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1. An Approach to Optimize a Regional Trauma Network

   Vaishnav, M. (May 2019, 2019 Industrial and Systems Engineering Research Conference)

   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. 

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2. Nested trauma network design considering equity and effectiveness in patient safety

   https://doi.org/10.1016/j.cie.2023.109250  

   Hirpara, Sagarkumar; Parikh, Pratik J; Kong, Nan (July 2023, Computers & Industrial Engineering)

   Trauma injuries continue to be the leading cause of mortality and morbidity among US citizens aged 44 years and under. Government agencies are often in charge of designing an effective trauma network in their region to provide prompt and definitive care to their citizens. This process is, however, largely manual, experience-based and often leads to a suboptimal network in terms of patient safety. To support effective decision making, we propose a Nested Trauma Network Design Problem (NTNDP), which can be characterized as a nested multi-level, multi-customer, multi-transportation, multi-criteria, capacitated model with the bi-objective of maximizing the weighted sum of equity and effectiveness in patient safety. We use mistriages (system-related under- and over-triages) as surrogates for patient safety. To add realism, we include intermediate trauma centers that are set up in many states in the US to serve as feeder centers to major trauma centers to improve patient safety and three criteria to mimic EMS’s on-scene decisions. We propose a ‘3-phase’ solution approach that first solves a relaxed version of the model, then solves a Constraint Satisfaction Problem, and then a modified version of the original optimization problem (if needed), all using a commercial solver. Our findings suggest that solutions are sensitive to (i) the proportion of assignments attributed to various destination determination criteria, (ii) distribution of trauma patients, and (iii) relative emphasis on equity vs. effectiveness. We also illustrate the use of our approach using real data from a midwestern US state; results show over 30% performance improvement in the objective value. 

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3. Planning the Trauma Hospital Network Expansion to Increase Access in Rural Communities

   Holly, Streber; Streber, Kayla; Van, Mary; Pérez, Eduardo (April 2022, Proceedings of the IISE Annual Conference & Expo 2022)

   Ellis, K.; Ferrell, W.; Knapp J. (Ed.)

   Trauma care services are a vital part of all healthcare-based networks as timely accessibility is important for citizens. Trauma care access is even more relevant when unexpected events such as the COVID-19 pandemic overload the capacity of hospitals. Research literature has highlighted that access to trauma care is not equal for all populations, especially when comparing rural and urban groups. In this research we present a decision-making model for the expansion of a trauma hospital network by considering the demand for services of rural communities. The decision making model provides recommendations in terms of where to place additional aeromedical facilities and where to locate additional trauma hospitals. A case study is presented for the state of Texas, where a sensitivity analysis was conducted to consider changes in demand, cost, and the total number of facilities allowed in the network. The results show that the location of new facilities is sensitive to the expected service demand and the maximum number of facilities allowed in the network. 

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4. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

   https://doi.org/10.1038/s41746-021-00453-0  

   Shamout, Farah E.; Shen, Yiqiu; Wu, Nan; Kaku, Aakash; Park, Jungkyu; Makino, Taro; Jastrzębski, Stanisław; Witowski, Jan; Wang, Duo; Zhang, Ben; et al (May 2021, npj Digital Medicine)

   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. 

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5. Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making

   https://doi.org/10.1145/3653708  

   Lu, Zhuoran; Wang, Dakuo; Yin, Ming (April 2024, Proceedings of the ACM on Human-Computer Interaction)

   AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore whether and how the provision of second opinions may affect decision-makers' behavior and performance in AI-assisted decision-making. We find that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI while increase their under-reliance on AI, regardless whether the second opinion is generated by a peer or another AI model. However, if decision-makers have the control to decide when to solicit a peer's second opinion, we find that their active solicitations of second opinions have the potential to mitigate over-reliance on AI without inducing increased under-reliance in some cases. We conclude by discussing the implications of our findings for promoting effective human-AI collaborations in decision-making. 

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