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Record W4285089743 · doi:10.29173/cjen182

Improving assessments and follow-up for pediatric emergency department mental health visits

2022· article· en· W4285089743 on OpenAlexvenueaboutno aff
Teresa Lightbody, Jennifer Thull‐Freedman, Stephen B. Freedman, Nicole Finseth, Stephanie McConnell, Angela Coulombe, Jennifer Woods, Shelley Groves-Johnston, Bruce Wright, Matthew M. Morrissette, Amanda S. Newton

Bibliographic record

VenueCanadian Journal of Emergency Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentTriageGeneral partnershipMental healthMedicineQuality managementMedical emergencyBest practiceHealth carePatient safetyNursingFamily medicineOperations managementBusinessPsychiatryPolitical scienceEngineeringManagement system

Abstract

fetched live from OpenAlex

Background: Over the past decade, the number of children presenting to emergency departments (ED) with mental health (MH) concerns has increased substantially. EDs struggle to respond to this increase with approaches that comprehensively address patient needs. The lack of standardized processes to perform risk stratification, assess severity, and ensure access to follow-up care pose barriers to the provision of safe MH care. Our team addressed this gap by introducing an evidence-based care bundle to Alberta’s two pediatric EDs. This report presents the quality improvement (QI) approach used to ensure fidelity of implementation at one of the EDs. This initiative was funded by Alberta Innovates (Partnership for Research and Innovation in the Health System; PRIHS). Methods: We used the Model for Improvement to test and implement each bundle element: suicide risk screening (Ask Suicide-Screening Questions [ASQ]) at ED triage; a tool (HEADS-ED) to streamline and standardize MH assessments by ED-based MH nurse); and an urgent, single-session ‘Choice Appointment’ with a MH professional within 96 hours of the ED visit for patients lacking access to appropriate and timely MH follow-up care. The two ED-based bundle elements did not require additional resources or funding and are expected to reduce length of stay. The follow-up clinic option for ED patients without resources is intended to prevent crisis escalation and match patients with supports. Each new practice was introduced sequentially over a 2-week period. For each practice, we identified 1 to 2 improvement aims, developed key driver diagrams, and selected primary outcomes and measures. Each practice was implemented using Plan-Do-Study-Act (PDSA) cycles with initial tests of change starting small and becoming larger as learning accrued from previous cycles. Our QI team included families with lived experience, patient care and unit managers, nurse educators, frontline healthcare providers, content experts, and clinical leaders who supported staff and led change management strategies. A nurse was hired as a QI lead to support execution of PDSA cycles. We developed a sustainability plan which included embedding education regarding new practices in new healthcare staff orientation, having a measurement strategy to ensure that improvement was maintained, and planning for transition of responsibility for these processes to operational and medical leadership. Evaluation Methods: Primary aims included: 80% of targeted patients would receive the ASQ and HEADS-ED and 100% of children eligible for an Urgent, single-session ‘Choice Appointments’ would be offered it within 96 hours. We used clinical data from the electronic health record (Epic/Connect Care) as well as patient experience data collected via parent/caregiver surveys to determine if the aims for each practice were achieved. We included balancing measures to test whether changes in care in one part of the system introduced unintended consequences in other parts. We evaluated results for the primary aims using run charts to rapidly detect change according to established rules for detecting special cause. We discussed the results from each PDSA cycle in the context of existing healthcare resources to support implementation of each element of the bundle. Results: Tests of change to introduce suicide risk screening began February 1st, 2021. Performance was measured in weekly intervals. The median initial use of ASQ by triage nurses was with 77% of MH patients (686/901 patients), and over time, improved to 93% (319/350 patients), with special cause (shift) in noted September 2021. Tests of change to introduce the HEADS-ED tool began February 16th, 2021. Initial use of the HEADS-ED by MH nurses was 81% (440/555) and improved to 87% (201/227) with special cause (shift) noted August 2021. Urgent, single-session ‘Choice Appointments’ were offered to all patients who did not have timely and access to urgent follow-up with an existing mental healthcare provider with 89.1% having an appointment booked within 96 hours of the ED visit (139/156). Advice and Lessons Learned: Three plans were viewed as crucial to the success of this initiative: 1) a robust strategy to develop proposed changes based on best evidence combined with patient and staff engagement; 2) a comprehensive QI strategy to test, measure, and implement changes; and 3) regular communication and collaboration among ED staff, mental healthcare staff, patients/families, and hospital leadership. There were also lessons learned regarding what could have further enhanced project success: 1) enhanced communication strategies using multiple methods to ensure that project communications reached all stakeholders, including those not regularly present in the ED; 2) hiring the QI lead earlier to begin change management prior to bundle implementation; and 3) outlining a transition plan for clinical data management and bundle monitoring earlier to ease the QI transition to clinical leadership.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.347
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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