Reducing “Left without being seen” in a community emergency department: A rapid-cycle change project
Bibliographic record
Abstract
Background and objective: The COVID-19 pandemic in 2020 increased the volume of patients seeking care in the Emergency Department (ED) for a respiratory crisis. Our community hospital experienced a filling of inpatient beds, leading to an overflow of admitted patients in the ED, where adequate staff (nurses, physicians, radiology, and laboratory staff), equipment, and rooms or places for patients were lacking. Times to obtain procedures that included cardiology, laboratory, and radiology performed and resulted significantly increased. Left without being seen (LWBS) is a challenge faced by EDs across the United States (US) and has become more prevalent since the COVID-19 pandemic. Best practice suggests an LWBS rate of less than 2%, but our hospital experienced an increasing rate of hitting over 5\% in January 2021. To reduce this rate, we implemented multiple rapid-cycle Plan-Do-Check-Act (PDCA) change interventions in triage and throughout the ED.Implementation/Methods: We implemented several rapid-cycle change interventions with a high-level action plan. These actions included hiring medical/surgical nurses to care for admitted patients awaiting beds, adding additional medical providers, implementing greeters, creating specialty chairs inside a major hospital thoroughfare, opening a 12-bed Admit Care Unit (ACU) adjacent to the ED, and more.Results: The rate of LWBS decreased from a high of 5.3% in January 2021 to 1.09% in January 2022.Conclusion/Implications to Practice: Patients in the ED recorded as LWBS are at higher risk for safety and quality transgressions. We continue to work toward excellent patient care by continuing to implement rapid-cycle changes in response to barriers as they arise. More research is needed to expand and rethink the process of ED throughput during pandemics and emergent national crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".