Air Medical Quality Research Articles

Improving Mobilization Times of a Specialized Neonatal and Pediatric Critical Care Transport Team

ObjectivesThe Ground and Air Medical Quality Transport database identifies the average mobilization time (AMT) of the transport team as a metric for benchmarking. Our specific aim was to decrease our AMT to < 25 minutes for our expanded role, ground, nonsimultaneous transports by the end of quarter 4 of 2018. MethodsStandardization of data collection with awareness building, ambulance vendor involvement, and team-focused interventions were the different phases of project implementation. Documentation of reasons for delay was performed pre- and postimplementation. ResultsOur AMT decreased from a baseline of 30.3 minutes to 24.5 minutes after project implementation. Communication delays (19.0%), reason for delay not documented (16.5%), no team available (14.0%), rotor wing vendor delays (12.4%), and ambulance vendor delays (7.4%) were the common reasons for delay on our preimplementation Pareto analysis. Communication and pharmacy delays were the most common reasons during all 3 phases of our project. ConclusionsImplementation of the project allowed us to achieve our goal of improving our AMT. Understanding the reasons for delay is a crucial consideration, and success depends on careful change management. Further iterations will need to focus on improving communication and the pharmacy medication dispatch process.

Cardiopulmonary Resuscitation in Interfacility Transport: An International Report Using the Ground Air Medical Quality in Transport (GAMUT) Database.

Background With the regionalization of specialty care, there is an increasing need for interfacility transport from local to regional hospitals. There are very limited data on rates of cardiopulmonary resuscitation (CPR) during medical transport and relationship between transport-specific factors, such as transport program type and need of CPR during transport of critically ill patients. We present the first, multicenter, international report of CPR during medical transport using the large Ground and Air Medical qUality Transport (GAMUT) database. Methods We retrospectively reviewed the GAMUT database from January 2014 to March 2017 for CPR during transport. We determined the overall CPR rate and CPR rates for adult, pediatric, and neonatal transport programs. The rate of CPR per total transports was expressed as percentage, and then, Spearman's rho nonparametric associations were determined between CPR and other quality metrics tracked in the GAMUT database. Examples include advanced airway presence, waveform capnography usage, average mobilization time from the start of referral until en route, 1st attempt intubation success rate, and DASH1A intubation success (definitive airway sans hypoxia/hypotension on 1st attempt). Data were analyzed using chi-square tests, and in the presence of overall significance, post hoc Bonferroni adjusted z tests were performed. Results There were 72 programs that had at least one CPR event during the study period. The overall CPR rate was 0.42% (777 CPR episodes/184,272 patient contacts) from 115 programs reporting transport volume and CPR events from the GAMUT database during the study period. Adult, pediatric, and neonatal transport programs (n = 57, 40 and 16, respectively) had significantly different CPR rates (P < 0.001) i.e., 0.68% (555/82,094), 0.18% (138/76,430), and 0.33% (73/21,823), respectively. Presence of an advanced airway and mobilization time was significantly associated with CPR episodes (P < 0.001) (Rs = +0.41 and Rs = −0.60, respectively). Other transport quality metrics such as waveform capnography, first attempt intubation, and DASH1A success rate were not significantly associated with CPR episodes. Conclusion The overall CPR rate during medical transport is 0.42%. Adult, pediatric, and neonatal program types have significantly different overall rates of CPR. Presence of advanced airway and mobilization time had an association with the rate of CPR during transport.

Using Epidemiology and Pediatric Direction to Inform Air Medical Quality Improvement

IntroductionPediatric air transport research is limited, especially scene transport. Study purpose was to review transport epidemiology, outcomes, and documentation to inform development of a pediatric flight quality improvement (QI) program and outreach. MethodsStudy design was ongoing review and analysis of flight, ED, EMS and hospital records over 2 years from children ≤ 18 years transported by a regional flight program. Mission type included trauma, medical, scene and interfacility. Records were reviewed monthly by a pediatric medical director (PMD) with ongoing QI and educational initiatives. Peer review was added in year two. Demographic and outcome variables included weight, times, procedures, pain scales, Glasgow Coma Scale (GCS), medications, disposition, etc. Two QI focus areas were studied using QI Macros®: weight and pain documentation. ResultsChildren accounted for 8% of total flights (165/2076). Transport was 58% scene; 42% interfacility. Median dispatch to arrival time was 21 minutes. Saturday accounted for 24% of flights. Mean scene GCS was 12; 39 (24%) patients were intubated. Scene weight in kilograms improved 18% and pain documentation improved from 49% to 79% during the study. ConclusionAddition of PMD, peer and outcome review processes provided opportunities for improving pediatric transport QI initiatives and targeted outreach education.

The Rising Tide Lifts All Boats: Using the Ground and Air Medical qUality in Transport (GAMUT) Database as a Tool for Quality Improvement

Introduction The Ground and Air Medical qUality in Transport (GAMUT) Quality Improvement Collaborative uses a quality database to track critical care transport (CCT) performance for a discrete set of quality metrics on a monthly basis. Benchmark performance feedback reports are available for programs to allow comparison. We sought to describe the impact of GAMUT participation as determined by quality metric measurement over time. Methods To provide an estimate of the impact of GAMUT reporting feedback over the entire reporting period (Jan 2014 - Dec 2017), data from each institution that provided complete data for the study period were aggregated. Evaluations focused on 4 exemplar quality metrics: blood glucose measurement in patients with altered mental status, first attempt intubation success, confirmation of tracheal tube placement, and mechanical ventilation utilization. The slopes of the yearly metric values were determined by linear regression modeling to estimate the annual rate of improvement. To better isolate the effects of GAMUT feedback from the learning associated with evolving processes, a before-and-after approach was employed. The initial metric values were determined prior to GAMUT reporting feedback and then again one year later after GAMUT feedback. The one year period with +/- 3 month window was chosen to minimize any potential seasonality in the data. A two factor ANOVA model was then employed with fixed effects for year, to take into account natural process evolution, and an indicator of whether the data was before or after GAMUT feedback. Estimated marginal means with 95% confidence intervals were displayed for each metric for the before and after GAMUT feedback study groups. Results The annual growth rates as determined by the cohort (N=23 institutions) reporting over the entire GAMUT reporting period for blood glucose measurement increased by 1.8%; first attempt intubation rates increased by 4.8% annually; tracheal tube place checks increased by 0.1% annually; mechanical ventilation of advanced airways increased by 2.5% annually. The 1-year impact of GAMUT measurement and reporting feedback is reported in Figure 1, demonstrating improvement in each of the 4 exemplar metrics (p Conclusion GAMUT quality metric reporting and benchmarked feedback is associated with improvement over the entire reporting period for a variety of CCT quality improvement metrics. GAMUT QI collaborative is an effective tool for supporting quality improvement.

Risk-adjusted CPR Rates as a Measure of Quality During Interfacility Transports

Introduction Over the past decade, the health care industry has witnessed an unparalleled reporting of hospital-specific comparative outcomes. Clearly, there has been a growing consensus among a broad array of agencies and institutions around the importance of reporting quality measures, including those that measure clinical outcomes. Previous research suggests that there is substantial variability in care between hospitals, particularly in trauma centres. In 2006, the American College of Surgeons (ACS) Committee on Trauma launched the Trauma Quality Improvement Program (TQIP) to study the variability in outcomes between trauma centers to improve the quality of trauma care in North America. The primary goal of TQIP is to improve the quality of trauma care through outcomes-based, risk-adjusted benchmarking of trauma centers and feedback reports. However, differences in patient selection (selection bias), case mix, data quality, geography, and other factors inherent to different injured populations likely contribute in part to observed differences in outcome. Risk-adjustment is a methodology used to ensure that “like” patients are being compared, so that differences in outcome can be attributed solely to differences in the processes and quality of care provided. Transport services are beginning to use performance metrics to analyze for variations between ambulance and transport services in order to improve quality of care during transport. The “Ground Air Medical qUality Transport Quality Improvement” (GAMUT) Collaborative collects transport data in a large database to use as a free resource for transport teams to track, report, and analyze their performance on transport-specific quality metrics by comparing it to other program's. Because of the nature of transport, most of the metrics are process metrics or intermediate outcomes. Arguably, the only transport-related definitive outcome that GAMUT reports is rate of cardiac arrest during transport (Rate of CPR performed during transport). However, this rate is reported as an unadjusted rate: there is no risk-adjustment. We propose a simple methodology to allow for risk-adjustment of CPR rate during transport. Methods Ornge is the sole provider of Air Ambulance and Critical Care Transport Services to the province of Ontario, Canada. The province has a land mass of over 1 million square kilometers, and has a population of almost 14 million people. Ornge operates a fleet of Pilatus Next Generation PC-12 airplanes, Leonardo AW-139 helicopters, and Crestline Commander land ambulances, staffed with Critical Care and Advanced Care Paramedics. For low acuity patients requiring a lower level of care, Ornge uses a Standing Agreement to Chart Air Ambulance services from contract carriers, staffed by primary care paramedics. For interfacility transfer patients, we propose to compare CPR rates year over year for Ornge (2013-2017). SA transports will not be included, as they represent a very low-risk group. Risk adjustment will be done by adjusting for mechanical ventilation (yes/no), use of infusion(s) of vasopressors (yes/no), mechanical ventilation and pressors (yes/no), or none. Rates will be standardized to the distribution of cases during the reference year of 2013-14, and reported per thousand hours. Results Discussion Comparing variability in outcomes is helpful in improving quality of care. High performing centres can share their best practices to improve quality of care throughout the system. Risk adjustment is a methodology used to ensure that “apples” are being compared to “apples”, and that case selection is not affecting the analysis. Transport agencies are now reporting CPR rates during transports as an outcome measure, as part of their quality improvement programs. These rates are unadjusted, and are difficult to compare across groups. Air ambulances transport many different types of patients, such that standard risk adjustment methods may not be directly applicable. Risk adjustment for trauma patients is completely different than for post STEMI patients. As well, transport agencies only care for patients during a very short period of time. We presented a methodology of risk-adjustment for CPR rates during interfacility transports that have correlational validity with our changes in our quality processes over time.

Plug and Play: Mechanical Ventilation Utilization During Critical Care Transport of Patients with Advanced Airway

Introduction Healthcare has undergone significant regionalization over the past 20 years, necessitating high quality interhospital transport. Multiple studies have shown that the use of transport mechanical ventilators (MV) in lieu of manual ventilation during such transports provides more consistent ventilation and thus improved clinical outcomes. By analyzing MV use during interhospital transport of patients with advanced airways among diverse neonatal/pediatric/adult transport teams, we sought to describe current practice, identify quality benchmarks, and describe associated performance in relation to other quality indicators. Methods Data are contained in the Ground and Air Medical qUality Transport (GAMUT) database. Use of MV during transport is defined and calculated as follows: NUMERATOR = Number of transport patient contacts during the calendar month involving patient with an advanced airway supported by a MV. DENOMINATOR = Number of transport patient contacts during calendar month involving a patient with an advanced airway. GAMUT data are reported in aggregate monthly using the REDCap database and quality metrics data from December 2016 - December 2017 (12 months) were analyzed. Analyses included stratifying institutions by patient population, evaluation of mean and achievable benchmarks, and rank correlation to identify associations with other GAMUT quality metrics. Results 172 programs supplied GAMUT data on transports during the study period representing 217,434 patient contacts. Of those, 39,185 (18.0%) were transported with an advanced airway and 36,487 (93.1%) of advanced airway patients were supported by a MV. During the same study period, the achievable benchmark of care for MV utilization was 99.6%. Patients transported by pediatric programs were less likely have an advanced airway (12.1%) but also used MV significantly less (88.9%, p Conclusion Amongst GAMUT quality improvement collaborative participants, pediatric programs transported patients with advanced airways less commonly and used MV in those advanced airway patients less frequently. Neonatal programs used MV most frequently. This anlysis provides clarity regarding the use of MV in transport and permits targeted interventions for improved performance and ultimately best practice in this important quality measure.

(19) Cardiopulmonary Resuscitation During Ground and Air Medical Transport: The First, Multi-center, International Report Using A Large Database on Ground and Air Medical qUality Transport (GAMUT)

(19) Cardiopulmonary Resuscitation During Ground and Air Medical Transport: The First, Multi-center, International Report Using A Large Database on Ground and Air Medical qUality Transport (GAMUT)

Setting the Benchmark for the Ground and Air Medical Quality in Transport International Quality Improvement Collaborative

ObjectiveCritical care transport (CCT) supports regionalization of medical care. Focus on the quality of CCT care prompted the development of the Ground and Air Medical qUality in Transport (GAMUT) Quality Improvement collaborative database which tracks consensus quality metrics. The Institute of Medicine recommends benchmarking of comparative data to accelerate improvement. Herein, we report the strategies and rationale for GAMUT QI Collaborative benchmarking. MethodsThe GAMUT database includes >350 programs internationally with >200,000 annual patient contacts. Evidence-based literature review performed in May 2016 and October 2017 identified benchmarking strategies were evaluated and summarized, specific to the GAMUT metrics. Statistical analyses include simple statistics and weighted expectation calculations for benchmark examples (Pearson chi-square with Bonferroni adjusted post-hoc z tests). ResultsEvidence-based literature search yielded 70 articles, and 31 were selected for inclusion in our evidence table. 5 evidence-based benchmark strategies were considered: average (mean), average (median), adjusted benchmark (based on expected outcome), Achievable Benchmark of Care (ABC), and Delphi. ABC threshold establishes a higher target (90th percentile) forcing more programs to achieve higher performance. ConclusionBenchmarking is not well-suited for a single strategy and requires customized consideration based on each metric, though adjusted benchmark and ABC generally set higher performance benchmarks.

Intubation Success in Critical Care Transport: A Multicenter Study

Introduction: Tracheal intubation (TI) is a lifesaving critical care skill. Failed TI attempts, however, can harm patients. Critical care transport (CCT) teams function as the first point of critical care contact for patients being transported to tertiary medical centers for specialized surgical, medical, and trauma care. The Ground and Air Medical qUality in Transport (GAMUT) Quality Improvement Collaborative uses a quality metric database to track CCT quality metric performance, including TI. We sought to describe TI among GAMUT participants with the hypothesis that CCT would perform better than other prehospital TI reports and similarly to hospital TI success. Methods: The GAMUT Database is a global, voluntary database for tracking consensus quality metric performance among CCT programs performing neonatal, pediatric, and adult transports. The TI-specific quality metrics are “first attempt TI success” and “definitive airway sans hypoxia/hypotension on first attempt (DASH-1A).” The 2015 GAMUT Database was queried and analysis included patient age, program type, and intubation success rate. Analysis included simple statistics and Pearson chi-square with Bonferroni-adjusted post hoc z tests (significance = p < 0.05 via two-sided testing). Results: Overall, 85,704 patient contacts (neonatal n [%] = 12,664 [14.8%], pediatric n [%] = 28,992 [33.8%], adult n [%] = 44,048 [51.4%]) were included, with 4,036 (4.7%) TI attempts. First attempt TI success was lowest in neonates (59.3%, 617 attempts), better in pediatrics (81.7%, 519 attempts), and best in adults (87%, 2900 attempts), p < 0.001. Adult-focused CCT teams had higher overall first attempt TI success versus pediatric- and neonatal-focused teams (86.9% vs. 63.5%, p < 0.001) and also in pediatric first attempt TI success (86.5% vs. 75.3%, p < 0.001). DASH-1A rates were lower across all patient types (neonatal = 51.9%, pediatric = 74.3%, adult = 79.8%). Conclusions: CCT TI is not uncommon, and rates of TI and DASH-1A success are higher in adult patients and adult-focused CCT teams. TI success rates are higher in CCT than other prehospital settings, but lower than in-hospital success TI rates. Identifying factors influencing TI success among high performers should influence best practice strategies for TI.

Abstract 7: Mobilization Time Among Neonatal/Pediatric Transport Teams: The Ground and Air Medical Quality in Transport (GAMUT) Collaborative

Abstract 7: Mobilization Time Among Neonatal/Pediatric Transport Teams: The Ground and Air Medical Quality in Transport (GAMUT) Collaborative

Analyzing Communication Errors in an Air Medical Transport Service

IntroductionPoor communication can result in adverse events. Presently, no standards exist for classifying and analyzing air medical communication errors. This study sought to determine the frequency and types of communication errors reported within an air medical quality and safety assurance reporting system. MethodsOf 825 quality assurance reports submitted in 2009, 278 were randomly selected and analyzed for communication errors. Each communication error was classified and mapped to Clark's communication level hierarchy (ie, levels 1–4). Descriptive statistics were performed, and comparisons were evaluated using chi-square analysis. ResultsSixty-four communication errors were identified in 58 reports (21% of 278). Of the 64 identified communication errors, only 18 (28%) were classified by the staff to be communication errors. Communication errors occurred most often at level 1 (n = 42/64, 66%) followed by level 4 (21/64, 33%). Level 2 and 3 communication failures were rare (, 1%). ConclusionCommunication errors were found in a fifth of quality and safety assurance reports. The reporting staff identified less than a third of these errors. Nearly all communication errors (99%) occurred at either the lowest level of communication (level 1, 66%) or the highest level (level 4, 33%). An air medical communication ontology is necessary to improve the recognition and analysis of communication errors.

Pediatric, neonatal, and maternal patient care addendum to the AAMS/NFNA resource document for air medical quality assurance programs

Pediatric, neonatal, and maternal patient care addendum to the AAMS/NFNA resource document for air medical quality assurance programs

AAMS resource document for air medical quality assurance programs

AAMS resource document for air medical quality assurance programs

The status of air medical quality assurance

In February of 1989, the AAMS Quality Assurance (QA) Committee surveyed 177 flight programs regarding their QA activities. There was a 58% return rate, with the majority of the respondents being single hospital-based programs. Eighty-nine percent of those surveyed stated that they wanted assistance with their QA programs. The majority of the respondents (95%) review patient care issues, with most performing regular flight record review (88%) and monitoring of the timeliness of care delivered (85%), such as scene/response times. The information generated by the survey has been used by the AAMS QA Committee in planning QA seminars, and may also be helpful to individual air medical programs in designing their QA programs, as well as a tool for use in comparing themselves to other programs. Specific areas needing improvement are mentioned and AAMS is challenged to offer leadership and support in these efforts.