Lessons Learned From a Systematic, Hospital-Wide Implementation of the ABCDEF Bundle: A Survey Evaluation.

Objective: We recently reported the first part of a study testing the impact of data literacy training on "assessing pain, spontaneous awakening and breathing trials, choice of analgesia and sedation, delirium monitoring/management, early exercise/mobility, and family and patient empowerment" [ABCDEF [A-F]) compliance. The purpose of the current study, part 2, was to evaluate the effectiveness of the implementation approach by surveying clinical staff to examine staff knowledge, skill, motivation, and organizational resources. DESIGN: The Clark and Estes Gap Analysis framework was used to study knowledge, motivation, and organization (KMO) influences. Assumed influences identified in the literature were used to design the A-F bundle implementation strategies. The influences were validated against a survey distributed to the ICU interprofessional team. SETTING: Single-center study was conducted in eight adult ICUs in a quaternary academic medical center. SUBJECTS: Interprofessional ICU clinical team. INTERVENTIONS: A quantitative survey was sent to 386 participants to evaluate the implementation design postimplementation. An exploratory factor analysis was performed to understand the relationship between the KMO influences and the questions posed to validate the influence. Descriptive statistics were used to identify strengths needed to sustain performance and weaknesses that required improvement to increase A-F bundle adherence. MEASUREMENT AND RESULTS: The survey received an 83% response rate. The exploratory factor analysis confirmed that 38 of 42 questions had a strong relationship to the KMO influences, validating the survey's utility in evaluating the effectiveness of implementation design. A total of 12 KMO influences were identified, 8 were categorized as a strength and 4 as a weakness of the implementation. CONCLUSIONS: Our study used an evidence-based gap analysis framework to demonstrate key implementation approaches needed to increase A-F bundle compliance. The following drivers were recommended as essential methods required for successful protocol implementation: data literacy training and performance monitoring, organizational support, value proposition, multidisciplinary collaboration, and interprofessional teamwork activities. We believe the learning generated in this two-part study is applicable to implementation design beyond the A-F bundle.

Improving ABCDEF Bundle Compliance and Clinical Outcomes in the ICU: Randomized Control Trial to Assess the Impact of Performance Measurement, Feedback, and Data Literacy Training.

OBJECTIVES: Describe the effects of data literacy training and continuous performance reports on ICU staff compliance with the 6-element ICU quality bundle approach known as the ABCDEF (A-F) bundle and patient outcomes. DESIGN: Stepped-wedge cluster randomized trial conducted during an institutional A-F bundle implementation program. SETTING: Single-center study conducted in eight adult ICUs. PATIENTS: Adult patients admitted for at least 24 hours, not undergoing active withdrawal of life support or palliative care. INTERVENTIONS: Four ICUs in the intervention group received bundle-related staff education, data literacy training, and weekly bundle performance reports during the 12-month study period. The four ICUs in the nonintervention group received none of these interventions. Bundle compliance and patient outcomes were tracked, including ICU and hospital mortality, transfer and discharge, discharge disposition, mechanical ventilation, and ICU delirium. MEASUREMENT AND RESULTS: In the intervention group, staff education alone increased bundle compliance from 9% to 16% (p < 0.0001); data literacy training further increased compliance from 16% to 21% (p = 0.03). This improvement was sustained throughout the study period including the onset of the COVID-19 pandemic and was greater than improvement in the nonintervention group (p < 0.001). Full A-F bundle compliance was associated with a lower likelihood of next-day ICU and hospital mortality, discharge to a facility other than home, and was associated with a higher likelihood of next-day extubation in patients. Next-day ICU and hospital discharge likelihood decreased, and delirium frequency was not affected. CONCLUSIONS: This is the first study demonstrating that the combination of staff education, data literacy training, and access to performance data improves A-F bundle compliance, sustains performance, and improves ICU patient outcomes (ICU and hospital mortality, mechanical ventilation duration, and home discharge rates). In contrast to previous studies, increased bundle compliance did not hasten ICU or hospital discharges or reduce delirium frequency in patients.

ICU Resource Limitations During Peak Seasonal Influenza: Results of a 2018 National Feasibility Study.

OBJECTIVES: Demonstrate the feasibility of weekly data collection and analysis of public health emergency (PHE) data. Assess fluctuations in, and challenges of, resource matching and potential effect on patient care for influenza in ICUs. DESIGN: Multicenter prospective noninterventional study testing effectiveness of leveraging the Discovery Critical Care Research Network Program for Resilience and Emergency Preparedness (Discovery-PREP) in performing PHE research. A 20-question internet survey was developed to prospectively assess ICU influenza-related resource stress. An informatics tool was designed to track responses; data were analyzed within 24 hours of weekly survey completion by the team biostatistician for timely reporting. PARTICIPANTS: Critical care and Emergency Medicine Discovery-PREP network investigators self-selected to participate in the voluntary query. SETTING: ICUs of 13 hospitals throughout the United States, 12 academic, and one community. INTERVENTIONS: ICU physicians were electronically surveyed weekly over 17 weeks during the influenza season (January 2018-April 2018). Responses were collected for 48 hours after each email query. MEASUREMENTS AND MAIN RESULTS: The average weekly response among the sites was 79% (range, 65-100%). Significant stress, defined as alterations in ICU staffing and/or resource allocation, occurred in up to 41% of sites during the national peak of influenza activity. These alterations included changes in staffing, not accepting external patient transfers, and canceling elective surgery. During this same period, up to 17% of the sites indicated that these changes might not have been sufficient to prevent potentially avoidable patient harm. CONCLUSIONS: This novel approach to querying ICU operational stress indicated that almost half of participating sites experienced critical care resource limitations during peak influenza season and required process and/or staffing changes to better balance resources with patient care demands. This weekly national reporting infrastructure could be adapted and expanded to better inform providers, hospital emergency management teams, and government leaders during PHEs.

Safety evaluation of a Medical Device Data System.

Our hospital became interested in the extraction of electronic data from our bedside monitor network to enrich clinical care, and enable various quality improvement projects, research projects, and future applications involving advanced decision-support. We conducted a range of tests to confirm the safety of deploying BedMaster (Excel Medical Electronics, Jupiter FL, USA), which is third-party software sold expressly to provide electronic data extraction and storage from networked General Electric Healthcare bedside patient monitors. We conducted a series of tests examining the changes in network performance when the BedMaster system was on our isolated patient monitor network. We found that use of BedMaster led to measurable, but trivial increases in network traffic and latency. We did not identify any failure scenarios in our analysis and testing. The major value of this report is to highlight potential challenges inherent in data and electronic device integration within the healthcare setting. In describing our strategy for testing the BedMaster system, it is our intention to present one testing protocol and to generate thought and discussion in the broader community about what types of problems can arise with inter-operability, and what types of testing are necessary to mitigate against these risks. Standards for inter-operability would surely reduce the inherent risks.