Harmon, Lori

IMPORTANCE: Initial Society of Critical Care Medicine Discovery Viral Infection and Respiratory illness Universal Study (VIRUS) Registry analysis suggested that improvements in critical care processes offered the greatest modifiable opportunity to improve critically ill COVID-19 patient outcomes. OBJECTIVES: The Structured Team-based Optimal Patient-Centered Care for Virus COVID-19 ICU Collaborative was created to identify and speed implementation of best evidence based COVID-19 practices. DESIGN, SETTING, AND PARTICIPANTS: This 6-month project included volunteer interprofessional teams from VIRUS Registry sites, who received online training on the Checklist for Early Recognition and Treatment of Acute Illness and iNjury approach, a structured and systematic method for delivering evidence based critical care. Collaborators participated in weekly 1-hour videoconference sessions on high impact topics, monthly quality improvement (QI) coaching sessions, and received extensive additional resources for asynchronous learning. MAIN OUTCOMES AND MEASURES: Outcomes included learner engagement, satisfaction, and number of QI projects initiated by participating teams. RESULTS: Eleven of 13 initial sites participated in the Collaborative from March 2, 2021, to September 29, 2021. A total of 67 learners participated in the Collaborative, including 23 nurses, 22 physicians, 10 pharmacists, nine respiratory therapists, and three nonclinicians. Site attendance among the 11 sites in the 25 videoconference sessions ranged between 82% and 100%, with three sites providing at least one team member for 100% of sessions. The majority reported that topics matched their scope of practice (69%) and would highly recommend the program to colleagues (77%). A total of nine QI projects were initiated across three clinical domains and focused on improving adherence to established critical care practice bundles, reducing nosocomial complications, and strengthening patient- and family-centered care in the ICU. Major factors impacting successful Collaborative engagement included an engaged interprofessional team; an established culture of engagement; opportunities to benchmark performance and accelerate institutional innovation, networking, and acclaim; and ready access to data that could be leveraged for QI purposes. CONCLUSIONS AND RELEVANCE: Use of a virtual platform to establish a learning collaborative to accelerate the identification, dissemination, and implementation of critical care best practices for COVID-19 is feasible. Our experience offers important lessons for future collaborative efforts focused on improving ICU processes of care.

Abstract Background Little is known about the strategies used by critical care leaders to implement new practices in response to the COVID-19 pandemic. Prior studies have identified implementation factors, such as structured communication mechanisms and collaborative leadership styles, that facilitate implementation of new clinical practices. However, the association between differing implementation climates and COVID-19 clinical outcomes has not been examined. The purpose of this mixed methods study was to evaluate the relationship between implementation determinants and COVID-19 mortality rates. Methods We used mixed methods guided by the Consolidated Framework for Implementation Research (CFIR) to assess implementation factors that were associated with COVID-19 mortality rates across diverse critical care settings. Semi-structured qualitative interviews were conducted with critical care leaders and analyzed using the CFIR to rate the influence of constructs on implementation of new care practices. Qualitative and quantitative comparisons of CFIR construct ratings were performed between hospital groups with low versus high mortality rates. Results 31 clinical leaders were interviewed across 17 hospitals. Of the 13 relevant CFIR constructs, three constructs (implementation climate, leadership engagement, and engaging staff) had both qualitative and statistically significant quantitative correlations with mortality outcomes. An implementation climate governed by a trial-and-error approach was correlated with high COVID-19 mortality, while leadership engagement and engaging staff were correlated with low mortality. Another three constructs (needs of patient; organizational incentives and rewards; and engaging implementation leaders) were qualitatively different across mortality outcome groups, but these differences were not statistically significant. Seven constructs did not distinguish between low versus high mortality hospitals based on qualitative or quantitative comparison of CFIR construct ratings. Conclusions Application of the CFIR to identify and quantify constructs across critical care settings enabled us to characterize implementation barriers and facilitators at the practice, interpersonal, and provider level that were associated with COVID-19 mortality rates. Improving clinical outcomes during future public health emergencies will require reducing identified barriers associated with high mortality and harnessing salient facilitators associated with low mortality. Our findings suggest that clinical care implementation efforts should focus on avoiding trial-and-error approaches to adopting new practices, while promoting collaborative and engaged leadership styles.

Abstract Background The COVID-19 pandemic produced unprecedented demands and rapidly changing evidence and practices within critical care settings. The purpose of this study was to identify factors and strategies that hindered and facilitated effective implementation of new critical care practices and policies in response to the pandemic. Methods We used a cross-sectional, qualitative study design to conduct semi-structured in-depth interviews with critical care leaders across the United States. The interviews were audio-taped and professionally transcribed verbatim. Guided by the Consolidated Framework for Implementation Research (CFIR), three qualitative researchers used rapid analysis methods to develop relevant codes and identify salient themes. Results: Among the 17 hospitals that agreed to participate in this study, 31 clinical leaders were interviewed. The CFIR-driven rapid analysis of the interview transcripts generated 12 major themes, which included six implementation facilitators (i.e., factors that promoted the implementation of new critical care practices) and six implementation barriers (i.e., factors that hindered the implementation of new critical care practices). These themes spanned the five CFIR domains (Intervention Characteristics, Outer Setting, Inner Setting, Characteristics of Individuals, and Process) and 11 distinct CFIR constructs. Salient facilitators to implementation efforts included staff resilience, commitment, and innovation, which were supported through collaborative feedback and decision-making mechanisms between leadership and frontline staff. Major identified barriers included lack of access to reliable and transferable information, available resources, uncollaborative leadership and communication styles. Conclusions: Through applying the CFIR to organize and synthesize our qualitative data, this study revealed important insights into implementation determinants that influenced the uptake of new critical care practices during COVID-19. As the pandemic continues to burden critical care units, clinical leaders should consider emulating the effective change management strategies identified. The cultivation of streamlined, engaging, and collaborative leadership and communication mechanisms not only supported implementation of new care practices across sites, but it also helped reduce salient implementation barriers, particularly resource and staffing shortages. Future critical care implementation studies should seek to capitalize on identified facilitators and reduce barriers.

Abstract Background The COVID-19 pandemic produced unprecedented demands and rapidly changing evidence and practices within critical care settings. The purpose of this study was to identify factors and strategies that hindered and facilitated effective implementation of new critical care practices and policies in response to the pandemic. Methods We used a cross-sectional, qualitative study design to conduct semi-structured in-depth interviews with critical care leaders across the United States. The interviews were audio-taped and professionally transcribed verbatim. Guided by the Consolidated Framework for Implementation Research (CFIR), three qualitative researchers used rapid analysis methods to develop relevant codes and identify salient themes. Results Among the 17 hospitals that agreed to participate in this study, 31 clinical leaders were interviewed. The CFIR-driven rapid analysis of the interview transcripts generated 12 major themes, which included six implementation facilitators (i.e., factors that promoted the implementation of new critical care practices) and six implementation barriers (i.e., factors that hindered the implementation of new critical care practices). These themes spanned the five CFIR domains (Intervention Characteristics, Outer Setting, Inner Setting, Characteristics of Individuals, and Process) and 11 distinct CFIR constructs. Salient facilitators to implementation efforts included staff resilience, commitment, and innovation, which were supported through collaborative feedback and decision-making mechanisms between leadership and frontline staff. Major identified barriers included lack of access to reliable and transferable information, available resources, uncollaborative leadership and communication styles. Conclusions Through applying the CFIR to organize and synthesize our qualitative data, this study revealed important insights into implementation determinants that influenced the uptake of new critical care practices during COVID-19. As the pandemic continues to burden critical care units, clinical leaders should consider emulating the effective change management strategies identified. The cultivation of streamlined, engaging, and collaborative leadership and communication mechanisms not only supported implementation of new care practices across sites, but it also helped reduce salient implementation barriers, particularly resource and staffing shortages. Future critical care implementation studies should seek to capitalize on identified facilitators and reduce barriers.

Objectives: To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hospital admission. Design: Post hoc analysis of a single-center, prospective, before-and-after feasibility trial. Setting: Rural district hospital in Rwanda, a low-income country in Sub-Sahara Africa. Patients: Infants and children greater than 28 days and less than 18 years of life hospitalized because of an acute infection. Interventions: None. Measurements and Main Results: Age, vital signs (heart rate, respiratory rate, and temperature) capillary refill time, altered mental state collected at hospital admission, as well as survival status at hospital discharge were extracted from the trial database. This information was collected for 1,579 adult and pediatric patients admitted to a regional referral hospital with an acute infection in rural Rwanda. Nine-hundred forty-nine children were included in this analysis. We predicted survival in study subjects using random forests, a machine learning algorithm. Five prediction models, all including age plus two to five other variables, were tested. Three distinct optimization criteria of the algorithm were then compared. The in-hospital mortality was 1.5% (n = 14). All five models could predict in-hospital mortality with an area under the receiver operating characteristic curve ranging between 0.69 and 0.8. The model including age, respiratory rate, capillary refill time, altered mental state exhibited the highest predictive value area under the receiver operating characteristic curve 0.8 (95% CI, 0.78–0.8) with the lowest possible number of variables. Conclusions: A machine learning-based algorithm could reliably predict hospital mortality in a Sub-Sahara African population of 949 children with an acute infection using easily collected information at admission which includes age, respiratory rate, capillary refill time, and altered mental state. Future studies need to evaluate and strengthen this algorithm in larger pediatric populations, both in high- and low-/middle-income countries.

Objective: To evaluate whether a focused education program and implementation of a treatment bundle increases the rate of early evidence-based interventions in patients with acute infections. Design: Single-center, prospective, before-and-after feasibility trial. Setting: Emergency department of a sub-Saharan African district hospital. Patients: Patients > 28 days of life admitted to the study hospital for an acute infection. Interventions: The trial had three phases (each of four months). Interventions took place during the second (educational program followed by implementation of the treatment bundle) and third (provision of resources to implement treatment bundle) phases. Measurements and Main Results: Demographic, clinical, and laboratory data were collected at study enrollment; 24, 48, and 72 hours after hospital admission; and at discharge. A total of 1,594 patients were enrolled (pre-intervention, n = 661; intervention I, n = 531; intervention II, n = 402). The rate of early evidence-based interventions per patient during Intervention Phase I was greater than during the pre-intervention phase (74 ± 17 vs. 79 ± 15%, p < 0.001). No difference was detected when data were compared between Intervention Phases I and II (79 ± 15 vs. 80 ± 15%, p = 0.58). No differences in the incidence of blood transfusion (pre-intervention, 6%; intervention I, 7%; intervention II, 7%) or severe adverse events in the first 24 hours (allergic reactions: pre-intervention, 0.2%; intervention I, 0%; intervention II, 0%; respiratory failure: pre-intervention, 2%; intervention I, 2%; intervention II, 2%; acute renal failure: pre-intervention, 2%; intervention I, 2%; intervention II, 1%) were observed. Conclusions: Our results indicate that a focused education program and implementation of an infection treatment bundle in clinical practice increased the rate of early evidence-based interventions in patients with acute infections (mostly malaria) admitted to a sub-Saharan African district hospital. Provision of material resources did not further increase this rate. While no safety issues were detected, this could be related to the very low disease severity of the enrolled patient population (www.clinicaltrials.gov: NCT02697513).