The Association Between Ambulance Hospital Turnaround Times and Patient Acuity, Destination Hospital, and Time of Day

Vandeventer, Steve; Studnek, Jonathan R.; Garrett, John S.; Ward, Steven R.; Staley, Kevin; Blackwell, Tom

doi:10.3109/10903127.2011.561412

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…, Sinnema ) and elsewhere (Vandeventer et al. ). Some hospitals and health authorities have responded with protocols for expediting patients when emergency department congestion reaches certain thresholds (Alberta Health Services ) and such protocols might explain the irregular pattern for average hospital time when the number of busy ambulances is large.…”

Section: Ems System With Repositioningmentioning

confidence: 99%

A Markov Chain Model for an EMS System with Repositioning

Alanis

Ingolfsson

Kolfal

2013

Production and Operations Management

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W e propose and analyze a two-dimensional Markov chain model of an Emergency Medical Services system that repositions ambulances using a compliance table policy, which is commonly used in practice. The model is solved via a fixed-point iteration. We validate the model against a detailed simulation model for several scenarios. We demonstrate that the model provides accurate approximations to various system performance measures, such as the response time distribution and the distribution of the number of busy ambulances, and that it can be used to identify near-optimal compliance tables. Our numerical results show that performance depends strongly on the compliance table that is used, indicating the importance of choosing a well-designed compliance table.

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Section: Ems System With Repositioningmentioning

confidence: 99%

A Markov Chain Model for an EMS System with Repositioning

Alanis

Ingolfsson

Kolfal

2013

Production and Operations Management

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“…Actual historical data on patient arrival rate and traffic was obtained from the South Korean Open Data Portal (data.go.kr) and the Seoul Traffic Information Center, respectively. These data reveal an average of 127.9 calls per day, of which 24.8% are assumed to be high risk [32] The area contains three hospitals with emergency rooms, six ambulances, and six fire stations that function as waiting locations (Figure 5). Actual data on the time-varying demand and changes in ambulance speed over time were also used in the model.…”

Section: Numerical Experiments and Resultsmentioning

confidence: 99%

“…Moreover, the ratio of actual high-risk patients to all patients (Pr H A ) is assumed to be known from the historical data. In this study, Pr H A = 24.8%, according to a survey by Vandeventer et al [32]. Therefore, if α , β , and Pr H A are known, we can calculate the probability of a patient being correctly classified as high risk (Pr H A |H C ) and vice versa (Pr L A |L C ):PrHAHC=1−αPrHA1−αPrHA+β1−PrHA,PrLALC=1−β1−PrHAα·PrHA+1−β1−PrHA.…”

Section: Problem Descriptionmentioning

confidence: 99%

Two-Tiered Ambulance Dispatch and Redeployment considering Patient Severity Classification Errors

Park

Lee

2019

Journal of Healthcare Engineering

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A two-tiered ambulance system, consisting of advanced and basic life support for emergency and nonemergency patient care, respectively, can provide a cost-efficient emergency medical service. However, such a system requires accurate classification of patient severity to avoid complications. Thus, this study considers a two-tiered ambulance dispatch and redeployment problem in which the average patient severity classification errors are known. This study builds on previous research into the ambulance dispatch and redeployment problem by additionally considering multiple types of patients and ambulances, and patient classiﬁcation errors. We formulate this dynamic decision-making problem as a semi-Markov decision process and propose a mini-batch monotone-approximate dynamic programming (ADP) algorithm to solve the problem within a reasonable computation time. Computational experiments using realistic system dynamics based on historical data from Seoul reveal that the proposed approach and algorithm reduce the risk level index (RLI) for all patients by an average of 11.2% compared to the greedy policy. In this numerical study, we identify the influence of certain system parameters such as the percentage of advanced-life support units among all ambulances and patient classiﬁcation errors. A key finding is that an increase in undertriage rates has a greater negative effect on patient RLI than an increase in overtriage rates. The proposed algorithm delivers an efficient two-tiered ambulance management strategy. Furthermore, our findings could provide useful guidelines for practitioners, enabling them to classify patient severity in order to minimize undertriage rates.

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“…Previous investigations have identified that day of the week, time of day, and location are associated with utilization of emergency services (14). Additional information on factors impacting EMS demand could inform EMS administrators in ways to better optimize staffing levels to meet the peaks and troughs of EMS demand (15).…”

Section: Introductionmentioning

confidence: 99%

Weather and Temporal Factors Associated with Use of Emergency Medical Services

Ramgopal¹,

Dunnick²,

Owusu‐Ansah³

et al. 2019

Prehospital Emergency Care

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Background: Fluctuations in emergency medical services (EMS) responses can have a substantial impact on the ability of agencies to meet resource needs within an EMS system. We aimed to identify weather characteristics as potentially predictable factors associated with EMS responses. Methods: We reviewed hourly counts of scene responses documented by 24 EMS agencies in Western Pennsylvania from January 1, 2014 to December 31, 2017 and compared rates of responses to weather characteristics. Responses to counties nonadjacent to the studied weather reporting station and interfacility/scheduled transports were excluded. We identified the mean temperature, meters visibility, dew point, wind speed, total millimeters of precipitation, and presence of rain or snow in 6-hour windows prior to dispatch, in addition to temporal factors of time of day and weekend vs. weekday. Analysis was performed using multivariable linear regression of a negative binomial distribution, reporting incidence rate ratios (IRR) with 95% confidence intervals (CI). Secondary analyses were performed for transports to the hospital and cases involving transports for traumatic complaints and pediatric patients (age <18 years). Results: We included 529,058 responses (54.8% female, mean age 57.2 ± SD 24.7 years). In our multivariable model, responses were associated with (IRR, 95% CI) rain (1.10, 1.08-1.11) snow (1.07, 1.05-1.09), and both rain and snow (1.15, 1.11-1.19). A lower incidence of responses occurred on weekends (0.84, 0.83-0.85) and at night (0.62,0.61-0.62). Increasing temperature in 5 °C increments was associated with an increase in responses across seasons with an effect that varied between 1.16 (1.15-1.17) in winter to 1.31 (1.28-1.33) in summer. Windy weather was associated with increased responses from light breeze (1.10, 1.09-1.11) to fresh breeze or greater (1.23, 1.16-1.30). Transports occurred in a similar pattern to responses. Trauma transports

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The Association Between Ambulance Hospital Turnaround Times and Patient Acuity, Destination Hospital, and Time of Day

Cited by 25 publications

References 13 publications

A Markov Chain Model for an EMS System with Repositioning

A Markov Chain Model for an EMS System with Repositioning

Two-Tiered Ambulance Dispatch and Redeployment considering Patient Severity Classification Errors

Weather and Temporal Factors Associated with Use of Emergency Medical Services

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