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Can interprofessional teamwork reduce patient throughput times? A longitudinal single-centre study of three different triage processes at a Swedish emergency department.

ABSTRACT: OBJECTIVE:To determine the impact on emergency department (ED) throughput times and proportion of patients who leave without being seen by a physician (LWBS) of two triage interventions, where comprehensive nurse-led triage was first replaced by senior physician-led triage and then by interprofessional teamwork. DESIGN:Single-centre before-and-after study. SETTING:Adult ED of a Swedish urban hospital. PARTICIPANTS:Patients arriving on weekdays 08:00 to 21:00 during three 1-year periods in the interval May 2012 to November 2015. A total of 185 806 arrivals were included. INTERVENTIONS:Senior physicians replaced triage nurses May 2013 to May 2014. Interprofessional teamwork replaced the triage process on weekdays 08:00 to 21:00 November 2014 to November 2015. MAIN OUTCOME MEASURES:Primary outcomes were the median time to physician (TTP) and the median length of stay (LOS). Secondary outcome was the LWBS rate. RESULTS:The crude median LOS was shortest for teamwork, 228?min (95%?CI 226.4 to 230.5) compared with 232?min (95%?CI 230.8 to 233.9) for nurse-led and 250?min (95%?CI 248.5 to 252.6) for physician-led triage. The adjusted LOS for the teamwork period was 16?min shorter than for nurse-led triage and 23?min shorter than for physician-led triage. The median TTP was shortest for physician-led triage, 56?min (95%?CI 54.5 to 56.6) compared with 116?min (95%?CI 114.4 to 117.5) for nurse-led triage and 74?min (95%?CI 72.7 to 74.8) for teamwork. The LWBS rate was 1.9% for nurse-led triage, 1.2% for physician-led triage and 3.2% for teamwork. All outcome measure differences had two-tailed p values<0.01. CONCLUSIONS:Interprofessional teamwork had the shortest length of stay, a shorter time to physician than nurse-led triage, but a higher LWBS rate. Interprofessional teamwork may be a useful approach to reducing ED throughput times.

SUBMITTER: Liu J

PROVIDER: S-EPMC5914774 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Can interprofessional teamwork reduce patient throughput times? A longitudinal single-centre study of three different triage processes at a Swedish emergency department.

Liu Jenny J Masiello Italo I Ponzer Sari S Farrokhnia Nasim N

BMJ open 20180419 4

<h4>Objective</h4>To determine the impact on emergency department (ED) throughput times and proportion of patients who leave without being seen by a physician (LWBS) of two triage interventions, where comprehensive nurse-led triage was first replaced by senior physician-led triage and then by interprofessional teamwork.<h4>Design</h4>Single-centre before-and-after study.<h4>Setting</h4>Adult ED of a Swedish urban hospital.<h4>Participants</h4>Patients arriving on weekdays 08:00 to 21:00 during t ...[more]

PMID: 29674366

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Project description:Background Interprofessional teams contribute to patient safety during clinical care. However, little is known about how interprofessional teams manage and cope with critical incidents in the emergency department (ED). Therefore, the study aimed to describe healthcare professionals (HCPs) perceptions of critical incidents linked to the enablers of and barriers to interprofessional teamwork in a high-risk setting, the ED. Methods Individual interviews with HCPs regarding events at the ED were held during the period of May 2019–January 2020. The Critical Incident Technique approach was used to guide the interviews and the qualitative analysis. Data were analyzed inductively using qualitative content analysis. Results Interview participants (n = 28) included 7 physicians (25%), 12 registered nurses (43%), 7 nurse assistants (25%) and 2 administrators (7%). Overall, 108 critical incidents were described. Eight categories that described functional and dysfunctional experiences within interprofessional teamwork were identified: salience of reflection; professional experience makes a difference; demanding physical and psychosocial work environment; balancing communication demands; lacking management support, structure, and planning; tensions between professional role and responsibility; different views on interprofessional teamwork; and confidence in interprofessional team members. Conclusion Findings of this study indicate that poor ED-specific communication and limited professional experience are essential factors in handling critical incidents related to interprofessional teamwork. An important aspect of critical incident management is the ergonomics of the physical work environment and how it enables interprofessional teamwork. This study emphasizes the factors enabling interprofessional teamwork to manage critical incidents in the complex working environment of the ED. Supplementary Information The online version contains supplementary material available at 10.1186/s13049-022-01034-0.

Project description:IntroductionEffective teamwork has been shown to optimize patient safety. However, research centered on the critical inputs, processes, and outcomes of team effectiveness in emergency medical services (EMS) has only recently begun to emerge. We conducted a theory-driven qualitative study of teamwork processes-the interdependent actions that convert inputs to outputs-by frontline EMS personnel in order to provide a model for use in EMS education and research.MethodsWe purposively sampled participants from an EMS agency in Houston, TX. Full-time employees with a valid emergency medical technician license were eligible. Using semi-structured format, we queried respondents on task/team functions and enablers/obstacles of teamwork in EMS. Phone interviews were recorded and transcribed. Using a thematic analytic approach, we combined codes into candidate themes through an iterative process. Analytic memos during coding and analysis identified potential themes, which were reviewed/refined and then compared against a model of teamwork processes in emergency medicine.ResultsWe reached saturation once 32 respondents completed interviews. Among participants, 30 (94%) were male; the median experience was 15 years. The data demonstrated general support for the framework. Teamwork processes were clustered into four domains: planning; action; reflection; and interpersonal processes. Additionally, we identified six emergent concepts during open coding: leadership; crew familiarity; team cohesion; interpersonal trust; shared mental models; and procedural knowledge.ConclusionIn this thematic analysis, we outlined a new framework of EMS teamwork processes to describe the procedures that EMS operators employ to convert individual inputs into team performance outputs. The revised framework may be useful in both EMS education and research to empirically evaluate the key planning, action, reflection, and interpersonal processes that are critical to teamwork effectiveness in EMS.

Project description:IntroductionCanadian patients presenting to the emergency department (ED) typically undergo a triage process where they are assessed by a specially trained nurse and assigned a Canadian Triage and Acuity Scale (CTAS) score, indicating their level of acuity and urgency of assessment. We sought to assess the ability of patients to self-triage themselves through use of one of two of our proprietary self-triage tools, and how this would compare with the standard nurse-driven triage process.MethodsWe enrolled a convenience sample of ambulatory ED patients aged 17 years or older who presented with chief complaints of chest pain, abdominal pain, breathing problems, or musculoskeletal pain. Participants completed one, or both, of an algorithm generated self-triage (AGST) survey, or visual acuity scale (VAS) based self-triage tool which subsequently generated a CTAS score. Our primary outcome was to assess the accuracy of these tools to the CTAS score generated through the nurse-driven triage process.ResultsA total of 223 patients were included in our analysis. Of these, 32 (14.3%) presented with chest pain, 25 (11.2%) with shortness of breath, 75 (33.6%) with abdominal pain, and 91 (40.8%) with musculoskeletal pain. Of the total number of patients, 142 (47.2%) completed the AGST tool, 159 (52.8%) completed the VAS tool and 78 (25.9%) completed both tools. When compared to the nurse-driven triage standard, both the AGST and VAS tools had poor levels of agreement for each of the four presenting complaints.ConclusionsSelf-triage through use of an AGST or VAS tool is inaccurate compared to the established standard of nurse-driven triage. Although existing literature exists which suggests that self-triage tools developed for specific subsets of complaints may be feasible, our results would suggest that adopting the self-triage approach on a broader scale for all-comers to the ED does not appear to be a viable option to enhance the current triage process. Further study is required to show if self-triage can be used in the ED to optimize the triage process.

Project description:ImportanceEmergency department (ED) triage substantially affects how long patients wait for care but triage scoring relies on few objective criteria. Prior studies suggest that Black and Hispanic patients receive unequal triage scores, paralleled by disparities in the depth of physician evaluations.ObjectivesTo examine whether racial disparities in triage scores and physician evaluations are present across a multicenter network of academic and community hospitals and evaluate whether patients who do not speak English face similar disparities.Design, setting, and participantsThis was a cross-sectional, multicenter study examining adults presenting between February 28, 2019, and January 1, 2023, across the Mass General Brigham Integrated Health Care System, encompassing 7 EDs: 2 urban academic hospitals and 5 community hospitals. Analysis included all patients presenting with 1 of 5 common chief symptoms.ExposuresEmergency department nurse-led triage and physician evaluation.Main outcomes and measuresAverage Triage Emergency Severity Index [ESI] score and average visit work relative value units [wRVUs] were compared across symptoms and between individual minority racial and ethnic groups and White patients.ResultsThere were 249 829 visits (149 861 female [60%], American Indian or Alaska Native 0.2%, Asian 3.3%, Black 11.8%, Hispanic 18.8%, Native Hawaiian or Other Pacific Islander <0.1%, White 60.8%, and patients identifying as Other race or ethnicity 5.1%). Median age was 48 (IQR, 29-66) years. White patients had more acute ESI scores than Hispanic or Other patients across all symptoms (eg, chest pain: Hispanic, 2.68 [95% CI, 2.67-2.69]; White, 2.55 [95% CI, 2.55-2.56]; Other, 2.66 [95% CI, 2.64-2.68]; P < .001) and Black patients across most symptoms (nausea/vomiting: Black, 2.97 [95% CI, 2.96-2.99]; White: 2.90 [95% CI, 2.89-2.91]; P < .001). These differences were reversed for wRVUs (chest pain: Black, 4.32 [95% CI, 4.25-4.39]; Hispanic, 4.13 [95% CI, 4.08-4.18]; White 3.55 [95% CI, 3.52-3.58]; Other 3.96 [95% CI, 3.84-4.08]; P < .001). Similar patterns were seen for patients whose primary language was not English.Conclusions and relevanceIn this cross-sectional study, patients who identified as Black, Hispanic, and Other race and ethnicity were assigned less acute ESI scores than their White peers despite having received more involved physician workups, suggesting some degree of mistriage. Clinical decision support systems might reduce these disparities but would require careful calibration to avoid replicating bias.

Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Project description:BackgroundWithin intensive care settings such as neonatal intensive care units, effective intra- and interprofessional teamwork has been linked to a significant reduction of errors and overall improvement in the quality of care. In Kenya, previous studies suggest that coordination of care among healthcare teams providing newborn care is poor. Initiatives aimed at improving intra- and interprofessional teamwork in healthcare settings largely draw on studies conducted in high-income countries, with those from resource-constrained low and middle countries, particularly in the context of newborn care lacking. In this study, we explored the nature of intra- and interprofessional teamwork among health care providers in newborn units (NBUs) of three hospitals in Kenya, and the professional and contextual dynamics that shaped their interactions.MethodsThis exploratory qualitative study was conducted in three hospitals in Nairobi and Muranga Counties in Kenya. We adopted an ethnographic approach, utilizing both in-depth interviews (17) and non-participant observation of routine care provision in NBUs (250 observation hours). The study participants included: nurses, nursing students, doctors, and trainee doctors. All the data were thematically coded in NVIVO 12.ResultsThe nature of intra- and interprofessional teamwork among healthcare providers in the study newborn units is primarily shaped by broader contextual factors and varying institutional contexts. As a result, several team types emerged, loosely categorized as the 'core' team which involves providers physically present in the unit most times during the work shift; the emergency team and the temporary ad-hoc teams which involved the 'core' team, support staff students and mothers. The emergence of these team types influenced relationships among providers. Overall, institutionalized routines and rituals shaped team relations and overall functioning.ConclusionsPoor coordination and the sub-optimal nature of intra-and interprofessional teamwork in NBUs are attributed to broader contextual challenges that include low staff to patient ratios and institutionalized routines and rituals that influenced team norming, relationships, and team leadership. Therefore, mechanisms to improve coordination and collaboration among healthcare teams in these settings need to consider contextual dynamics including institutional cultures while also targeting improvement of team-level processes including leadership development and widening spaces for more interaction and better communication.

Project description:BackgroundDevelopment of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI).MethodsUsing National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.ResultsOf 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85-0.87] in the deep neural network vs 0.74 [95%CI 0.72-0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82-0.83] in the deep neural network vs 0.69 [95%CI 0.68-0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit-a larger number of appropriate triages considering a trade-off with over-triages-across the range of clinical thresholds.ConclusionsCompared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.

Project description:BackgroundEmergency department (ED) crowding is associated with negative health outcomes, patient dissatisfaction, and longer length of stay (LOS). The addition of advanced diagnostic imaging (ADI), namely CT, ultrasound (U/S), and MRI to ED encounter work up is a predictor of longer length of stay. Earlier and improved prediction of patients' need for advanced imaging may improve overall ED efficiency. The aim of the study was to detect the association between ADI utilization and the structured and unstructured information immediately available during ED triage, and to develop and validate models to predict utilization of ADI during an ED encounter.MethodsWe used the United States National Hospital Ambulatory Medical Care Survey data from 2009 to 2014 to examine which sociodemographic and clinical factors immediately available at ED triage were associated with the utilization of CT, U/S, MRI, and multiple ADI during a patient's ED stay. We used natural language processing (NLP) topic modeling to incorporate free-text reason for visit data available at time of ED triage in addition to other structured patient data to predict the use of ADI using multivariable logistic regression models.ResultsAmong the 139,150 adult ED visits from a national probability sample of hospitals across the U.S, 21.9% resulted in ADI use, including 16.8% who had a CT, 3.6% who had an ultrasound, 0.4% who had an MRI, and 1.2% of the population who had multiple types of ADI. The c-statistic of the predictive models was greater than or equal to 0.78 for all imaging outcomes, and the addition of text-based reason for visit information improved the accuracy of all predictive models.ConclusionsPatient information immediately available during ED triage can accurately predict the eventual use of advanced diagnostic imaging during an ED visit. Such models have the potential to be incorporated into the ED triage workflow in order to more rapidly identify patients who may require advanced imaging during their ED stay and assist with medical decision-making.

Dataset Information

Can interprofessional teamwork reduce patient throughput times? A longitudinal single-centre study of three different triage processes at a Swedish emergency department.

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Can interprofessional teamwork reduce patient throughput times? A longitudinal single-centre study of three different triage processes at a Swedish emergency department.

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