Forecasting the effect of physician assistants in a pediatric ED

Doan, Quynh; Hall, Wendy; Shechter, Steven M.; Kissoon, Niranjan; Sheps, Sam; Singer, Joel; Wong, Hubert; Johnson, David W.

doi:10.1097/01.jaa.0000451860.95151.e1

Cited by 10 publications

(20 citation statements)

References 29 publications

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“…Regression modeling of ED waiting times has several purposes, including predicting ED time to first physician contact or overall LOS [7, 8], identifying relevant factors which are potentially modifiable [4], developing a forecasting model to simulate effects of interventions [17, 18], and for evaluating an actual intervention based on observational data [19–21]. While our presentation focused on the first two of these aspects, the multistate model developed in this work is amenable to the latter two as well.…”

Section: Discussionmentioning

confidence: 99%

“…While our presentation focused on the first two of these aspects, the multistate model developed in this work is amenable to the latter two as well. Our conditional transition probability estimates from the multistate model can form the basis of a discrete event simulation model to forecast how changes in staffing and other hospital-level factors would affect transition probabilities between the states in the model [18]. The flow into the model could by varied to account for spikes and dips in patient influx and to evaluate limitations on the system.…”

Section: Discussionmentioning

confidence: 99%

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Multistate model of the patient flow process in the pediatric emergency department

et al. 2019

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Objectives The main purpose of this paper was to model the process by which patients enter the ED, are seen by physicians, and discharged from the Emergency Department at Nationwide Children’s Hospital, as well as identify modifiable factors that are associated with ED lengths of stay through use of multistate modeling. Methods In this study, 75,591 patients admitted to the ED from March 1 st , 2016 to February 28 th , 2017 were analyzed using a multistate model of the ED process. Cox proportional hazards models with transition-specific covariates were used to model each transition in the multistate model and the Aalen-Johansen estimator was used to obtain transition probabilities and state occupation probabilities in the ED process. Results Acuity level, season, time of day and number of ED physicians had significant and varying associations with the six transitions in the multistate model. Race and ethnicity were significantly associated with transition to left without being seen, but not with the other transitions. Conversely, age and gender were significantly associated with registration to room and subsequent transitions in the model, though the magnitude of association was not strong. Conclusions The multistate model presented in this paper decomposes the overall ED length of stay into constituent transitions for modeling covariate-specific effects on each transition. This allows physicians to understand the ED process and identify which potentially modifiable covariates would have the greatest impact on reducing the waiting times in each state in the model.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multistate model of the patient flow process in the pediatric emergency department

et al. 2019

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“…Once verified, we simulated five iterations of 1 year of patients flowing through the model in order to generate the average WT, LOS and patient volume by ESI, a previously described method of discrete event simulation model validation for these output metrics 9 14. We defined our principal patient flow metrics of WT, as the time from the completion of the nurse screening process until that patient was seen by a physician for a full evaluation and LOS, as the time from the completion of the nurse screening process until that patient departed the ED.…”

Section: Methodsmentioning

confidence: 99%

“…This methodology can be used to generate quantitative performance reports for a real system as well as test theoretical system changes, including those to support QI efforts 10 11. While discrete event simulation models can serve to evaluate ongoing improvement efforts, they have also demonstrated predictive validity in both adult and paediatric EDs 12–15…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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ObjectiveWe 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.MethodsThe 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.ResultsWe 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.ConclusionsSimulation 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.

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“…According to some estimates, up to 10% of the PA workforce is employed in the ED 1,2. The National Hospital Ambulatory Medical Care Survey of 2016 revealed that one in six ED visits involve a PA, compared with one in 12 in 2008 3-5. PAs may be part of the solution for throughput delays; with skills and scope of practice well suited for the ED, the trend of increased PA staffing in EDs is anticipated to continue 4.…”

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confidence: 99%

Paired analysis of ED efficiency for low-acuity children treated by PAs and pediatricians

McKinley¹,

Tran²,

Chamberlain³

et al. 2023

Jaapa

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Objective:To determine if there was a clinically important difference (15 minutes or more) in length of stay (LOS) for low-acuity pediatric ED patients treated by PAs compared with those treated by pediatricians.Methods:Between July 2017 and February 2020, shifts were identified that had shared PA and pediatrician staffing in the low-acuity care area for a large, urban ED. LOS was collected for every patient during the 6 hours of overlap for each shift. Using a paired analysis, we calculated the difference in mean LOS for these shifts.Results:Mean shift LOS for children seen by PAs (160.1 minutes, SD: 48.6) was 10.1 minutes longer (95% CI: 6.1, 14.1) than mean shift LOS for children seen by pediatricians (150 minutes, SD: 47.3).Conclusions:No clinically important difference in LOS was found for low-acuity children treated by PAs compared with those treated by pediatricians in a large, urban ED.

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Forecasting the effect of physician assistants in a pediatric ED

Abstract: With restricted autonomy, PAs mainly benefitted the high-acuity patients. Increasing the level of PA autonomy was critical in broadening the effect of PAs to all acuity levels.

Cited by 10 publications

References 29 publications

Multistate model of the patient flow process in the pediatric emergency department

Multistate model of the patient flow process in the pediatric emergency department

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

Paired analysis of ED efficiency for low-acuity children treated by PAs and pediatricians

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