Reducing PIA by over 50% While Generating New Patient Flows: A Comprehensive Assessment of Emergency Department Redesign
Bibliographic record
Abstract
Background On June 6, 2011 the Emergency Department (ED) at Southlake Regional Health Center, a very high-volume ED, initiated a comprehensive redesign project to improve patient waiting times. The primary initial goal of the project was to reduce Time to Physician’s Initial Assessment (TPIA) - one of the Key Performance Indicators (KPIs) tracked by the Ontario Ministry of Health and Long-Term Care. The objective was to achieve a significant improvement in TPIA without sacrificing performance on any other important KPIs such as Length of Stay (LOS), Left Without Being Seen (LWBS), or time to admission (T2A). The effect on TPIA was immediate and dramatic: the 90−th percentile TPIA declining from 4 hrs to under 2.5 hrs, with further improvements seen over time. The patient in-flows also increased; anecdotally this increase was directly related to shorter wait time. However, like any other large-scale and on-going system redesign project, the impacts are not limited to the listed KPIs, but are multi-dimensional, affecting patient inflows, flows within the ED, workloads, staffing levels, etc. Thus, teasing out the impact of system redesign requires from other concurrent factors (population changes, staffing changes, etc.) requires a comprehensive system assessment. The available data exhibits auto-correlations, heteroscedasticity, and interdependence among variables, rendering simple statistical analysis of individual KPIs inapplicable. We develop a novel methodology and conduct counterfactual analysis demonstrating that the decrease in TPIA, as well as new patient inflows can indeed be attributed to the ED redesign. This suggests that a similar system redesign should be considered by other EDs looking to improve wait times. Objectives To (1) statistically estimate the impacts of the redesign project on various performance measures over time, (2) examine whether the project’s initial goal of improvement in TPIA without compromising other service performance measures was achieved, and (3) study whether the project impacted patient inflows. Methods We (1) estimate simultaneous equations models to quantify interdependent and timevarying relations among variables, (2) conduct an iterative counterfactual analysis to estimate the mean-level impacts of the project, and (3) construct 95% confidence intervals for the estimated impacts using the Bootstrap method. Results We study project impacts over 720 days after it was initiated. During this time, the 90 th percentile of TPIA has been reduced by nearly 2.5 hours on average (translating into an over 50% improvement), with continuous improvement over the study period. This effect is statistically and operationally significant. The project also improved LOS for non-admitted patients (both acute and non-acute), and did not have statistically significant impact on LOS for admitted patients. There was also a decrease in LWBS, though it was not statistically significant. Thus the project achieved its stated primary goals. We also observed an increase in inflows of both acute and nonacute patients; our analysis confirms that this increase can be attributed to the project, indicating that improvements in TPIA attracted new patients to the ED. All of these effects have persisted over the 720-day post-project period. Conclusions The redesign project has significantly reduced TPIA over time while also improving some LOS measures; none of the waiting time KPIs were compromised. The reduction in TPIA also attracted significant volumes of new patients. However, the redesigned process was able to deal with this volume without compromising performance. The redesign project involved a number of major changes in ED operations. We provide an overview of these changes, and while our analysis cannot attribute specific project impacts to specific changes, we believe that implementing similar changes should receive strong consideration by other EDs. Conflicts of interest None
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".