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Resilient Performance in Acute Health Care: Implementation of an Intervention across Care Boundaries
Published in Jeffrey Braithwaite, Erik Hollnagel, Garth S Hunte, Working Across Boundaries, 2019
Robyn Clay-Williams, Brette Blakely, Paul Lane, Siva Senthuran, Andrew Johnson
By establishing rules for decision-making around ICU bed allocation, the intervention improved internal professional relationships within the ICU and between the ICU and external departments. The reduced rate of elective surgery cancellations that were found to follow the intervention reflects a more resilient system. It is interesting that offering extra ICU capacity to the surgeons when the ICU was in a state of GREEN, while not practically useful (due to the lead time involved in scheduling additional surgical patients), accumulated a store of goodwill on a social level. This store of goodwill appeared to strengthen system resilience by providing ‘boundary spanners’ with resources to draw on to provide a better foundation for conversations in the event that pressure increases.
Human Skin Xenografts to Athymic Rodents as a System to Study Toxins Delivered to or Through Skin
Published in Rhoda G. M. Wang, James B. Knaak, Howard I. Maibach, Health Risk Assessment, 2017
Gerald G. Krueger, Lynn K. Pershing
The greatest experience in transplanting human skin to congenitally athymic rodents has been with remnants of skin from elective surgery, abdominoplasties, face lifts, breast reduction, etc. Remnants are trimmed to a thickness similar to the skin of the mouse, approximately 300 to 500 μm, and transplanted to the lateral thoracic cage. At this thickness, adnexal structures are lost as engraftment ensues; however, an occasional hair follicle will persist and hair growth will be noted.5,17
Estimation and propagation on a given CEG
Published in Rodrigo A. Collazo, Christiane Görgen, Jim Q. Smith, Chain Event Graphs, 2018
Rodrigo A. Collazo, Christiane Görgen, Jim Q. Smith
We now make a number of assumptions. First that patients with minor or serious disorders have the same probability of responding to the clinical treatment. Second that the result of a semi‐elective surgery has the same probability distribution for all patients. Third that patients with non‐minor disorders have the same rate of survival along the clinical treatment and the emergency surgery. The positions in the staged tree are then WT=w0=v0,w1=v1,w2=v2,w3=v2w4=v4,v5,v7,w5=v6,v8,w6=v9,v10. $$ {W_T} = \left\{ {{w_0} = \left\{ {{v_0}} \right\},{w_1} = \left\{ {{v_1}} \right\},{w_2} = \left\{ {{v_2}} \right\},{w_3} = \left\{ {{v_2}} \right\}} \right.{\rm{ }}{\mkern 1mu} {\mkern 1mu} {\mkern 1mu} {\mkern 1mu} {\mkern 1mu} {\mkern 1mu} {\mkern 1mu} {w_4} = \left\{ {{v_4},~{v_5},~{v_7}} \right\},~{w_5} = \left\{ {{v_6},~{v_8}} \right\},~{w_6} = \left. {\left\{ {{v_9},~{v_{10}}} \right\}} \right\}.{\rm{ }} $$
A prediction-optimization approach to surgery prioritization in operating room scheduling
Published in Journal of Industrial and Production Engineering, 2022
Abdulaziz Ahmed, Lu He, Chun-an Chou, Mohammad M. Hamasha
The novelty of this work comes from integrating both mathematical modeling and machine learning to schedule surgeries. In most existing operation situations, both surgery selection and scheduling decisions are made by senior nurses or nurse managers manually based on empirical evidence. Such scheduling methods are time- and resource-consuming, and not efficient. The contributions of this study are as follows: 1) We propose an OR scheduling strategy by integrating machine learning and mathematical optimization; 2) The surgery schedule is performed in two decision levels: Level 1) Decide elective surgery selection criteria (i.e. surgery priority); Level 2): Generate surgery sequence considering both surgery priorities and OR constraints; 3) Extensive experiments are conducted to show the effectiveness of the proposed decision models; 4) A sensitivity analysis is conducted to explain the effects of different parameters on surgery scheduling including the costs of overtime, OR setup cancellation.
Inpatient discharge planning under uncertainty
Published in IISE Transactions, 2022
Maryam Khatami, Michelle Alvarado, Nan Kong, Pratik J. Parikh, Mark A. Lawley
A few research papers in the operations research literature focus on the importance of discharge strategies in hospital units. Dobson et al. (2010) develop a Markov chain model to reduce ICU overcrowding by discharging patients with the smallest remaining length of stay. The authors also investigate the impact of elective surgery schedules on ICU performance. Chan et al. (2012) investigate the impact of ICU discharge strategies on readmission rate and mortality. They use dynamic programming to find an index policy, which is proven to be optimal under some conditions and near-optimal otherwise. The policy assists ICU physicians in deciding on discharging a relatively stable patient to admit a new one. Shi et al. (2015) propose a stochastic network model for inpatient flow management to minimize ED boarding. Using simulation studies, the authors use their model to evaluate the impact of operational policies on ED boarding. The policies are increasing bed capacity, limiting the length of stay, and reducing allocation delays caused by reasons other than bed unavailability.
Operating room scheduling problem under uncertainty: Application of continuous phase-type distributions
Published in IISE Transactions, 2020
Mohsen Varmazyar, Raha Akhavan-Tabatabaei, Nasser Salmasi, Mohammad Modarres
The OTR scheduling problem has been intensively studied in the literature from the decision scientists’ perspective (Cardoen et al., 2010a; Guerriero and Guido, 2011; Ferrand et al., 2014; Abe et al., 2016). Dexter et al. (1999) used on-line and off-line bin-packing techniques to plan elective patients and evaluated their performances using the lognormal distribution in a simulation model. Lamiri et al. (2007) addressed the planning of elective surgery under uncertain surgery times, estimated by a lognormal distribution. They first modeled the planning problem as a stochastic integer program and then used Monte Carlo simulation to approximate the problem by a mixed-integer program. They assigned elective patients to a particular OR in a particular period by a column-oriented formulation, and solved the linear relaxation of the latter formulation via column generation. The duration of surgeries on the basis of a number of different hazard models was predicted by Joustra et al. (2013) to plan the surgeries in the Academic Medical Center. They used several hazard models to compare the results with the predictions provided by surgeons. Bruni et al. (2015) suggested a comprehensive stochastic programming modeling framework, which handles the inherent uncertainty characterizing the arrival of emergency patients and the duration of surgery. In their research, surgery durations were sampled from lognormal distributions. The reviewed literature shows that fitting with popular distributions, especially normal and lognormal, has been extensively studied in the solution of the OTR problem.