Background: In response to a facility-wide COVID-19 outbreak, our tertiary acute care hospital implemented an evidence-based bundle of infection control practices including the use of audits and trained observers "dofficers" to provide real-time constructive feedback. Methods: We trained furloughed staff to perform the role of dofficer. They offered support and corrective feedback on proper PPE use and completed 21-point audits during a 4-week intervention period. Audits tracked appropriate signage, placement and availability of supplies (equipment), correct PPE use, enhanced environmental cleaning, along with cohorting and social distancing rates. Audit data was used to provide weekly quality improvement reports to units. Results: Nine hundred and sixty two separate audits recorded 36,948 observations, over 7,696 observerhours. The most common errors were with environmental cleaning and PPE use; the least common were with regards to equipment availability and cohorting and social distancing. Mean error rates decreased from 9.81% to 2.88% (P < .001). The largest reduction, 22.57%, occurred in the category of PPE doffing errors. Conclusions: Dofficer led audits effectively identified areas for improvement. Feedback through weekly reports and real-time correction of PPE errors by dofficers led to statistically significant improvements; however, error rates remained high. Further research is needed establish if these relationships are causal.
An analysis of individual and department triage variances to identify, quantify, and improve markers of nurse triage accuracy. Rebecca Cotton, Richard Drew, Matthew Douma, Domhnall O’Dochartaigh, Candice Keddie, Karen Muncaster, Christopher Picard Background: Canadian Emergency Departments (ED) use the five-point Canadian Triage Acuity Scale (CTAS) to sort and prioritize patients according to acuity. CTAS scores are used to make decisions on patient flow, staffing complement, and funding. Despite this, there is a paucity of literature describing how CTAS data can be audited, and how the data can inform quality improvement/assurance (QI/QA). Implementation: Triage data downloaded from Tableau were analyzed using Microsoft Excel and IBM SPSS 26. Staff were informed of the audit using email and social media, and invited to discuss the results with educators and administrators. Staff identified for intervention were approached individually with the administrative plan. Anonymized versions of the work plan were posted on the departmental audit board. Nurses triaging greater than 50% department average were offered the option to triage less frequently, while nurses triaging less than 50% the department average were preferentially placed in triage. Nurses triaging fewer than 100 patients per year were informed they would be relieved of triage responsibility unless their rates increased above threshold. Nurses “down-triaging” patients at rates greater than 2 SD were informed that if their practice remained outside 2 SD at repeat audit they would be relieved of triage responsibility until they voluntarily completed CTAS refresher training. Nurses with average assigned CTAS scores > 2 SD department average had 20 visits randomly audited per month for error/appropriateness. Evaluation Method: Computer-assisted analysis of complete triage records was conducted for August 2019 to August 2020 at the Misericordia Hospital Emergency. Complete triage entries of every patient triaged by all triage trained nurses in the department were examined. Nurse’s with practice variation two deviations from department mean were identified and received additional detailed audits. Items examined for error were: FTE adjusted triage frequency; average CTAS score assigned; triage score manual override “down/up-triage” rate; proportion of absent Numeric Pain Scores (NPS) for patients with primary presenting complaints of pain; and vital signs modifier error rates. Initial department averages were used for benchmarking individual nurses; zone averages were used to benchmark department performance. Nurses were interviewed, audit results and action plans were posted. Repeat audits were performed on a three-month basis and benchmarked to initial measures, and a staff awareness campaign was enacted to improve NPS scoring. Data were extracted using text-parsing algorithms programmed into Microsoft Excel and analyzed using IBM SPSS 26. Data were normally distributed and descriptive statistics were calculated using means and standard deviations. T-testing was used for comparisons, and all testing was two-tailed with a pre-defined significance set at 0.05. Results: After the 3rd quarterly audit and associated interventions, global improvements were appreciated in triage nurse practice. There was a 68% reduction in the need for administrative action (n=51, n=18) with reduced variance in individual nurse triage rates and a 50% reduction in nurses who triaged >50% more patients than their peers. 50% fewer nurses had a mean triage rate >.02 above or below department average, there was an 86% reduction in high risk vital sign error rates, a 78% reduction in ”down-triage” rates, and a 6.5% improvement in documentation of numerical pain scores. Advice and Lessons Learned:1) Triage data analytics can rapidly identify staff with significant deviations from the average,making auditing and QI/QA activities more efficient and effective. 2) Having a concrete performance management framework and dissemination plan in place areessential for auditing to have a significant impact on triage consistency and quality over time. 3) Future QI/QA work should consider expanding computer-assisted text parsing to identifypatients at risk for mis-triage for reasons other than vital sign derangement, which will allowfor broader ED rollout across the Edmonton Zone and beyond.
Background: Medical cardiac arrest care in Edmonton Zone Emergency Departments does not undergo structured quality monitoring or continuous improvement. Prior to this work, quality indicators had not been selected, nor had tracking or reporting activities been undertaken. This work brings the Edmonton Zone EDs to the forefront of the continuous quality improvement recommendations made by Heart and Stroke Canada and the American Heart Association that are believed to improve both patient outcomes and overall system performance. For this project quality indicator development and implementation takes three perspectives: patients and families, frontline staff and the health care system. This work is informed by the Institute for Healthcare Improvement, the National Institute of Science and the International Liaison Committee on Resuscitation’s work on Systems of Care and Continuous Quality Improvement for Emergency Cardiovascular Care. This work is motivated by the desire to improve patient/family experience and outcome, provider experience while improving system performance. Implementation: An iterative process identified the lowest resource/highest impact areas for improvement. This process was informed through a Delphi survey conducted by the Alberta Cardiac Arrest Stakeholders group and stakeholder engagement. Four areas for improvement were identified: support of patients and families, support of staff, improvement in care metrics, and system level interventions. Support of patients and families was accomplished through the development of an advisory network, by linking families with existing supports, and through the implementation of a bereavement package. Supporting staff was accomplished through the development of a formal and informal debriefing processes. Improving clinical care was accomplished through the integration of chest compression feedback devices into clinical care. Improvements at the system level will be accomplished through the creation of a cardiac arrest registry. Evaluation Methods: Mixed methods approaches are used to evaluate this project. Post cardiac arrest quality track forms are being filled out. Chest compression feedback device data was obtained through simulated patient-care scenarios, staff experiences were obtained through a structured survey. Clinically chest compression data was collected from the feedback devices by Clinical educators, through tracking forms, and pre-and-post surveys of frontline staff measuring burnout and occupational stress are underway. Data is being collected in a local registry to generate accurate incidence and survival rates. Eventual post-implementation interviews with providers, survivors and families will be conducted. Results: A patient/family advisor network has been established. Survivor and families can be connected with the Bystander Support Network and the Heart and Stroke Foundation portal through the bereavement packages being offered at one of the QI sites. Two sites have developed staff debriefing processes: an interdisciplinary Critical Incident Stress Management (CISM) team at one site, and referral to an existing CISM team at two other sites. Chest compression feedback is being used at two sites, staff feedback has been positive. One site is tracking resuscitation metrics which are being used to guide and evaluate the interventions: continued improvement in chest compression quality has been noted. Data analytics are being used at all sites to identify additional opportunities to improve resuscitation care and efforts are underway to expand data collection to other sites and to unify pre-hospital and in-hospital cardiac arrest data. Advice and Lessons Learned: Pre-intervention data would have allowed for more meaningful comparisons in patient care. Efforts should be put into identifying what these measures could be. High levels of staff engagement at one site appear to have influenced the uptake of chest compression feedback. Effort should identify key stakeholders and gain buy in to increase uptake There are significant barriers to unifying pre-hospital and in-hospital cardiac arrest data. It is our belief that a continuous record offers some greatest opportunity to collect data on resuscitation care. Efforts should focus on building a linkage between these data sources and creating a shared data set.
The clinical effects of CPR meter on chest compression quality: a QI project. Christopher Picard, Richard Drew, Domhnall O’Dochartaigh, Matthew J Douma, Candice Keddie, Colleen Norris. Background: High-quality chest compressions are the cornerstone of resuscitation. Training guidelines require CPR feedback, and pre-clinical data shows that feedback devices improve chest compression quality; but devices are not being used in many emergency departments, and their impact on clinical care is less well understood. Some services use defibrillator generated reports for quality improvement, but these measurements may be limited in scope and have not been rigorously compared to other tools. Methods: Laerdal CPRMeter 2 chest compression feedback devices were purchased using funds made available by a zone QI initiative. Initial training for implementation consisted of staff performing one minute of blinded chest compression using the feedback device, followed by one minute of chest compression unblinded. Staff were shown the raw percentage of chest compressions meeting target depth, release, and rate under both conditions as well as overall improvement. Following initial orientation, devices were incorporated into clinical care and all subsequent staff simulation and training. Clinically, use of the feedback device and completion or QI tracking forms was not mandated but was encouraged by drawing code participant names from completed forms for a free ACLS or PALS course. Data from all codes were automatically collected by the LifePak 20, data from any resuscitation using the Laerdal CPRmeter 2 were also automatically recorded when the device was used: these data were downloaded weekly. Completed questionnaire forms were submitted to the Clinical Educators and extracted as received. Evaluation Methods: Chest compression quality data was collected in two ways: first, using a Laerdal CPRMeter2, second, by downloading and analyzing cardiac arrest data from a LifePak20 defibrillator using CodeStatTM software. Device data were matched and synthesized by an emergency department CNE using Microsoft excel and IBM SPSS 26. Descriptive statistics (mean and standard deviations) are used to describe the data. Differences in chest compression quality and duration of resuscitations between resuscitation that did or did not use a feedback device or a backboard were compared using independent t-testing. Differences in chest compressions at the target depth, release, and rate between the numbers of staff involved were assessed using ANOVA. Agreement between devices (CPRMeter2 and LifePak) used during the resuscitations were evaluated using paired t-testing, Pearson correlations, and Bland-Altman plots. All tests were two-tailed with predetermined significance levels set at a=0.05. Results: Data collection occurred between August 2019 and December 2020. There were a total of 50 cardiac arrests included, 36 had questionnaire data returned, 36 had data collected from the CPR meter 2, 24 had data collected from the LifePak, and 10 had data collected using all three methods. The average duration of resuscitation (number of chest compressions) was 1079.56 (SD=858.25); there was no difference in the duration of resuscitation (number of chest compressions) between resuscitations using versus not using CPR feedback devices (p=0.673). Resuscitations utilizing chest compression feedback had a higher percentage of chest compressions at the target rate compared to resuscitations not using feedback (74.08% vs 42.18%, p=0.007). Resuscitations that utilized a backboard had a higher percentage of chest compressions at target depth (72.92% vs 48.73%, p=0.048). There were no differences noted in the duration of resuscitation attempt (p=0.167) or percentages of chest compressions at the target depth (p=0.181), release (p=0.538), or rate (p=0.656) between resuscitations with different sized teams (4-5, 6-7, 8-9, >10 staff involved). There was a strong positive correlation (r=0.771, p=0.005, n=11) between the two measurement methods and chest compression rates, and no statistically significant difference in measured scores (p=0.999), with 100% of values falling within the Bland-Altman confidence intervals of 36.72 and -36.72, n=11. Interpretation of the levels of agreement between these two device measures methods should be done cautiously however, given the small sample size and wide confidence intervals. Implications 1) Incorporation of visual chest compression feedback and use of a backboard are fast andaffordable and significantly improved the percentage of chest compression at the target rateand depth. 2) There was no correlation between the size of the resuscitation team and the percentage ofchest compressions at the target depth, release or rate; nor was the feedback device useassociated with the duration of the resuscitation attempt. 3) The implications of improvement with the CPR meter suggests that areas or service not usingfeedback should consider implementing its use to achieve the target compression rate. 4) Compared to LifePak feedback alone the CPRMeter2 will also allow services to target depthand release targets as well as rate.
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