skip to main content


Title: An Approach to Optimize a Regional Trauma Network
Trauma continues to be the leading cause of mortality and morbidity among US citizens aged <44 years. Literature suggests that geographical maldistribution of trauma centers (TCs) is associated with increasing fatality rate. Existing models for TC network design do not address the question often raised by trauma decision makers: how many TCs are required to achieve acceptable levels of mistriages? We propose a model to optimize the network of TCs under mistriage constraints. We propose a notional field triage protocol to estimate mistriages (under and over), based on existing guidelines in the trauma literature. Due to the complexity of the underlying model, we propose a Particle Swarm Optimization based solution approach. We use 2012 data from the State of Ohio, and model both ground and air transportation modes. Our results show that, for 2012 mistriage levels, it is possible to reduce the number of TCs from 21 to 10 by distributing them appropriately across urban and rural areas. Further, redistributing these 21 TCs can help satisfy the recommendation of under-triage ≤0.05 by the American College of Surgeons. In general, our study provides trauma decision makers an ability to determine a network that could improve care and/or reduce cost.  more » « less
Award ID(s):
1761022
NSF-PAR ID:
10129667
Author(s) / Creator(s):
Date Published:
Journal Name:
2019 Industrial and Systems Engineering Research Conference
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Trauma continues to be the leading cause of mortality and morbidity among US citizens aged <44 years. Literature suggests that geographical maldistribution of trauma centers (TCs) is associated with increasing fatality rate. Existing models for TC network design do not address the question often raised by trauma decision makers: how many TCs are required to achieve acceptable levels of mistriages? We propose a model to optimize the network of TCs under mistriage constraints. We propose a notional field triage protocol to estimate mistriages (under and over), based on existing guidelines in the trauma literature. Due to the complexity of the underlying model, we propose a Particle Swarm Optimization based solution approach. We use 2012 data from the State of Ohio, and model both ground and air transportation modes. Our results show that, for 2012 mistriage levels, it is possible to reduce the number of TCs from 21 to 10 by distributing them appropriately across urban and rural areas. Further, redistributing these 21 TCs can help satisfy the recommendation of under-triage ≤0.05 by the American College of Surgeons. In general, our study provides trauma decision makers an ability to determine a network that could improve care and/or reduce cost. 
    more » « less
  2. Objectives

    There are no widely accepted metrics to determine the optimal number and geographic distribution of trauma centers (TCs). We propose a Performance-based Assessment of Trauma System (PBATS) model to optimize the number and distribution of TCs in a region using key performance metrics.

    Methods

    The proposed PBATS approach relies on well-established mathematical programming approach to minimize the number of level I (LI) and level II (LII) TCs required in a region, constrained by prespecified system-related under-triage (srUT) and over-triage (srOT) rates and TC volume. To illustrate PBATS, we collected 6002 matched (linked) records from the 2012 Ohio Trauma and EMS registries. The PBATS-suggested network was compared to the 2012 Ohio network and also to the configuration proposed by the Needs-Based Assessment of Trauma System (NBATS) tool.

    Results

    For this data, PBATS suggested 14 LI/II TCs with a slightly different geographic distribution compared to the 2012 network with 21 LI and LII TC, for the same srUT≈.2 and srOT≈.52. To achieve UT ≤ .05, PBATS suggested 23 LI/II TCs with a significantly different distribution. The NBATS suggested fewer TCs (12 LI/II) than the Ohio 2012 network.

    Conclusion

    The PBATS approach can generate a geographically optimized network of TCs to achieve prespecified performance characteristics such as srUT rate, srOT rate, and TC volume. Such a solution may provide a useful data-driven standard, which can be used to drive incremental system changes and guide policy decisions.

     
    more » « less
  3. Abstract

    During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

     
    more » « less
  4. Knowledge quality assessment (KQA) has been developed in order to analyze the role of knowledge in situations of high stakes and urgency when characterized by deep uncertainty and ignorance. Governing coastal flood risk in the face of climate change is typical of such situations. These are situations which limit the ability to establish objective, reliable, and valid facts. This paper aims to identify the moral frameworks that stakeholders use to judge flood risk situations under climate change, and infer from these knowledge legitimacy criteria. Knowledge legitimacy, defined as being respectful of stakeholders' divergent values and beliefs, is one of the three broad quality criteria that have been proposed in order to assess knowledge quality in such situations; credibility (as scientific adequacy) and salience (relevance to the needs of decision makers) being the two others. Knowledge legitimacy is essentially the subject of a literature analyzing, ex-post (once knowledge has been deployed), how stakeholders' participation is a factor contributing to knowledge legitimacy. Very little is known about ex-ante characteristics (i.e.: that can be observed, determined, before knowledge is deployed) that would make some types of knowledge more legitimate (i.e., respectful of stakeholders' divergent values and beliefs) than others. We see this as a significant blind spot in the analysis of knowledge and its role under deep uncertainty. In this paper we posit that this blind spot may be addressed, in part. In order to achieve this we first identify the ethical frameworks that stakeholders use to judge a situation of risk under rapidly changing conditions. We then associate these ethical frameworks to characteristics of knowledge. We tested this conceptualization through a case study approach centered on flood risk on the French Atlantic coast. We have adopted a narrative approach to the analysis of two diachronic corpora consisting of interviews conducted in 2010–2012 (33 interviews) and 2020 (15 interviews). These were approached as narratives of a risk situation. We thematically coded these along themes considered as metanarratives. These metanarratives are associated with predefined (deontology, consequentialism, virtue ethics) and emerging (discourse ethics, connection ethics, and a naturalistic ethic) ethical theories. Our results show that, when faced with flood risks, stakeholders tell stories that mobilizes several metaethical frameworks as guiding principles in the form of both procedural and substantive injunctions. In order to respect what we interpret as manifestations of the moral stances of stakeholders, our results indicate that knowledge legitimacy may be assessed against the following criteria: lability, debatability and adaptability; degree of co-production invested; place-based approach; ability to include lessons that would be given by nature. The operationalization of these criteria is promising in a time when the knowledge that is used for decision making under certainty is increasingly contested on the ground of its legitimacy. 
    more » « less
  5. Abstract

    Tropical cyclones (TCs) are one of the greatest threats to coastal communities along the US Atlantic and Gulf coasts due to their extreme wind, rainfall and storm surge. Analyzing historical TC climatology and modeling TC hazards can provide valuable insight to planners and decision makers. However, detailed TC size information is typically only available from 1988 onward, preventing accurate wind, rainfall, and storm surge modeling for TCs occurring earlier in the historical record. To overcome temporally limited TC size data, we develop a database of size estimates that are based on reanalysis data and a physics‐based model. Specifically, we utilize ERA5 reanalysis data to estimate the TC outer size, and a physics‐based TC wind model to estimate the radius of maximum wind. We evaluate our TC size estimates using two high‐resolution wind data sets as well as Best Track information for a wide variety of TCs. Using the estimated size information plus the TC track and intensity, we reconstruct historical storm tides from 1950 to 2020 using a basin‐scale hydrodynamic model and show that our reconstructions agree well with observed peak storm tide and storm surge. Finally, we demonstrate that incorporating an expanded set of historical modeled storm tides beginning in 1950 can enhance our understanding of US coastal hazard. Our newly developed database of TC sizes and associated storm tides/surges can aid in understanding North Atlantic TC climatology and modeling TC wind, storm surge, and rainfall hazard along the US Atlantic and Gulf coasts.

     
    more » « less