trigger followup activities to address problems. The audit forms were submitted to the coordinating center and reviewed during one-on-one telephone calls between liaisons and project staff.
As described by Berwick,13 the rate of dissemination of an innovation depends upon three sets of factors: (1) the characteristics of the innovation or intervention itself (e.g., complexity, trialability, relative benefit); (2) the context of implementation (e.g., how the change was implemented, who implemented the change, what planning and training were done, and how open the organizational culture was to change); and (3) the characteristics of the individuals receiving the innovation (e.g., were they innovators and early adopters, or traditionalists requiring strong evidence for change). In this project, the investigative team had information regarding the characteristics of the innovation and the context of implementation but not about the characteristics of those receiving the innovation (beyond the titles and credentials of the liaisons). To gain additional information about the implementation experience within and across sites, the investigators took notes during coaching calls and calls with individual sites, and they looked for common themes and factors that impeded or enhanced implementation.
The project was reviewed by the IRBs associated with the academic medical center and the coordinating center. The health care system determined that this project was a quality improvement initiative and that the activity the hospitals were undertaking was not considered research with human subjects because there was no interaction or intervention with live humans or their identifiable data for research purposes: the hospitals were doing this on their own for treatment purposes based on the project's merits.14 The AHRQ liaison to the Federal Office of Management and Budget (OMB) also reviewed the project and approved a clinical exemption for the project.
The investigative team summarized 19 guidelines for the TEP regarding preoperative screening, preoperative bathing, nasal decolonization, and selection, dosing, and administration of perioperative antibiotics for prophylaxis relevant to orthopedic or cardiac procedures.1,2,15–31 Of these, eight recommended use of (1) a β-lactam (cefazolin, cefuroxime, or cefamandole) as the first-line agent for preoperative prophylaxis; (2) vancomycin, clindamycin, or a fluoroquinolone for patients with established allergies to β-lactam agents; and (3) vancomycin for patients with known previous MRSA infection or colonization. Most guidelines recommended β-lactams for routine prophylaxis, and one recommended adding gram-negative coverage if vancomycin was used.1 Three guidelines recommended screening patients' nares, of which only one guideline specifically recommended preoperative screening for MRSA colonization. Two guidelines recommended decolonization of S. aureus carriers before the procedure, and one recommended preoperative bathing with an antiseptic soap. One specifically recommended screening patients for S. aureus carriage and decolonizing carriers with mupirocin.
Several TEP members were aware that the American Society of Health-System Pharmacists (ASHP) was working with four major professional societies to develop a new consensus guideline regarding perioperative prophylaxis, which was scheduled for release in summer 2011.18 TEP members who were on the ASHP guideline committee shared their knowledge of the draft recommendations, which helped the investigative team address gaps identified during guideline review.
Call for Algorithms
Forty-eight hospitals submitted information in response to the call for algorithms. Most responses were from infection preventionists, infectious disease physicians, and hospital epidemiologists. The submissions were in the format of policy and procedures (8), standing order sets (15), hospital (local) guidelines (11), flowcharted algorithms (6), and narrative text sent via email (8). Many submissions included more than one format (e.g., policy and order set). Fifteen algorithms were specific to cardiac procedures and 19 to orthopedic procedures; 10 applied to both or did not specify procedures. The majority of the hospitals that submitted algorithms were large (>300 beds) teaching institutions, and all were located in urban areas.
Overall, the algorithms lacked specificity and demonstrated substantial practice variability. Staff at the coordination center could not identify preferred formats or a single widely used algorithm. However, the coordinating center staff's ability to draw conclusions from the call for algorithms was severely limited because information was collected using an unstructured approach; due to regulatory and time constraints, staff could not follow up with respondents. Nevertheless, the investigative team concluded that, given the variation in responses, this project could have substantial benefit by providing evidence-based guidance in a standardized format.
The final algorithm developed in Phase I recommended a bundle of practices: (1) preoperative screening of the nares for both MRSA and MSSA; (2) preoperative chlorhexidine gluconate (CHG) bathing; (3) intranasal mupirocin to decolonize carriers and patients whose screening results were unknown prior to their operation; and (4) cefazolin and vancomycin as perioperative prophylaxis for patients colonized with MRSA or whose screening test results were unknown at the time of the operation (Figure 1). Details associated with each of the components are available upon request.
Implementing the Algorithm
Initially, 42 hospitals expressed interest in participating in the study, of which 25 completed the followup questionnaire. None of these 25 hospitals had previously implemented all of the practices recommended in the algorithm, although some had applied individual components to their patient populations. Sites had the option of applying the algorithm only to patients undergoing cardiac operations, only to patients undergoing orthopedic operations, or to both patient populations. The 25 eligible sites were sent invitation letters; five hospitals declined participation before the training session because they identified resource constraints, and one withdrew from the project after training but before implementing the algorithm because several key quality staff members left their positions. One additional site joined the project 1 month after the training session. Ultimately, 20 hospitals implemented the algorithm.
All sites were community hospitals that varied in size from 52 to 514 beds, and most were in the South (Table 1). Nine sites implemented the algorithm for both orthopedic and cardiac procedures, eight implemented it for orthopedic operations only, and four implemented it for cardiac procedures only.
Thirty-one representatives from 19 sites attended the in-person training session. Twelve sites sent two people, of whom three were physician champions. Most liaisons (19) were nurses with advanced degrees or certifications; two were clinical laboratory scientists. Fourteen liaisons worked as infection preventionists, three were directors of surgical services, one was the vice president of cardiovascular services, one was the director of quality and risk management, and one was the manager of the post-anesthesia care unit.
|Hospital||Census Division Region||Location (Rural / Urban)||Teaching Status||Bed Size (S <100; M 100-299; L >299)||Procedures Included in Implementation (Cardiac / Orthopedic / Both)||Date Hospital Implemented Algorithm|
|5||South Atlantic||Urban||Non -teaching||M||Cardiac||8/1/2012|
|6||South Atlantic||Urban||Minor teaching||M||Orthopedic||10/9/2012|
|13||South Atlantic||Urban||Non-teaching||L||Both||Orthopedic on 8/6/2012; Cardiac on 8/22/12|
|14||South Atlantic||Urban||Minor teaching||L||Orthopedic||7/25/2012|
|15||West North Central||Urban||Non-teaching||S||Orthopedic||7/15/2012|
|16||West South Central||Urban||Minor teaching||S||Both||6/15/2012|
|17||West South Central||Urban||Non-teaching||M||Orthopedic||6/18/2012|
|18||West South Central||Urban||Minor teaching||L||Both||6/25/2012|
|19||West South Central||Urban||Non-teaching||L||Cardiac||6/11/2012|
|20||West South Central||Urban||Minor teaching||L||Both||6/302012|
Notes: All were located in urban settings; 15 hospitals were non-teaching, and 5 were classified as minor teaching status. Consolidated Metropolitan Statistical Area (CMSA): Mountain (ID); South Atlantic (FL, GA, SC, VA); West South Central (LA, TX); West North Central (MO); New England (NH). Bed size: Small <100; Medium 100-299; Large >299. American Medical Association teaching status, CMSA, bed size data, and location were obtained from the American Hospital Association Annual Survey Database, FY 2010 edition. All hospitals offered orthopedic surgical services.
During May and June 2012 (the preparation phase), HCP from numerous service lines at each hospital accomplished several important activities (Figure 2): establishing a multidisciplinary implementation team and plans; setting up systems and processes to identify eligible patients; organizing and providing training materials for staff, patients, and physician offices external to the system hospitals when centralized preadmission testing services were not available at the hospital; obtaining medical executive committee approval for new project-specific order sets; working with local information technology staff to ensure that the study-specific charting screens would be available to staff, etc. Although Figure 2 places each activity within a service line, many activities were accomplished by members of different services, and HCP often implemented several tasks in parallel under the coordination and leadership of the site liaison. The corporate investigative team members helped guide HCP through the preparation activities and responded to questions and requests for assistance from individual sites as needed.
To minimize workload for the sites, the algorithm was integrated into usual care processes to the greatest extent possible. Health care system information technology staff developed specific query screens and fields for data entry and integrated them into the EHR. The health care system's supply chain services ensured that necessary supplies and equipment were obtained through the usual supply chain management process.
Given the number and complexity of activities during the preparation phase, several sites needed more time than expected to implement the intervention. As shown in Figure 3, 11 sites reported they had fully implemented the algorithm by the start date of July 1, 2012, and all 20 sites had implemented the algorithm by the end of October 2012.
Examples of challenges and facilitators commonly reported during the first 6 months of the project are described in Appendix B. During the preparation period, the most common challenges were delays in obtaining medical executive committee approval for revised preoperative orders (due to infrequent meetings and full agendas) and obtaining surgeons' commitment to implement the algorithm. Sites also had difficulty establishing reliable processes for screening and decolonizing patients who were admitted through the emergency department for urgent or emergent procedures, bypassing the usual pre-admission process, particularly on weekends and holidays.
The facilitating factors that sites reported most commonly included a corporate physician champion who addressed physicians' concerns regarding specific recommendations in the algorithm and corporate and local technical staff who addressed information technology issues. Additionally, many sites had participated in previous centralized research initiatives, and all sites were screening for MRSA before the project was initiated.
The sites audited adherence to the algorithm components, which helped the local participants and the investigative team identify opportunities for improvement. For example, project liaisons who investigated adherence issues often found problems with documentation in the EHR rather than failure to apply the algorithm. However, the liaisons had difficulty continuing regular self-audits over time, in part because they had to retrieve information from multiple reports.
The STOP SSIs project was designed to develop and evaluate an evidence-based algorithm to prevent SSIs associated with gram-positive bacteria. The investigative team identified important challenges and facilitators during both phases of the project. During algorithm development, the team identified few high-quality studies that evaluated practices to prevent these infections. Although numerous groups have pertinent guidelines, none of the guidelines address a full range of preventive measures. Moreover, the algorithms submitted to the investigative team varied substantially in form and content, which indicates that hospital practices for communicating recommended practices, and the practices themselves, are not standardized. Given the lack of standardized guidelines, the investigative team benefited greatly from the TEP members' broad range of expertise and their work with diverse professional societies.
The clinical, technological, and research-related resources of the health care system in which this process improvement project was conducted were essential to rapidly implementing the algorithm at 20 volunteer sites and, thus, to the project's success. Frequent coaching calls helped participants expedite the implementation because they were able to learn about solutions that other participants had found for common obstacles, and they could ask questions of the investigative team. Other quality improvement collaboratives have demonstrated that sharing tools such as checklists and reminder stickers early in the implementation process allows sites to facilitate rapid change.32
Investigative team members noted that they often heard the same challenges repeated during different coaching calls. When they reviewed the minutes of the calls, they expected to find that multiple sites had reported the same challenges. Instead, they found that single sites tended to repeat the same challenge during several calls, indicating that HCP had not resolved the issues. This observation suggests that all hospitals do not have the same ability to overcome challenges, and that some challenges are harder to overcome than others. Future research should assess the factors that limit or enhance a hospital's ability to overcome challenges and the factors that make some challenges very difficult to resolve.
The investigative team relearned the age-old lesson that implementation always takes more time and resources than predicted. In particular, teams need to allot sufficient time and resources for (1) information technology staff to make necessary changes in the EHR to allow data collection and adherence monitoring; (2) educators to develop and conduct programs for initial and ongoing training of all HCP who will use the protocol, including HCP on all shifts, temporary staff, and new staff; (3) project liaisons to develop systems that allow coordination of new activities between the inpatient and outpatient setting, especially when a centralized preadmission testing area is not available; and (4) physicians to consider and adopt the practice changes.
The bundle of practices constituting the SSI prevention algorithm is a complex intervention because it requires patients to follow instructions for treating themselves with a topical antimicrobial agent and bathing with a medicated soap, and it requires collaboration of HCP from multiple disciplines—surgical departments, outpatient offices, inpatient perioperative nursing, preoperative surgical services, postoperative surgical units, infection prevention, information technology services, procurement, pharmacy, and the laboratory. Alexander and Hearld have stated that the implementation of complex interventions must be assessed systematically because the greater the complexity of an intervention, the greater the probability that some components of the intervention will not be implemented fully.33 In general, implementers assume that the components of an intervention will function as a system to achieve the desired effects. However, if some components are not implemented fully, or if the timing and intensity of the implementation varies by component, then the outcomes do not represent the outcomes of the full "complex intervention." For the current project, the investigative team assumed that all components of the algorithm were essential and that they would act synergistically to reduce infections. To prove this assumption, the investigative team must assess adherence to each algorithm component (screening, decolonization, and appropriate prophylaxis). Future investigators should ensure that they have a mechanism for documenting which components of a complex intervention are implemented, when each component is implemented, and the extent to which the component is implemented (e.g., partially or fully) so that they can assess each component's contribution to the overall outcome.
This project has several important limitations. The investigative team was limited in its ability to collect data about existing algorithms and factors affecting implementation due to regulatory requirements associated with the Government contract. Thus, the investigative team could not collect information about characteristics of the hospitals or programs implementing the existing guidelines, the attitudes of their staff about the guidelines, and the organizational cultures of the institutions in which the guidelines were implemented. Also, the quality of interpersonal relationships within and between departments, and the skills and experience of the staff championing the initiative, are known to greatly affect the success of implementation.34 The current study did not have the resources to assess these subtle but important factors related to local culture and level of collaboration. Since the project was conducted within a health care system, the findings may not be generalizable to individual hospitals that do not have access to a similar infrastructure. On the other hand, implementation in a variety of community hospitals may be considered a strength, since these facilities are more like the overall hospital population in the United States than are academic health centers.
The current guidelines regarding prevention of gram-positive SSIs vary as do current practices. Thus, an algorithm that synthesizes current knowledge and expert opinion may help standardize practice and improve patient care. Hospitals in a health care system differed in their ability to implement the evidence-based algorithm for preventing SSIs, despite significant support from the health care system's strong infrastructure. Health care personnel implementing process improvement interventions must have sufficient time and resources to establish new cross-departmental systems that facilitate collaboration, develop essential educational programs, and create the information technology systems needed to support the implementation and assess the results.
The Study to Optimally Prevent Surgical Site Infections (STOP SSIs) project was funded under contract number HHSA 29022006100021i, Accelerating Change and Transformation in Organizations and Networks (ACTION I) program initiative, "Optimizing PreOp Antibiotic Prophylaxis for Cardiac and Orthopedic Procedures," from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. The findings and conclusions in this document are those of the authors, who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
We sincerely thank the study participants listed in Appendix A for their hard work and dedication to quality improvement. We are grateful for their tremendous efforts in providing us with a wealth of information and insight into their implementation experiences. We also thank the members of the Technical Expert Panel—Michael Banbury, MD; Dale Bratzler, DO, MPH; Joseph Buckwalter, MD, MS; E. Patchen Dellinger, MD; Richard Embrey, MD, MBA; Stephan Harbarth, MD; Keith Kaye, MD, MPH; Matthew Koff, MD; Randy Loftus, MD; Vincent Pellegrini, MD; James Steinberg, MD; and Edward Wong, MD—for their contributions to the algorithm development and to the study design and implementation. We also gratefully acknowledge the contributions of Jonathan B. Perlin, MD; David Vulcano, LCSW, MBA, CIP, RAC; Jerod M. Loeb, PhD; Michele R. Bozikis, MPH; and Darryl Gray, MD, ScD, FAHA.
Division of Healthcare Quality Evaluation, The Joint Commission, Oakbrook Terrace, IL (BIB, JMH, CLR). Department of Internal Medicine, Carver College of Medicine, the University of Iowa, Iowa City, IA (LH, MS, EP). Infection Prevention Services, Clinical Services Group, Hospital Corporation of America, Nashville TN (JM, JH). Clinical Quality, Safety, and Performance Improvement (CQSPI), the University of Iowa Hospitals and Clinics, University of Iowa, Iowa City, IA (MAW). Infection Prevention and Epidemiology Clinical Service Group, HCA Healthcare System, Houston, TX, and Department of Internal Medicine, College of Medicine, Texas A&M Health Science Center, Bryan, TX (ES).
Address correspondence to: Barbara Braun, PhD, Division of Healthcare Quality Evaluation, The Joint Commission, One Renaissance Boulevard, Oakbrook Terrace, IL 60181; Email: firstname.lastname@example.org.
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Appendix A. Project Participants
Doris (Paula) Bates, RN, ICP; Preston Blake, MD; Peter J. Bolan, MD, Clarence Book, RN, CIC; Darien W. Bradford, MD; Eleanor Cardwell, RN, BSN, CDE; Leonard Charns, RN, BSN, CIC; Muddasar N. Chaudry, MD; Maribeth Coluni, RN, BSN; Sheryl Creech, RN, ICP; Van (Vanda) L. Davidson, MD; Sande Day, RN, BSN, CCRN; Debra Douglas, RN; Michael Driks, MD; Stacey Estes-Juve, RN, CNS-BC, ONC; Charlotte Evans, RN, BSN, CIC; Larry Feinman, DO; Manuel Feliberti, MD; Pablo Feuillet, MD; Anne Finnerty, RN, BSN, CNOR; Sheri Franklin, RN, BSN; Marcy Frisina, RN, BSN, MBA, ACNO; Stuart Gardner, MD; James R. Garrison Jr., MD; Trudy Grillo, RN, MSN; Carol Harmon, RN; Mark Hebert, MD; Belinda Holley, RN; Julie Hunter, RN, ICP; David J. Itkin, MD; Trudy Jackson, RN; Joan Jenne, RN, MHA, CIC; Janna Jernigan Matautia, RN, BSN; Aida Jimenez-Sanchez, MD; Joe Johnston, MD; William Jones, RN; Teresa Kenyon, RN; Eric Keyser, MD; Sharon Kurtz, RN, MPH, CIC; Denise Leaptrot, CIC; James M. Lovelace, MD; Christine Ludwig, RN; Robert J. Maddalon, MD; Ginna Maggard, BSN, RN, COHN/CM, CIC; Mariamma Mathew, RN; Pat Mayberry RN, CPHQ, CIC; Lisa Metheney, RN, BSN; Laura Netardus, RN, MN, CIC; David Jason Oberste, MD; Charlotte Pate, RN, CNOR; Christopher Phelps, MD; Toni Polk, RN, CEN, CIC; Maria Robles, RN, BSN; Becky Robinson, ICP; Richard Sall, MD; Michaela Schulte, MD; Patrice Stark, RN, BSN, CPHRM; Eric S. Stem, MD; Teresa Stowasser, MSN, CIC; Regina Suniga, RN; Debora Tichy, RN; Martha Wassell, MT (ASCP), MPH, CIC; Kathleen Wright, RN, MSBA, LHRM; Monica Yates, RN, BSN, CIC.
|Category||Examples of Challenges||Number of Unique Sites Reporting a Challenge at Least Once||Examples of Facilitators||Number of Unique Sites Reporting a Facilitator at Least Once|
|Technology, equipment and supplies|
|Availability of equipment and supplies||Delays in receiving equipment for CHG cloths, CHG cloth warmers, or lab to identify MSSA||3–5||Site supplied CHG liquid to pre-op patients before participating in project; site set up a system to deliver CHG cloths to patients||1-2|
|Hospitals that did not use PCR experienced delays in getting MSSA results before surgery.||3–5||Participation in project enabled hospitals to justify the funds to purchase PCR equipment||1-2|
|Electronic health record||Sites that screened patients more than 14 days before surgery manually entered screening results||10 or more||Sites were familiar with documentation from established system MRSA prevention initiatives where look-back period was up to 30 days||10 or more|
|Delays in setting up data entry screens and local installation and customization||3–5||Corporate and local IT staff provided technical assistance||3–5|
|Communicating results of screening tests performed at community labs was more difficult than communicating the results of tests done in the hospital's lab||1-2||Sites transitioned lab work to a central hospital-based lab||1-2|
|Turnover of key project personnel||Turnover of nurse managers, pre-admission unit HCP, staff nurses on nursing units||3–5||Standardized procedure documents and archived webinars posted on health system intranet were used to re-educate HCP as needed; site designated a person to answer questions in OR; site created a PowerPoint presentation; designated champions on each floor met monthly; incorporated protocol into orientation for new hires||10 or more|
|Turnover of site liaisons||1-2||Trained alternate or co-liaison as backup or assistant||3–5|
|Turnover of surgeons and other clinicians||Surgical volume decreased when a surgeon left||1-2||New surgeons were highly supportive of project||1-2|
|New surgeons or physician assistants missed opportunities to apply algorithm||6-9||Provided one-on-one training as soon as possible||6-9|
|Physicians and other providers' attitudes / perceptions of algorithm components||Physicians were concerned about nephrotoxicity when using vancomycin for the ‟unknown group;” that overuse of mupirocin would contribute to mupirocin resistance; about using gentamicin for β-lactam allergic patients||10 or more||One-on-one calls and onsite visits from the health care system physician champion; shared updated consensus guidelines published January 2013||10 or more|
|Supportive surgeons expressed desire to apply algorithm to other orthopedic surgical groups||1-2|
|Temporary staff||Agency staff may not be trained in applying protocol, especially those working weekends, holidays, and nights||1-2||Utilized just-in-time training resources to fast-track care practices|
|Site-specific systems and processes|
|Documentation of care processes in EHR||Nurses sometimes did not select necessary study screens; not all HCP could document directly in EHR and relied on internal communication||3–5||Liaisons re-educated staff to increase awareness of procedures|
|Coordination and communication between surgeons' offices and inpatient preoperative surgical staff||Surgical office staff did not obtain swabs or educate patients about the protocol||1-2||Liaisons traveled to surgeons' outpatient offices to enhance relationships, provide education, and engender embracing of best practices||1-2|
|Preoperative ordering of mupirocin, CHG baths, and antimicrobial prophylaxis||Order for intranasal mupirocin or for CHG bathing missing||10 or more||Gave pharmacy ownership of mupirocin order process; used a standing order set (e.g., orders part of the “open heart package”)||3–5|
|Process for medical executive committee to approve new orders||Approval of new orders delayed because the committee met infrequently or cancelled meetings||6-9||Physician champion and facility liaison obtained pre-approval from committee chair in anticipation of formal adoption|
|Communication of lab screening results to doctors and pharmacy||Surgeons or pharmacy did not receive lab results before the operation||3–5||Daily list of lab results sent to liaison||1-2|
|Process for identifying and applying the algorithm to patients who need urgent or emergent procedures and bypass the usual preoperative processes||Algorithm not applied to patients that were admitted through the emergency department, from ICU, catheterization lab, etc.||10 or more||Liaisons used reminders, checklists, chart stickers/flags; cardiac catheterization lab obtained swabs; admissions staff notified directors by email of patients coming in for procedures in their areas; nurses printed a checklist that accompanied the patient||10 or more|
|Process for applying algorithm outside of normal business hours||Health care personnel on nights, weekends, and holidays may be unfamiliar with protocol, resulting in missed opportunities||10 or more||House supervisor called or paged liaison, who then contacted floor nurse; liaison used reminders and checklists||10 or more|
|Process for postoperative mupirocin orders||Failure to re-order mupirocin post-op when indicated; failure to discontinue mupirocin orders or stop applying mupirocin post-op||6-9||Liaisons and physician champions re-educated physicians, nurse practitioners, and physician assistants; pharmacy monitored screening results; stopped mupirocin as needed||3–5|
|Process for training existing and new HCP on the protocol||HCP not familiar with algorithm because they were not trained or their training was delayed||3–5||Health care system posted standardized procedure documents and archived webinars on a common intranet; site designated a person to answer questions in OR; site created a PowerPoint presentation; site incorporated ongoing training into annual proficiency testing; manager made a pocket tool for CHG bathing instructions||1|
|Processes for monitoring and improving adherence to protocol||Continued inconsistent application of algorithm elements||10 or more||Investigative team required sites to submit audit forms periodically and discussed the results with the sites; liaisons provided rewards for HCP (e.g., stars, candy) who applied the algorithm correctly; real-time review of every eligible case when workload/volume permitted; daily (M-F) multidisciplinary team rounds on all orthopedic patients included assessment of compliance with STOP SSI algorithm; surgical care improvement project nurse led efforts to educate nurses and track study patients; liaisons conducted concurrent review; staff in pre-op holding area addressed missing practices on the day of surgery||10 or more|
|Procedure changes from an excluded procedure to an included procedure during the procedure||Algorithm was not applied because the scheduled procedure was not included (e.g., hip-nailing), but the procedure was changed to an included total hip replacement||1-2||Sites expanded the bundle of practices to apply to all orthopedic procedures that have the potential to be converted to an included procedure||1-2|
|Organization and external environment|
|Physical environment or technology is changed||Adapted to infrastructure changes such as CPOE implementation; construction of new ICUs or operating rooms; conversion of units to different functions||6-9|
|Related quality improvement initiatives and policy changes||New universal decolonization policy in the ICU could not be applied to STOP SSI patients; HCP were diverted to other priority issues||3–5||A crosswalk was created to help HCP apply the two protocols appropriately||10 or more|
|Publication of national guidelines||Publication of a major national consensus guideline on antimicrobial prophylaxis, which was consistent with the algorithm, was delayed more than a year||3–5||The liaisons and clinical champions used the guideline to improve physician adherence to the protocol when it became available midway through implementation||1-2|
|National public reporting requirements||NHSN 2013 updated SSI followup periods for procedures with implants to 90 days, which is consistent with the followup period for the project; several sites were already reporting SSIs to State agencies||10 or more|
|Change in department level or C-suite leadership||Turnover led to need for re-education (e.g., CEO, CNO, Co-chair, lab director)||6-9||Liaison provided awareness of evidence-based care practices similar to that for other key HCP||6-9|
|Change in patient care priorities||Community outbreaks of influenza and fungal meningitis associated with contaminated medication||3–5||Alternative liaisons, colleagues assumed additional responsibilities, reprioritized resources to support the implementation while addressing the other patient care priorities||1-2|
|Organizational changes||>HCP were diverted from project to support the application for trauma center status; changes in organization of nursing staff||1-2|
|Understanding bathing and decolonization instructions||Limited English proficiency||3–5||Patient instructions were translated into Spanish; patients were called and reminded; patient education packets were color-coded to indicate the screening test results||3–5|
|Adherence to instructions for applying mupirocin and CHG bathing||Outpatients forgot or failed to complete recommended number of baths for various reasons||1-2||Patients were instructed to discontinue bathing when patient's skin was sensitive to CHG or reacted adversely to CHG. Outpatients were called daily for reminders to purchase supplies (if not provided) and perform decolonization||1-2|
|Out-of-pocket costs for purchasing CHG liquid or mupirocin||High cost for unit-dosed mupirocin||1-2||The health care system approved in-kind contribution to cover the cost of CHG cloths; most hospitals provided CHG free of charge; patients were willing to purchase CHG since it would help them avoid SSIs||3–5|
|Adherence to filling out pre-op form and bringing it to the hospital when admitted||Patients forgot to bring the forms when they arrived for surgery||1-2||Nurses interviewed patients on day of surgery and documented their adherence; infection preventionists participated in pre-op classes for joint replacement patients||1-2|
Abbreviations: CEO = chief executive officer; CHG = chlorhexidine gluconate; CNO = chief nursing officer; CPOE = computerized provider order entry; ED = emergency department; EHR = electronic health record; HCP = health care personnel; ICU = intensive care unit; IT = information technology; MRSA = methicillin-resistant Staphylococcus aureus; MSSA = methicillin-susceptible S. aureus; NHSN = National Healthcare Safety Network; OR = operating room; SSI = surgical site infection.
Note: Due to limitations in the data collection methodology, the range of numbers represents how many unique sites reported the obstacle or facilitator, not the number that experienced the obstacle or facilitator.
Page originally created June 2014