Dataset Information

Functionality of Scarce Healthcare Resource Triage Teams During the COVID-19 Pandemic: A Multi-Institutional Simulation Study.

ABSTRACT: Plans for allocating scarce healthcare resources during the COVID-19 pandemic commonly involve the activation of institutional triage teams. These teams would be responsible for selecting patients who are most likely to survive to be prioritized to receive scarce resources. However, there is little empirical support for this approach.

Design

High-fidelity triage-team simulation study.

Setting

Healthcare institutions in Washington state.

Subjects

Triage teams, consisting of at least two senior clinicians and a bioethicist.

Interventions

Participants reviewed a limited amount of deidentified information for a diverse sample of critically ill patients. Teams then assigned each patient to one of five prioritization categories defined by likelihood of survival to hospital discharge. The process was refined based on observation and participant feedback after which a second phase of simulations was conducted.

Measurements and main results

Feasibility was assessed by the time required for teams to perform their task. Prognostic accuracy was assessed by comparing teams' prediction about likelihood of survival to hospital discharge with real-world discharge outcomes. Agreement between the teams on prognostic categorization was evaluated using kappa statistics. Eleven triage team simulations (eight in phase 1 and three in phase 2) were conducted from December 2020 to February 2021. Overall, teams reviewed a median of 23 patient cases in each session (interquartile range [IQR], 17-29) and spent a median of 102 seconds (IQR, 50-268) per case. The concordance between expected survival and real-world survival to discharge was 71% (IQR, 64-76%). The overall agreement between teams for placement of patients into prognostic categories was moderate (weighted kappa = 0.53).

Conclusions

These findings support the potential feasibility, accuracy, and effectiveness of institutional triage teams informed by a limited set of patient information items as part of a strategy for allocating scarce resources in healthcare emergencies. Additional work is needed to refine the process and adapt it to local contexts.

SUBMITTER: Butler CR

PROVIDER: S-EPMC8785932 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Functionality of Scarce Healthcare Resource Triage Teams During the COVID-19 Pandemic: A Multi-Institutional Simulation Study.

Butler Catherine R CR Webster Laura B LB Sakata Vicki L VL Tonelli Mark R MR Diekema Douglas S DS Gray Megan M MM

Critical care explorations 20220121 1

Plans for allocating scarce healthcare resources during the COVID-19 pandemic commonly involve the activation of institutional triage teams. These teams would be responsible for selecting patients who are most likely to survive to be prioritized to receive scarce resources. However, there is little empirical support for this approach.<h4>Design</h4>High-fidelity triage-team simulation study.<h4>Setting</h4>Healthcare institutions in Washington state.<h4>Subjects</h4>Triage teams, consisting of a ...[more]

PMID: 35083438

Similar Datasets

Project description:ImportanceThe COVID-19 pandemic prompted health care institutions worldwide to develop plans for allocation of scarce resources in crisis capacity settings. These plans frequently rely on rapid deployment of institutional triage teams that would be responsible for prioritizing patients to receive scarce resources; however, little is known about how these teams function or how to support team members participating in this unique task.ObjectiveTo identify themes illuminating triage team members' perspectives and experiences pertaining to the triage process.Design, setting, and participantsThis qualitative study was conducted using inductive thematic analysis of observations of Washington state triage team simulations and semistructured interviews with participants during the COVID-19 pandemic from December 2020 to February 2021. Participants included clinician and ethicist triage team members. Data were analyzed from December 2020 through November 2021.Main outcomes and measuresEmergent themes describing the triage process and experience of triage team members.ResultsAmong 41 triage team members (mean [SD] age, 50.3 [11.4] years; 21 [51.2%] women) who participated in 12 simulations and 21 follow-up interviews, there were 5 Asian individuals (12.2%) and 35 White individuals (85.4%); most participants worked in urban hospital settings (32 individuals [78.0%]). Three interrelated themes emerged from qualitative analysis: (1) understanding the broader approach to resource allocation: participants strove to understand operational and ethical foundations of the triage process, which was necessary to appreciate their team's specific role; (2) contending with uncertainty: team members could find it difficult or feel irresponsible making consequential decisions based on limited clinical and contextual patient information, and they grappled with ethically ambiguous features of individual cases and of the triage process as a whole; and (3) transforming mindset: participants struggled to disentangle narrow determinations about patients' likelihood of survival to discharge from implicit biases and other ethically relevant factors, such as quality of life. They cited the team's open deliberative process, as well as practice and personal experience with triage as important in helping to reshape their usual cognitive approach to align with this unique task.Conclusions and relevanceThis study found that there were challenges in adapting clinical intuition and training to a distinctive role in the process of scarce resource allocation. These findings suggest that clinical experience, education in ethical and operational foundations of triage, and experiential training, such as triage simulations, may help prepare clinicians for this difficult role.

Project description:ObjectivesSimulation and evaluation of a prioritization protocol at a German university hospital using a convergent parallel mixed methods design.DesignProspective single-center cohort study with a quantitative analysis of ICU patients and qualitative content analysis of two focus groups with intensivists.SettingFive ICUs of internal medicine and anesthesiology at a German university hospital.PatientsAdult critically ill ICU patients ( n = 53).InterventionsAfter training the attending senior ICU physicians ( n = 13) in rationing, an impending ICU congestion was simulated. All ICU patients were rated according to their likelihood to survive their acute illness (good-moderate-unfavorable). From each ICU, the two patients with the most unfavorable prognosis ( n = 10) were evaluated by five prioritization teams for triage.Measurements and main resultsPatients nominated for prioritization visit ( n = 10) had higher Sequential Organ Failure Assessment scores and already a longer stay at the hospital and on the ICU compared with the other patients. The order within this worst prognosis group was not congruent between the five teams. However, an in-hospital mortality of 80% confirmed the reasonable match with the lowest predicted probability of survival. Qualitative data highlighted the tremendous burden of triage and the need for a team-based consensus-oriented decision-making approach to ensure best possible care and to support professionals. Transparent communication within the teams, the hospital, and to the public was seen as essential for prioritization implementation.ConclusionsTo mitigate potential bias and to reduce the emotional burden of triage, a consensus-oriented, interdisciplinary, and collaborative approach should be implemented. Prognostic comparative assessment by intensivists is feasible. The combination of long-term ICU stay and consistently high Sequential Organ Failure Assessment scores resulted in a greater risk for triage in patients. It remains challenging to reliably differentiate between patients with very low chances to survive and requires further conceptual and empirical research.

Project description:ObjectivesWe draw on institutional theory to explore the roles and actions of innovation teams and how this influences their behaviour and capabilities as 'institutional entrepreneurs (IEs)', in particular the extent to which they are both 'willing' and 'able' to facilitate transformational change in healthcare through service redesign.DesignA longitudinal qualitative study that applied a 'researcher in residence' as an ethnographic approach.SettingThe development and implementation of two innovation projects within a single public hospital setting in an Australian state jurisdiction.ParticipantsTwo innovation teams, with members including senior research fellows, PhD scholars and front-line clinicians (19 participants and 47 interviews).ResultsDespite being from the same hospital, the two innovation teams occupied contrasting subject positions with one facilitating transformational improvements in service delivery, while the other sought more conservative improvements. Cast as 'IEs' we show how one team took steps to build legitimacy for their interventions enabling spread and scale in improvements and how, in the other case, failure to build legitimacy resulted in unintended consequences which undermined the sustainability of the improvements achieved.ConclusionsAdopting an institutional approach provided insight into the 'willingness' and 'ability' to facilitate transformational change in healthcare through service redesign. The manner in which innovation teams operate from different subject positions influences the structural and normative legitimacy afforded to their activities. Specifically, we observed that those with the most power (organisational or professional) to bring about transformational change can be the least willing to do so in ways which challenge current practice. Those most willing to challenge the status quo (more peripheral organisation members or professionals) can be least able to deliver transformation. Better understanding of these insights can inform healthcare leaders in supporting innovation team efforts, considering their subject position.

Project description:BackgroundUnlike well-established diseases that base clinical care on randomized trials, past experiences, and training, prognosis in COVID19 relies on a weaker foundation. Knowledge from other respiratory failure diseases may inform clinical decisions in this novel disease. The objective was to predict 48-hour invasive mechanical ventilation (IMV) within 48 h in patients hospitalized with COVID-19 using COVID-like diseases (CLD).MethodsThis retrospective multicenter study trained machine learning (ML) models on patients hospitalized with CLD to predict IMV within 48 h in COVID-19 patients. CLD patients were identified using diagnosis codes for bacterial pneumonia, viral pneumonia, influenza, unspecified pneumonia and acute respiratory distress syndrome (ARDS), 2008-2019. A total of 16 cohorts were constructed, including any combinations of the four diseases plus an exploratory ARDS cohort, to determine the most appropriate cohort to use. Candidate predictors included demographic and clinical parameters that were previously associated with poor COVID-19 outcomes. Model development included the implementation of logistic regression and three ensemble tree-based algorithms: decision tree, AdaBoost, and XGBoost. Models were validated in hospitalized COVID-19 patients at two healthcare systems, March 2020-July 2020. ML models were trained on CLD patients at Stanford Hospital Alliance (SHA). Models were validated on hospitalized COVID-19 patients at both SHA and Intermountain Healthcare.ResultsCLD training data were obtained from SHA (n = 14,030), and validation data included 444 adult COVID-19 hospitalized patients from SHA (n = 185) and Intermountain (n = 259). XGBoost was the top-performing ML model, and among the 16 CLD training cohorts, the best model achieved an area under curve (AUC) of 0.883 in the validation set. In COVID-19 patients, the prediction models exhibited moderate discrimination performance, with the best models achieving an AUC of 0.77 at SHA and 0.65 at Intermountain. The model trained on all pneumonia and influenza cohorts had the best overall performance (SHA: positive predictive value (PPV) 0.29, negative predictive value (NPV) 0.97, positive likelihood ratio (PLR) 10.7; Intermountain: PPV, 0.23, NPV 0.97, PLR 10.3). We identified important factors associated with IMV that are not traditionally considered for respiratory diseases.ConclusionsThe performance of prediction models derived from CLD for 48-hour IMV in patients hospitalized with COVID-19 demonstrate high specificity and can be used as a triage tool at point of care. Novel predictors of IMV identified in COVID-19 are often overlooked in clinical practice. Lessons learned from our approach may assist other research institutes seeking to build artificial intelligence technologies for novel or rare diseases with limited data for training and validation.

Project description:BackgroundSimulation-based training used to train healthcare teams' skills and improve clinical practice has evolved in recent decades. While it is evident that technical skills training is beneficial, the potential of human factor training has not been described to the same extent. Research on human factor training has been limited to marginal and acute care scenarios and often to validate instruments. This systematic review aimed to investigate the effectiveness of simulation-based training in improving in-hospital qualified healthcare teams' human factor skills.MethodA review protocol outlining the study was registered in PROSPERO. Using the PRISMA guidelines, the systematic search was conducted on September 28th, 2021, in eight major scientific databases. Three independent reviewers assessed title and abstract screening; full texts were evaluated by one reviewer. Content analysis was used to evaluate the evidence from the included studies.ResultsThe search yielded 19,767 studies, of which 72 were included. The included studies were published between 2004 and 2021 and covered research from seven different in-hospital medical specialisms. Studies applied a wide range of assessment tools, which made it challenging to compare the effectiveness of human factor skills training across studies. The content analysis identified evidence for the effectiveness. Four recurring themes were identified: (1) Training human factor skills in qualified healthcare teams; (2) assessment of human factor skills; (3) combined teaching methods, and (4) retention and transfer of human factor skills. Unfortunately, the human factor skills assessments are variable in the literature, affecting the power of the result.ConclusionSimulation-based training is a successful learning tool to improve qualified healthcare teams' human factor skills. Human factor skills are not innate and appear to be trainable similar to technical skills, based on the findings of this review. Moreover, research on retention and transfer is insufficient. Further, research on the retention and transfer of human factor skills from simulation-based training to clinical practice is essential to gain knowledge of the effect on patient safety.

Dataset Information

Functionality of Scarce Healthcare Resource Triage Teams During the COVID-19 Pandemic: A Multi-Institutional Simulation Study.

Design

Setting

Subjects

Interventions

Measurements and main results

Conclusions

Publications

Functionality of Scarce Healthcare Resource Triage Teams During the COVID-19 Pandemic: A Multi-Institutional Simulation Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets