Reducing postponements of elective pediatric cardiac procedures: analysis and implementation of a discrete event simulation model

Impact of Universal Suicide Risk Screening in a Pediatric Emergency Department: A Discrete Event Simulation Approach

Objectives

The aim of this study was to use discrete event simulation (DES) to model the impact of two universal suicide risk screening scenarios (emergency department [ED] and hospital-wide) on mean length of stay (LOS), wait times, and overflow of our secure patient care unit for patients being evaluated for a behavioral health complaint (BHC) in the ED of a large, academic children’s hospital.

Methods

We developed a conceptual model of BHC patient flow through the ED, incorporating anticipated system changes with both universal suicide risk screening scenarios. Retrospective site-specific patient tracking data from 2017 were used to generate model parameters and validate model output metrics with a random 50/50 split for derivation and validation data.

Results

The model predicted small increases (less than 1 hour) in LOS and wait times for our BHC patients in both universal screening scenarios. However, the days per year in which the ED experienced secure unit overflow increased (existing system: 52.9 days; 95% CI, 51.5–54.3 days; ED: 94.4 days; 95% CI, 92.6–96.2 days; and hospital-wide: 276.9 days; 95% CI, 274.8–279.0 days).

Conclusions

The DES model predicted that implementation of either universal suicide risk screening scenario would not severely impact LOS or wait times for BHC patients in our ED. However, universal screening would greatly stress our existing ED capacity to care for BHC patients in secure, dedicated patient areas by creating more overflow.

Reducing Pediatric ED Length of Stay by Reducing Diagnostic Testing: A Discrete Event Simulation Model

Supplemental Digital Content is available in the text.

Quality improvement efforts can require significant investment before the system impact of those efforts can be evaluated. We used discrete event simulation (DES) modeling to test the theoretical impact of a proposed initiative to reduce diagnostic testing for low-acuity pediatric emergency department (ED) patients.

Discrete event simulation modelling to evaluate the impact of a quality improvement initiative on patient flow in a paediatric emergency department

OBJECTIVE

We developed a discrete event simulation model to evaluate the impact on system flow of a quality improvement (QI) initiative that included a time-specific protocol to decrease the time to antibiotic delivery for children with cancer and central venous catheters who present to a paediatric ED with fever.

METHODS

The model was based on prospective observations and retrospective review of ED processes during the maintenance phase of the QI initiative between January 2016 and June 2017 in a large, urban, academic children's hospital in New York City, USA. We compared waiting time for full evaluation (WT) and length of stay (LOS) between a model with and a model without the protocol. We then gradually increased the proportion of patients receiving the protocol in the model and recorded changes in WT and LOS.

RESULTS

We validated model outputs against administrative data from 2016, with no statistically significant differences in average WT or LOS for any emergency severity index (ESI). There were no statistically significant differences in these flow metrics between the model with and the model without the protocol. By increasing the proportion of total patients receiving this protocol, from 0.2% to 1.3%, the WT increased by 2.8 min (95% CI: 0.6 to 5.0) and 7.6 min (95% CI: 2.0 to 13.2) for ESI 2 and ESI 3 patients, respectively. This represents a 14.0% increase in WT for ESI 3 patients.

CONCLUSIONS

Simulation modelling facilitated the testing of system effects for a time-specific protocol implemented in a large, urban, academic paediatric ED, showing no significant impact on patient flow. The model suggests system resilience, demonstrating no detrimental effect on WT until there is a 7-fold increase in the proportion of patients receiving the protocol.

Simulation to Predict Effect of Citywide Events on Emergency Department Operations

Medical emergency preparedness has been an issue of medical relevance since the advent of hospital care. Studies have simulated emergency department (ED) overcrowding but not yet characterized effects of large-scale, planned events that drastically alter a city's demography, such as in Philadelphia, Pennsylvania during the 2015 World Meeting of Families. A discrete event simulation of the ED at the Children's Hospital of Philadelphia was designed and validated using past data. The model was used to predict the patient length of stay (LOS) and number of admitted patients if the arrival stream to the ED were to change by 50% from typical arrivals in either direction. We compared the model's estimations with data produced during the papal visit that had 39.65% fewer patient arrivals. For validation, the simulated mean LOS was 226.1 ± 173.3 minutes (mean ± SD) for all patients and 352.1 ± 170.3 minutes for admitted patients. Real-world mean LOSs for the fiscal year 2014 were 230.6 ± 134.8 for all patients and 345.0 ± 147.7 for admitted patients. For the estimation of the World Meeting of Families, the simulation accurately estimated the LOS of both patients overall and admitted patients within 10%. These results show that it is possible to use simulations to project the patient flow effects in EDs in case of large-scale events. Providing efficient care is essential to emergency operations, and projections of demand are crucial for targeting appropriate changes during large-scale events. Analysis of validated computer simulations allows for evidence-based decision making in a complex clinical environment.

Leveraging Health Care Simulation Technology for Human Factors Research: Closing the Gap Between Lab and Bedside

OBJECTIVE

We describe health care simulation, designed primarily for training, and provide examples of how human factors experts can collaborate with health care professionals and simulationists-experts in the design and implementation of simulation-to use contemporary simulation to improve health care delivery.

BACKGROUND

The need-and the opportunity-to apply human factors expertise in efforts to achieve improved health outcomes has never been greater. Health care is a complex adaptive system, and simulation is an effective and flexible tool that can be used by human factors experts to better understand and improve individual, team, and system performance within health care.

METHOD

Expert opinion is presented, based on a panel delivered during the 2014 Human Factors and Ergonomics Society Health Care Symposium.

RESULTS

Diverse simulators, physically or virtually representing humans or human organs, and simulation applications in education, research, and systems analysis that may be of use to human factors experts are presented. Examples of simulation designed to improve individual, team, and system performance are provided, as are applications in computational modeling, research, and lifelong learning.

CONCLUSION

The adoption or adaptation of current and future training and assessment simulation technologies and facilities provides opportunities for human factors research and engineering, with benefits for health care safety, quality, resilience, and efficiency.

APPLICATION

Human factors experts, health care providers, and simulationists can use contemporary simulation equipment and techniques to study and improve health care delivery.

Managing Disruptions to Patient Flow Capacity: Rapid-Cycle Improvement in a Pediatric Cardiac Procedure Complex

BACKGROUND

Managing service disruptions is a challenge in every health care environment. Discrete event simulation (DES)--a computer modeling tool used to build in silico (that is, in a digital computer) testbeds for potential changes in complex systems--has been deployed in health care for research and quality improvement (QI), specifically in surgical suite management. A strategy for managing a 6-week planned service disruption needed to be enacted 12 weeks after the announcement, in late October 2014, of the closure of the Hybrid Suite (operating room/catheterization laboratory) for renovation, at The Children's Hospital of Philadelphia's Cardiac Center's Cardiac Operative and Imaging Complex (COIC).

METHODS

A previously developed DES was queried to determine theoretical system throughput capacity during the temporary disruption. On the basis of this analysis, a rapid improvement event (RIE) was enacted to address systemic challenges to meeting demand with diminished capacity. During the RIE, system stakeholders (physicians, nurses, and technicians) engaged with performance improvement personnel to identify potential improvements, test those changes in rapid succession, and then implement successful candidates for the disruption.

RESULTS

First-case start time was 43 minutes earlier during the period of diminished capacity. Turnaround time between cases was reduced by 23 minutes. Length of day increased by 1 hour, in accordance with simulated predictions. System throughput was 138 patients during the disruption, compared with 135 patients during the same period the previous year.

CONCLUSION

A combination of systems analysis and QI methodologies enabled the Cardiac Center to meet demand during a six-week period of diminished capacity. Planned, temporary service disruptions, which must be managed by clinical personnel, can be addressed proactively with promising results.