Abstract
Introduction:
Half a million pediatric inpatients undergo surgery each year. At a large, tertiary-care academic pediatric hospital, the time required to transition inpatients from the acute care units to the preoperative zones was lengthy and unreliable, resulting in costly operating room (OR) delays.
Methods:
In 2024, the perioperative microsystem quality improvement team aimed to improve preoperative efficiency by 10% for nonintensive care unit inpatients undergoing nonemergent surgery in the main hospital ORs. The primary outcome measure was the time from the OR’s phone call for the inpatient to completion of preoperative nursing tasks, as documented in the electronic health record. The Institute for Healthcare Improvement model framework guided the work. Multiple Plan-Do-Study-Act cycles aimed to improve communication among acute care unit, OR, and preoperative staff. Data were displayed as statistical process control charts.
Results:
Mean time from calling for the inpatient to preoperative task completion improved from 48.2 minutes at baseline to 43.7 minutes (4.5 min, 9.3% improvement), with a sustained downward shift of the centerline on statistical process control analysis. After the second intervention, process variability also decreased relative to baseline and persisted after implementation.
Conclusions:
Multiple Plan-Do-Study-Act cycles focused on inpatient-to-OR readiness led to more than 9% improvement in patient flow, enhanced communication between groups, increased awareness of preoperative preparation tasks, and improved trust in OR readiness signals to the preoperative zones. Further study is needed to determine whether similar interventions would be effective at improving efficiency at other hospitals.
INTRODUCTION
Approximately half a million pediatric inpatients undergo surgery each year.1 Delays in inpatient flow to the operating room (OR) lead to inefficient use of OR time, directly impacting patient care, revenue, resource use, and staff satisfaction.2–6 Inconsistencies in communication, handoffs, timing, ownership, staffing, and process initiation can all contribute to delayed inpatient readiness for surgery.7–12
At Seattle Children’s Hospital (SCH), the time required to transition inpatients from the acute care units to the preoperative zones was lengthy and unreliable. There was no standardized signal from the OR to call for inpatients having surgery, leading to costly OR delays. Given these issues, the perioperative microsystem quality improvement team (QI team) decided to focus on improving inpatient-to-OR flow. This frontline multidisciplinary QI team was formed in 2020 to improve patient safety and quality in SCH’s perioperative space and includes anesthesiologists, surgeons, perioperative nurses, data analysts, administrators, QI experts, and a patient family representative, among others. This team sought to examine, understand, and improve efficiency in the inpatient preoperative process, with the following SMART aim: improve preoperative efficiency by 10% over 1 year (2024) for non-intensive care unit (non-ICU) inpatients undergoing non-emergent surgery in the main SCH ORs. Preoperative efficiency was defined as the time from the OR’s call for the inpatient to completion of preoperative tasks, as documented in the electronic health record (EHR).
METHODS
Context
SCH is a large tertiary-care academic pediatric hospital that performed more than 23,000 surgeries in 2024, of which approximately 5,900 were inpatient procedures.13 All inpatient surgeries take place at the main SCH campus in 1 of 3 different surgical locations; each has its own preoperative and recovery zone. Although preoperative nurses bring outpatients to the appropriate preoperative area based on scheduled surgery start times, inpatients require a call/signal from the OR to initiate transfer to the preoperative area. Although many of the non-ICU inpatients undergoing scheduled elective or add-on (urgent and nonurgent) surgery are located on surgical units, they may also come from medicine, rehabilitation, and cancer care units, where acute care nurses are less familiar with preoperative readiness requirements. ICU patients and those undergoing emergent/stat procedures were excluded because the anesthesia teams transport them directly to the ORs. The QI team also excluded emergency department patients because the project focused on inpatients.
Correct timing of inpatient transfer to the preoperative area is critical. If inpatients arrive too early, the preoperative zones may fill, causing backups and delays for patients entering and leaving the ORs. If inpatients arrive too late, ORs may sit idle, leading to costly delays, resource waste, and rushed experiences for patients and families. Baseline data from 2023 pulled from the hospital’s EHR (Epic Systems, Verona, WI) showed that it took more than 48 minutes to transfer inpatients to the preoperative area, check them in, and have them ready to speak to the surgical and anesthesia teams before surgery.
Discussions with OR nurses, preoperative nurses, acute care nurses, and clinical nurse specialists about inpatient-to-OR flow revealed several issues. First, multiple acute care nurses were unaware of their patients’ scheduled surgery times and were confused about preoperative readiness requirements. Second, SCH often employs traveling nurses, which makes it necessary to provide continuing education on the preoperative inpatient check-in process. Third, OR and preoperative nurses lacked clarity about whom to call, when to call, and how to call for inpatients. Fourth, the OR lacked standard signaling for calling for inpatients—was it up to the surgeon, anesthesiologist, or OR nurse to signal that it was time to call, and how far in advance should the OR call for the next inpatient? Inconsistencies in this process often resulted in the OR calling for inpatients too far in advance, which led to preoperative nurse distrust in the timing signaled by the OR.
Feedback about inconsistencies in communication, timing, and ownership helped the QI team develop several targeted interventions. The Institute for Healthcare Improvement model framework guided the work.14 The QI team used process mapping, simplified failure modes and effects analysis, current-state analysis, key driver diagram, and Plan-Do-Study-Act (PDSA) cycle methodologies to develop and implement interventions.
Interventions
Process mapping of inpatient-to-OR flow revealed a complex pathway, with multiple risks. (See figure, Supplemental Digital Content 1, which displays the process map. Process map depicting inpatient case scheduling, perioperative check-in, and acute care unit preoperative readiness processes. PDSA cycle interventions (red stars) are placed next to the risk(s) they addressed. 1, OR coordinator (ORC) phone calls; 2, OR signs; 3, 45-min prompt; 4, visibility board; CNA, certified nursing assistant; CHG, chlorhexidine gluconate; HCG, human chorionic gonadotropin; NPO, nil per os; PACU, postanesthesia care unit; preop, preoperative area; PIV, peripheral IV; RN, registered nurse, https://links.lww.com/PQ9/A788.) Key driver diagram development revealed that many of the proposed interventions involved improved communication between teams (Fig. 1). Thus, the QI team developed several interventions focused on improving communication.
Fig. 1.
Key driver diagram. The QI team implemented the first 7 interventions. PACU, postanesthesia care unit; preop, preoperative area.
Communication Interventions
OR Coordinator (ORC) Phone Calls
The ORC coordinates surgical cases with the OR charge nurse and the charge anesthesiologist. Before 2024, overnight preoperative nurses called the overnight acute care nurses caring for the first inpatient surgical cases of the day to update them on their patients’ surgical timing. However, no one made calls to acute care nurses whose patients were not the first cases. Thus, the QI team introduced scripted ORC phone calls for inpatient surgical cases scheduled after 9 am.
OR Signs
The presence of multiple preoperative locations contributed to confusion among OR nurses about whom to call to send for inpatients. To address this, the QI team posted signs by the OR nursing computers with the phone number that corresponded to each OR’s specific preoperative area.
Forty-five-minute Prompt
Conversations with staff indicated that OR nurses did not know when to call for inpatients, with calls typically occurring after the prior surgery stopped. There was also confusion about which OR team member(s) should initiate the call signal. The QI team decided that although it was everyone’s responsibility to bring up the topic, only the surgeons knew how much operative time remained. Baseline data showed that it took 48 minutes from the OR’s phone call to completion of preoperative tasks. Thus, if the surgeon notified the OR nurse when they had approximately 45 minutes of operating time left (and the nurse signaled the preoperative area at this time), the next inpatient could be checked in before the prior case ended. Starting in the neurosurgery OR, the QI team posted a 45-minute reminder at the end of the time-out checklist.
Visibility Board
The final communication intervention was a visual cue on the preoperative/recovery EHR tracking boards to remind the preoperative nurses to check with the OR if the current case was almost over and the OR team had forgotten to call for the next inpatient. Patient names and cases are highlighted in different colors on these boards, with each color indicating their status.
Preoperative Task Intervention
In addition to targeting improved communication, the QI team also brought attention to required preoperative tasks (Fig. 1). Acute care nurses should complete several tasks before sending inpatients to the preoperative area, including changing bed linens, cleaning the patient with chlorhexidine gluconate wipes, and performing a pregnancy test, if indicated. To increase awareness about these requirements, the QI team helped organize several educational sessions for acute care nurses.
Measures
Outcome Measures
“Call for patient → Preoperative tasks complete” was the primary outcome measure used to evaluate preoperative efficiency. This measure encompassed the time it took for (1) the OR to call for the next inpatient, (2) the preoperative nurse to call and receive handoff from the acute care nurse, (3) the acute care nurse to complete remaining preoperative tasks (eg, chlorhexidine gluconate wipes), (4) transport of the patient to the preoperative area, and (5) patient check-in by the preoperative nurse, including completion of unfinished preoperative tasks.
The QI team monitored 2 secondary outcome measures: “Call for patient → Previous case stop” (ie, surgery stop) and “Preoperative tasks complete → OR ready.” The former measure provided information on how well the OR team did at calling for the next inpatient 45 minutes before surgery ended. The latter measure assessed whether the patient was checked into the preoperative area early enough for all relevant parties (surgery, anesthesia, and the OR nurse) to have time to speak with the patient and family before surgery.
Process Measures
The QI team monitored 3 process measures; each was a component of the primary outcome measure. The first process measure, “Call for patient → Patient sent for,” indicated how long it took for someone to be deployed to the acute care unit to pick up the patient after receiving the OR’s call, and included the time it took for the preoperative nurse to receive handoff from the acute care nurse. The second process measure, “Patient sent for → In preoperative area/induction room,” covered the time it took to transport the patient from the acute care unit to the preoperative area. The third process measure, “In preoperative area/induction room → Preoperative tasks complete,” indicated how long it took the preoperative nurse to check the patient in after they arrived in the preoperative area.
Balancing Measure
The QI team monitored 1 balancing measure: “Preoperative tasks complete → Anesthesia start/in OR (whichever was earlier)” to determine if the interventions resulted in inpatients arriving to the preoperative area too early. This measure reflected the time that patients and families spent in the preoperative area after check-in, waiting for surgery and anesthesia to begin. If patients and their families wait longer in the preoperative area, they could become frustrated by the delay. Also, if preoperative area rooms fill, other patients could be denied access to the preoperative area and/or recovery, causing backups and further OR delays.
Analyses
Data were collected from the EHR (Epic) and displayed as statistical process control (SPC) charts. Each week, the QI team examined SPC chart trends to evaluate the effects of the interventions. SPC analyses were performed using the guidance provided by Provost and Murray15 and R version 4.4 (R Core Team 2025).16 Measures that could not be calculated due to incomplete or missing data were excluded. The QI team attempted to obtain at least 12 weeks of data for each PDSA cycle to evaluate process stability before moving on to the next intervention. However, SPC figures are presented monthly for simplicity. After the project’s completion and transition to operational leaders, the QI team tracked post-intervention data to assess whether improvements were sustained.
Ethical Considerations
The QI team submitted this project to SCH’s institutional review board, which determined that it did not constitute human subjects research. The project was expected to benefit all families, including non-English-speaking families who require an interpreter. The QI team tracked race, ethnicity, and language data to monitor the effects of the interventions on different demographic groups. Data were pulled from the EHR into a secure database, which was then used to populate a confidential report.
RESULTS
Interventions
Intervention evolution and details are shown in Table 1.
Table 1.
Intervention Evolution and Details
| PDSA Cycle Number | Interventions | Implementation Date | Intervention Description |
|---|---|---|---|
| 1 | ORC phone calls | Mid-January 2024 | • Scripted ORC phone calls introduced for all inpatient surgeries after 9 am • ORCs called acute care nurses and sent secure EHR chat messages to the following: 1. Notify the acute care nurse of their patient’s estimated surgical time 2. Reference hospital job aids for how to prepare inpatients for surgery 3. Ensure the availability of the patient’s legal guardian for consent purposes • Overnight preoperative nurses used a script to call for inpatient surgeries before 9am |
| 2 | OR signs | Late March 2024 | • Signs posted by OR nursing computers with phone number corresponding to each OR’s specific preoperative area • OR nurses educated about signs at morning huddles and via email • OR nurses reminded to document the required “Call for patient” time stamp in the EHR • Preoperative nurses reminded to document the required “Send for patient” time stamp |
| 3 | 45-min prompt | May 2024: neurosurgery pilot | • Neurosurgery OR selected for pilot because neurosurgeons often operate on inpatients • Sticker added to the end of time-out checklist in primary neurosurgery OR: “When will there be 45 minutes left in surgery to call for the next patient?” • Plan introduced at all-perioperative services staff meeting and via multiple emails |
| Late June: all ORs | • Pilot expanded to all ORs and surgical services • Stickers posted at the end of the time-out checklist in every OR • Informed surgical, anesthesia, and nursing teams via email |
||
| 4 | Visibility board | Mid-November 2024 | • Color change went live on preoperative area/recovery EHR tracking boards • A flag reminds preoperative nurses to check with the OR about the timing of the next inpatient • Text color changes from black to orange for an inpatient who needs to be called for when the previous case is 60 min from its scheduled end time |
| NA | Preoperative tasks | April–November 2024 | • Nurse specialists, managers, and preoperative nurses educated acute care nurses about required preoperative tasks at multiple staff and in-service meetings |
NA, not applicable
Outcome Measures
Implementation of sequential interventions via multiple PDSA cycles improved the timeliness of inpatient progression to the OR. The primary outcome measure, “Call for patient → Preoperative tasks complete,” decreased from a mean of 48.2 minutes during the baseline period to 43.7 minutes (4.5 min, 9.3% improvement) (Fig. 2). SPC analysis revealed a sustained downward shift of the centerline compared to baseline after implementing the first intervention. After the second intervention, variability around the X-bar chart mean also decreased compared to baseline and persisted post-implementation (Fig. 2). The S chart was stable except for 2 outliers (Fig. 2). The greatest gains in efficiency occurred among patients with the longest check-in times (Table 2).
Fig. 2.
The X-bar and S charts for the outcome measures. X-bar (A) and S (B) charts for the primary outcome measure “Call for patient” to “Preoperative tasks complete.” 1, ORC phone calls; 2, OR signs; 3, 45-minute prompt; 4, visibility board; 5, postimplementation period; Jan, January; preop, preoperative.
Table 2.
Summary Statistics for the Primary Outcome Measure “Call for Patient” to “Preoperative Tasks Complete”
| Intervention Period | Group Size, n | Mean, min | SD, min | Median, min | Quantiles, min | ||||
|---|---|---|---|---|---|---|---|---|---|
| Q0.05 | Q0.25 | Q0.5 | Q0.75 | Q0.95 | |||||
| Baseline | 393 | 48.3 | 19 | 44.0 | 26.0 | 35.0 | 44.0 | 57.0 | 85.0 |
| ORC phone calls | 70 | 44.9 | 16 | 42.5 | 24.9 | 35.0 | 42.5 | 53.8 | 74.2 |
| OR signs | 136 | 45.0 | 16 | 43.0 | 20.8 | 36.8 | 43.0 | 52.2 | 74.2 |
| 45-min prompt | 439 | 44.1 | 15 | 42.0 | 22.9 | 33.0 | 42.0 | 53.0 | 71.1 |
| Visibility board | 275 | 42.5 | 15 | 41.0 | 21.0 | 33.0 | 41.0 | 49.5 | 70.3 |
| Postimplementation | 832 | 43.6 | 17 | 41.0 | 22.0 | 32.0 | 41.0 | 52.0 | 75.0 |
n, number; Q, quantile.
The secondary outcome measure, “Call for patient → Previous case stop” (ie, surgery stop), also improved. During the baseline period, calls for patients frequently occurred after the prior surgery ended (mean –28.7 min). By the second intervention, there was a sustained improvement, with the OR calling for patients 2.5 minutes before the end of the case (108.7% improvement) (Fig. 3). There was also reduced variability around the mean after the second intervention. The S chart was unstable (Fig. 3).
Fig. 3.
The X-bar and S charts for the outcome measures. X-bar (A) and S (B) charts for the secondary outcome measure “Call for patient” to “Previous case stop.” The negative baseline number on the X-bar chart indicates that the OR team called for the inpatient after the surgery ended. 1, ORC phone calls; 2, OR signs; 3, 45-minute prompt; 4, visibility board; 5, postimplementation period; Jan, January. SD, standard deviation.
The secondary outcome measure, “Preoperative tasks complete → OR ready,” improved (Fig. 4). Preoperative nurses completed preoperative tasks earlier, allowing OR teams more time to talk with patients while the ORs were being set up. The baseline mean of 17.8 minutes increased to 24.4 minutes after the first intervention (6.6 min, 37.1% improvement), with reduced variability around the mean noted after the second intervention. The S chart was stable except for 3 outliers (Fig. 4).
Fig. 4.
The X-bar and S charts for the outcome measures. X-bar (A) and S (B) charts for the secondary outcome measure, “Preoperative tasks complete” to “OR ready.” 1, ORC phone calls; 2, OR signs; 3, 45-minute prompt; 4, visibility board; 5, postimplementation period; preop, preoperative; Jan, January.
Process Measures
The first process measure, “Call for patient → Patient sent for,” did not change despite several QI interventions. The average time for this measure remained around 20 minutes throughout the study period. The S chart had several outliers.
The second process measure, “Patient sent for → In preoperative area/induction room,” improved from a mean of 19 to 17.3 minutes (1.7 min, 8.9% improvement), with reduced variability around the mean beginning after the second intervention. (See figure, Supplemental Digital Content 2, which displays the X-bar chart for the process measure “Patient sent for” to “In preoperative area/induction room”. This chart should be interpreted with caution as the S chart indicates an unstable process. 1, ORC phone calls; 2, OR signs; 3, 45-min prompt; 4, visibility board; 5, postimplementation period; Jan, January; preop, preoperative, https://links.lww.com/PQ9/A789.) However, the S chart was unstable due to outliers.
The third process measure, “In preoperative area/induction room → Preoperative tasks complete,” improved from a mean of 10 to 8.3 minutes (1.7 min, 17% improvement). (See figure, Supplemental Digital Content 3, which displays the X-bar chart for the process measure “In preoperative area/induction room” to “Preoperative tasks complete.” This chart should be interpreted with caution as the S chart indicates an unstable process. 1, ORC phone calls; 2, OR signs; 3, 45-min prompt; 4, visibility board; 5, postimplementation period; Jan, January; preop, preoperative, https://links.lww.com/PQ9/A790.) However, the S chart was unstable, so these results are difficult to interpret.
Balancing Measure
The balancing measure, “Preoperative tasks complete → Anesthesia start/in OR,” increased from a baseline mean of 44.3 to 47.8 minutes after the first intervention (3.5 min, 7.9% change). (See figure, Supplemental Digital Content 4, which displays the X-bar chart for the balancing measure “Preoperative tasks complete” to “Anesthesia start/in OR.” This chart should be interpreted with caution as the S chart indicates an unstable process. 1, ORC phone calls; 2, OR signs; 3, 45-min prompt; 4, visibility board; 5, postimplementation period; Jan, January; preop, preoperative, https://links.lww.com/PQ9/A791.) As with the prior measures, the S chart was unstable due to outliers.
Equity Analysis
The QI team tracked race, ethnicity, and language data to monitor the effects of the interventions on different demographic groups. Baseline data for the primary outcome measure, “Call for patient → Preoperative tasks complete,” showed that non-English-speaking patients and families took 2 minutes longer to check in than English-speaking families (mean 50.0 versus 48.0 min). Post-implementation, non-English speakers were checked in 0.9 minutes faster than English speakers (mean 42.8 versus 43.7 min). Insufficient data limited analyses of race and ethnicity.
DISCUSSION
The QI team improved preoperative efficiency by 9.3% in 2024 for non-ICU inpatients undergoing nonemergent surgery at the main SCH campus. Although this fell short of the SMART aim of 10%, the project resulted in increased consistency in the preoperative process, as demonstrated by reduced variability around the mean for the primary outcome measure “Call for patient → Preoperative tasks complete” (Fig. 2). This reduction in variability, which occurred after the second intervention and persisted post-implementation (for all 3 outcome measures), is likely related to improvements for patients with the longest check-in times. The greatest improvement gains for the primary outcome measure were concentrated in the upper quantiles, with the longest 5% of check-in times at baseline declining by 10–15 minutes post-intervention (Table 2). A more reliable and efficient inpatient readiness process helps reduce unnecessary OR delays and improve staff satisfaction. By checking-in inpatients earlier, patients and families also have more time to talk with the surgeons, anesthesiologists, and OR nurses before entering the OR.
This project enhanced communication between acute care and perioperative staff, which led to improved efficiency and staff satisfaction. Although not objectively measured, anecdotal comments from preoperative nurses indicated increased trust in the inpatient readiness timing signaled by the OR, which may explain the reduced variability observed after the interventions. Anecdotal comments from acute care nurses also revealed improved awareness of their patients’ surgical timing. This project encouraged team engagement and ownership of inpatient flow to the OR and generated improvements that persisted after completion.
In addition, this project benefited both English-speaking and non-English-speaking patients and families. Non-English speakers took longer to check in than English speakers at baseline, which is not surprising given the need for an interpreter. However, after the project was complete, check-in times for both groups were similar, if not better, for the non-English speakers.
These findings are consistent with existing literature demonstrating that interventions targeting teamwork and communication in the perioperative environment improve team culture and may reduce OR delays and other adverse outcomes.5,17 Standardized communication among interdisciplinary perioperative team members supports patient safety and improves quality of care.11,18 Additionally, the implementation of structured team communication workflows, evidence-based perioperative protocols, and coordinated multidisciplinary process-improvement design have been associated with reductions in last-minute intraoperative requests, improved team coordination, and increased OR efficiency.6,19,20
This project has several limitations. Although the X-bar charts for multiple measures showed improvements, instability in the S charts made many of the results difficult to interpret. Instability was largely caused by outliers in the data. For example, the 2 S chart outliers for the primary outcome measure were caused by three patients; one patient had inaccurate timestamp documentation, one patient had a lack of OR staff availability, and one patient was medically complex (Fig. 2).
Second, some interventions were less effective than others. Several surgeons expressed concern about being responsible for the 45-minute prompt, and the stickers were eventually removed because there was no additional improvement gained from this intervention. Although the mean centerline for the primary outcome measure decreased after the first PDSA cycle, there was no further change following implementation of the other interventions. It is not clear whether this downward shift would have occurred had a different intervention been instituted first, or whether only the ORC phone calls led to a true and persistent change. It is possible that the gains made with this intervention left less room for further improvement.
Third, there was a 3.5-minute increase in the balancing measure, which tracked the time that patients and families spent in the preoperative area after being checked in, waiting for surgery. This does not seem excessive enough to result in family frustration or backup of the preoperative zones, though the QI team did not directly measure these outcomes.
Finally, the SCH experience may not generalize to other hospitals, given the specifics of SCH’s setting and work processes. As described earlier, SCH is a large academic pediatric hospital with 3 surgical locations, which creates complexity regarding communication and check-in location.
From a sustainability standpoint, the QI team transferred project oversight to perioperative nurse managers after the project was complete. The QI team continues to review data quarterly for quality assurance and collaborates with the operational owners on suggested improvement opportunities to maintain gains and prevent regression to baseline.
In conclusion, multiple PDSA cycles conducted around inpatient-to-OR readiness led to >9% improvement in patient flow, enhanced communication between groups, increased awareness of preoperative preparation tasks, and improved trust in OR readiness signals to the preoperative area. Further study is needed to determine whether similar interventions would be effective at improving efficiency at other hospitals.
ACKNOWLEDGMENTS
The authors acknowledge the QI team members who participated in this project, including the authors as well as Paul Merguerian, Suzanne Yandow, Evelyn Hwang, Jennifer Reebel, Nina Scharpp, Amanda Breedt, Christina Schwien, Katie Miller, Pearl Bautista, and Alejandra Cuevas Vega. In addition to the many perioperative staff at SCH who participated in this improvement work, the authors extend special thanks to Paul Foster, Colin Bowles, Dwight Barry, and Paul Sharek. These individuals supported the project through SCH QI leadership, Epic EHR support, acute care nurse communication coordination, and final paper figure preparation/data analyses.
Supplementary Material
Footnotes
Published online July 29, 2026.
Supplemental digital content is available for this article. Clickable URL citations appear in the text.
Disclosure: The authors have no financial interest to declare in relation to the content of this article.
Preliminary data were presented as posters at 3 nursing conferences: the American Society of Perianesthesia Nurses National Conference, April 28–May 1, 2025, Dallas, TX;21 the Seattle Nursing Research Consortium Annual Conference, April 29, 2025, Seattle, WA; and the Association of Perioperative Registered Nurses Global Surgical Conference and Expo, April 11–14, 2026, New Orleans, LA.
There was no specific funding for this project beyond the hospital’s budget for QI programs.
To cite: Franz AM, Street CM, Crook CD, Christensen PL, Chen E, Walkup R, Raney EJ, Burnett LA, Martin LD. Maximizing Efficiency: A Quality Improvement Project to Improve Inpatient Flow to the Operating Room. Pediatr Qual Saf 2026;11:e904.
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