Bibliographic record
Abstract
A hospital has recently implemented a goal of 30% of discharges before noon to address overcrowding and boarding in the emergency department (ED). To accommodate this, resident didactics were cut to 30 min and the hospital medicine team starts rounding at 8:30 a.m. On rounds, teams are encouraged to prioritize patients who are possible discharges to meet the administrative goal. The medical student, who is covering two of the more complex patients who are not ready for discharge and about whom she has several questions, worries that this change in priorities will negatively impact her patients by delaying their evaluation. She asks about this change in rounding structure and how inpatient discharges affect the ED. Hospital flow—the movement of patients from the ED to inpatient services and subsequently to postdischarge facilities or domiciles—is an essential aspect of healthcare delivery, impacting costs, patient satisfaction, and outcomes.1 ED crowding and boarding have been associated with medication delays, adverse events, increased length of stay (LOS), and even in-hospital mortality.1, 2 Numerous proposals to improve hospital flow and reduce ED crowding have been introduced, including “discharge before noon” (DCBN), the practice of focusing on discharging admitted patients during the morning to facilitate flow from the ED in the early afternoon. DCBN is increasingly regarded as a performance metric for efficiency and hospital flow. Moreover, DCBN was the most cited early discharge goal of hospitalists and general internal medicine leaders from academic medical centers across the United States in a contemporary survey, and the initiative is highlighted in guidelines from regulatory organizations such as the Joint Commission.3 As optimal hospital flow is disrupted by ED overcrowding that tends to happen in the late afternoon, focusing on “freeing up” hospital beds earlier in the day is a reasonable solution. Several single-center studies have shown an association between DCBN and decreased ED boarding times or “admission density,” the percentage of total daily admissions occurring in each hour.4-6 Other studies have identified a decrease in LOS associated with increasing rates of DCBN.7 Based on these studies, DCBN is an attractive concept for hospital administration and providers. A major limitation of the evidence supporting DCBN is confounding bias. Studies evaluating morning discharges often incorporate several additional concomitant interventions, such as improved interdisciplinary communication, electronic medical record (EMR) tools, and increases in staffing.4, 5, 7 For instance, one study that found a correlation between increased rate of DCBN and decreased ED boarding time also incorporated increased staffing during periods of increased demand and streamlined triage processes and discharge tasks.6 Similarly, a study that identified an association between DCBN and reduced LOS also included increased weekend medical and social work staffing during the study period.8 These additional interventions likely contributed to decreased ED boarding times and LOS, making it difficult to isolate the impact of DCBN on these outcomes. Studies that have evaluated DCBN in isolation have not demonstrated improvement in ED crowding or hospital LOS.9-11 In fact, Rachoin et al. found that morning discharges were independently associated with higher LOS among patients admitted to Internal Medicine services.10 A plausible explanation for this observation is that hospitals often incentivize providers to discharge patients in the morning; indeed, some studies describe “prizes” awarded when discharging teams meet their “quota.”7 This incentive structure promotes a practice pattern that can reward keeping patients in the hospital longer, placing patients at risk of infections, delirium, and other hospital-related complications as well as increasing financial costs. The external validity of the literature supporting DCBN is also not clear. Studies showing positive impacts of DCBN are exclusively single-institution studies. These institutions are often large referral centers, with longer and more complex admissions than the national average. The largest study evaluating the impact of DCBN, performed by Kirubarajan et al., included five academic hospitals and two community-based teaching hospitals over a period of 7 years. The study found no association between morning discharges and decreased ED boarding time.11 Another multicenter prospective trial prioritizing morning discharges similarly showed no change in LOS.12 These results suggest that DCBN, while potentially effective when bundled with other interventions, is largely ineffective as a single strategy to improve hospital flow. Furthermore, the impact of DCBN on patient satisfaction and outcomes is not clear. One study at a single academic center found no improvement in patient satisfaction scores after an increase in the rate of DCBN from 14% to 24%.13 Studies demonstrating improvement in LOS or ED crowding after increased DCBN generally showed no associated increase in readmissions, but did not comment on other clinical outcomes such as time to treatment. As the goal of the DCBN intervention is ultimately to improve patient-centered outcomes by improving hospital flow, the intervention currently lacks evidence to support its effectiveness, and further investigations are necessary. There is also evidence that DBCN is harmful to physicians, nurses, and trainees. Mendlovic et al. found that, after increasing the rate of morning discharges in an Internal Medicine department, a higher volume of transfers from the ED in the late morning and early afternoon disrupted workflow for medical and nursing staff, which likely contributed to burnout. After several weeks, this negative impact on workflow led to a subsequent reversion to a lower rate of morning discharges, demonstrating a lack of sustainability.14 Another study found most internal medicine residents felt early discharge initiatives compromised their learning during teaching rounds by diverting attention from more active teaching cases and focusing instead on stable patients who are ready for discharge.15 While prioritizing patient care initiatives over teaching time is preferable for evidence-based patient care, given the absence of quality evidence supporting DCBN, the harm to education must be considered. As healthcare teams focus on discharging patients in the morning, attention shifts away from their other patients, who are likely less stable than those they are discharging. There is an absence of literature examining the consequences of DCBN initiatives on other admitted patients, making the impact of this shift in attention unclear. Ultimately, no single intervention is likely to substantially impact a process as complex and multifaceted as patient flow. The studies that showed the largest impacts on LOS and patient flow all included multiple interventions besides a target discharge time. Indeed, the Institute for Healthcare Improvement, in its 2020 white paper Achieving Hospital-wide Patient Flow, noted that “optimizing hospital flow… requires an appreciation of the hospital as an interconnected, interdependent system of care.” They recommend a systems-based approach to hospital flow that includes various approaches and a learning system with data analytics to allow for evaluation of each approach and tailoring of interventions to the needs of the unique system.16 One such approach is queuing theory, which uses applied mathematics to model patient flow. This approach has been used to model patient flow in hospital systems, intensive care units, EDs, and rural clinics in Malawi.17-19 These models, which are individualized to their unique settings, can then be used to evaluate combinations of interventions such as staffing, bed allocation, unit configuration, and discharge time to determine the optimal management for that particular system. Individual healthcare providers should focus on discharging patients when they are medically ready and advocating for systems-level changes to facilitate improved hospital flow. Stop prioritizing “discharge before noon” as a goal to improve hospital flow. Focus on patient-specific discharge goals. Hospital systems should use a systems-based approach, including queuing theory and mathematical modeling, to optimize patient flow. Design and perform thoughtful multicenter studies to evaluate the impact of various interventions on hospital flow. The prioritization of “discharge before noon” is another example of a “Thing We Do for No Reason.” The evidence supporting DCBN is mixed, and the few studies showing a positive impact of DCBN are substantially limited by confounding bias and questions regarding external validity. Furthermore, DCBN can lead to delayed discharges and can negatively impact physicians, nurses, and trainees. Hospitalists can instead focus on patient-specific discharge goals and advocate for systems-level changes to improve hospital flow. The authors declare no conflict of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".