OR Practice—Efficient Short-Term Allocation and Reallocation of Patients to Floors of a Hospital During Demand Surges

Many hospitals face the problem of insufficient capacity to meet demand for inpatient beds, especially during demand surges. This results in quality degradation of patient care due to large delays from admission time to the hospital until arrival at a floor. In addition, there is loss of revenue because of the inability to provide service to potential patients. A solution to the problem is to proactively transfer patients between floors in anticipation of a demand surge. Optimal reallocation poses an extraordinarily complex problem that can be modeled as a finite-horizon Markov decision process. Based on the optimization model, a decision-support system has been developed and implemented at Windham Hospital in Willimantic, Connecticut. Projections from an initial trial period indicate very significant financial gains of about 1% of their total revenue, with no negative impact on any standard quality of care or staffing effectiveness indicators. In addition, the hospital showed a marked improvement in quality of care because of a resulting decrease of almost 50% in the average time that an admitted patient has to wait from admission until being transferred to a floor

Patient transfers in Australia: Implications for nursing workload and patient outcomes

Aim To discuss the impact of patient transfers on patient outcomes and nursing workload. Background Many patient transfers are essential and occur in response to patients' clinical changes. However, increasingly within Australia transfers are performed in response to reductions in bed numbers, resulting in 'bed block'. Evaluation A discussion of the literature related to inpatient transfers, nursing workload and patient safety. Key issues Measures to increase patient flow such as short-stay units may result in an increase in patient transfers and nursing workload. Frequent patient transfers may also increase the risk of medication incidents, health-care acquired infections and patient falls. Conclusions The continuing demand for health care has led to a reactionary bed management system that, in an attempt to accommodate patients, has resulted in increased transfers between wards. This can have a negative effect on nursing workload and affect patient outcomes. Implications for nursing management High nursing workload is cited as one reason for nurses leaving the profession. Reductions in non-essential transfers may reduce nurse workload, improve patient outcomes and enhance continuity of patient care. © 2011 Blackwell Publishing Ltd

Pandemic Recovery in Nursing: Labor Shortage and Financial Devastation

The COVID-19 pandemic inadvertently caused one of the greatest nursing labor shortages in history. Many healthcare organizations were forced to rely on external agency nurses to staff their organizations and care for large volumes of patients. The high demand for these nurses caused an increase in costs for external contracted labor. The costs of external agency use became financially devastating for healthcare organizations who had not budgeted for this expense and still the external agency use did not resolve the issue of nursing labor shortages. Learning and recovering from the labor and financial costs of the COVID-19 pandemic requires an internal resolution to long-term staffing needs. Implementation of an internal agency can resolve staffing needs within an organization without relying on contracted labor externally. An internal agency pool can be a catalyst to nursing retainment, decreasing nurse turnover and increasing nursing engagement within their organizations

Estimating the waiting time of multi-priority emergency patients with downstream blocking

To characterize the coupling effect between patient flow to access the emergency department (ED) and that to access the inpatient unit (IU), we develop a model with two connected queues: one upstream queue for the patient flow to access the ED and one downstream queue for the patient flow to access the IU. Building on this patient flow model, we employ queueing theory to estimate the average waiting time across patients. Using priority specific wait time targets, we further estimate the necessary number of ED and IU resources. Finally, we investigate how an alternative way of accessing ED (Fast Track) impacts the average waiting time of patients as well as the necessary number of ED/IU resources. This model as well as the analysis on patient flow can help the designer or manager of a hospital make decisions on the allocation of ED/IU resources in a hospital

Hospital-Wide Inpatient Flow Optimization

An ideal that supports quality and delivery of care is to have hospital operations that are coordinated and optimized across all services in real-time. As a step toward this goal, we propose a multistage adaptive robust optimization approach combined with machine learning techniques. Informed by data and predictions, our framework unifies the bed assignment process across the entire hospital and accounts for present and future inpatient flows, discharges as well as bed requests – from the emergency department, scheduled surgeries and admissions, and outside transfers. We evaluate our approach through simulations calibrated on historical data from a large academic medical center. For the 600-bed institution, our optimization model was solved in seconds, reduced off-service placement by 24% on average, and boarding delays in the emergency department and post-anesthesia units by 35% and 18% respectively. We also illustrate the benefit from using adaptive linear decision rules instead of static assignment decisions

Allocating commodity volumes in the citrus export cold chain: A case for the Port of Durban

Thesis (MComm)--Stellenbosch University, 2021.ENGLISH ABSTRACT: In this study, the feasibility of using “forced” allocation as a mechanism to aide in alleviating capacity challenges at the Port of Durban is explored and insights on the impact of reallocation to the citrus export cold chain is provided. The use of the mechanism is explored by limiting the allowable citrus throughput that may be handled at the Port of Durban for varying through put scenarios, and using allocation techniques to allocate the allowable citrus throughput amongst the competing production regions. An allocation model framework is formulated to optimally allocate the total citrus export volumes in a season to each of the South African ports that export citrus, taking into account the allowable port throughput constraint at the Port of Durban. The allocation model framework is modelled as a minimum cost transport problem and is solved using linear programming. The results of the 2019 actual export season for citrus exports is compared to the results of the 2019 forecasted export season to determine if there is a single suitable allocation technique that can be used to allocate the allowable port throughput to the production regions in the allocation model framework for future export seasons. The results show that there is no single suitable allocation technique, and so allocations on forecasted citrus export volumes must be done on a case-by-case basis. A possible export plan for the 2021 forecasted export season is calculated using the allocation model framework for each scenario to provide a baseline export plan for the different allowable throughput scenario’s at the Port of Durban. The forecasted citrus export volumes are forecasted using a four period double moving average forecasting model. The feasibility of using “forced” allocation as mechanism to alleviate capacity challenges faced at the Port of Durban is assessed on two criteria, namely the availability of theoretical excess capacity at the alternate ports to handle the citrus volumes reallocated and the change in total transport cost to the citrus export cold chain. The assessment of the criteria, and the analysis of the results, indicate that the use of “forced” allocation is feasible in the majority of, but not in all of the port throughput scenarios. Even though it is feasible in terms of the available capacity, there is, however, an increased transport cost to the citrus export cold chain in the majority of the scenario’s analysed. This additional transport cost must be weighed up against the cost of congestion and lost time, and will have to be absorbed by the citrus export cold chain. Eventhough there is an increase in transport cost, which can affect the total citrus export cold chain by as much as +35.2% (in the worst case scenario), the mechanism is deemed feasible as the impact of the increased transport cost is a relative measure that will have a varying impact amongst the different stakeholders of the citrus export cold chain and so each stakeholder will have to decide independently if it is feasible to them. The study achieved its primary aim of alleviating capacity pressures at the Port of Durban by reallocating citrus volumes to all South African ports that can handle citrus under different levels of available capacity at the Port of Durban. Therefore, “forced” allocation is deemed a good alternative solution to the current congested situation.AFRIKAANSE OPSOMMING: Hierdie studie ondersoek die lewensvatbaarheid van “geforseerde” toewysing as ’n meganisme om te help met die verligting van kapasiteitsuitdagings by Durban-hawe. Verder word insigte oor die impak van hertoewysing op die sitrus uitvoer-koue-ketting verskaf. Die gebruik van hierdie meganisme word ondersoek deur die toelaatbare sitrus deurvoer wat by Durban-hawe hanteer kan word vir verskillende deurvoer scenario’s te beperk, en gebruik te maak van toekenningstegnieke om die toelaatbare sitrusdeurvoer aan die mededingende produksiestreke toe te wys. ’n Toekenningsmodelraamwerk word geformuleer om die totale sitrusuitvoervolumes in ’n seisoen optimal aan elk van die Suid-Afrikaanse sitrus hawens toe te wys, met inagneming van die toelaatbare hawedeurvoerbeperking by Durban-hawe. Die toekenningsmodelraamwerk is gemodelleer as ’n minimum koste vervoerprobleem en word deur die gebruik van liniere programmering opgelos.Die resultate van die 2019 werklike uitvoerseisoen vir sitrusuitvoere word vergelyk met die resultate van die 2019 voorspelde uitvoerseisoen om te bepaal of daar ’n enkele geskikte toekenningstegniek is wat gebruik kan word om die toelaatbare hawedeurvoer aan die produksiestreke in die toekenningsmodelraamwerk vir toekomstige uitvoerseisoene toe te wys. Die resultate toon dat daar geen enkele geskikte toekenningstegniek is nie, dus moet toekennings op vooruitgeskatte sitrus uitvoervolumes op ’n geval-tot-geval grondslag gedoen word. ’n Moontlike uitvoerplan vir die 2021 vooruitgeskatte uitvoerseisoen word bereken deur gebruik te maak van die toeken-ningsmodelraamwerk vir elke scenario om ’n basislyn-uitvoerplan vir die verskillende toelaatbare deurvoerscenario’s by Durban-hawe te verskaf. Die sitrus uitvoervolumes word voorspel deur ’n vier-tydperk-dubbelbewegende-gemiddelde-vooruitskattingsmodel. Die haalbaarheid van die gebruik van “geforseerde” toewysing as meganisme om kapasiteitsuitdagings wat Durban-hawe in die gesig staar te verlig, word op twee kriteria geassesseer, naamlik: die beskikbaarheid van teoretiese oortollige kapasiteit by die alternatiewe hawens om die sitrusvolumes te hanteer, en die verandering in totale vervoerkoste aan die sitrusuitvoer-koue-ketting. Die assessering van die kriteria, en die ontleding van die resultate, dui daarop dat die gebruik van “geforseerde” toekenning haalbaar is in die meerderheid, maar nie in al die hawedeurvoerscenario’s nie. Alhoewel dit haalbaar is in terme van die beskikbare kapasiteit, is daar egter ’n verhoogde vervoerkoste vir die sitrusuitvoer-koue-ketting in die meerderheid van die scenario’s wat ontleed is. Hierdie addisionele vervoerkoste moet opgeweeg word teen die koste van opeenhoping, asook verlore tyd, en sal deur die sitrusuitvoer-koue-ketting geabsorbeer moet word. Selfs al is daar ’n verhoging in vervoerkoste wat die totale sitrusuitvoer-koue-ketting met soveel as +35.2% (in die slegste geval scenario) kan verhoog, word die meganisme as haalbaar geag aangesien die impak van die verhoogde vervoerkoste ’n relatiewe maatstaf is wat ’n wisselende impak op die verskillende belanghebbendes van die sitrusuitvoer-koue-ketting sal he, dus sal elke belanghebbende onafhanklik moet besluit of dit lewensvatbaar vir hulle sal wees. Die studie het geslaag in sy primere doel, naamlik die verligting van kapasiteitsdruk by Durban-hawe, deur die hertoewysing van sitrus volumes aan al die sitrus hawens, onder verskillende vlakke van kapasiteit beskikbaarheid by Durban-hawe. Gevolglik word ”geforseerde” toewysing as ’n goeie alternatiewe oplossing vir die huidige oorlaaide situasie beskou.Master

The Impact of Delays on Service Times in the Intensive Care Unit

Mainstream queueing models are frequently employed in modeling healthcare delivery in a number of settings, and they further are used in making operational decisions for the same. The vast majority of these queueing models ignore the effects of delay experienced by a patient awaiting care. However, long delays may have adverse effects on patient outcomes and can potentially lead to a longer length of stay (LOS) when the patient ultimately does receive care. This work sets out to understand these delay issues from an operational perspective. Using data of more than 57,000 emergency department (ED) visits,we use an instrumental variable approach to empirically measure the impact of delays in intensive care unit (ICU) admission, i.e., ED boarding, on the patient's ICU LOS for multiple patient types. Capturing these empirically observed effects in a queueing model is challenging because the effect introduces potentially long-range correlations in service and interarrival times. We propose a queueing model that incorporates these measured delay effects and characterizes approximations to the expected work in the system when the service time of a job is adversely impacted by the delay experienced by that job. Our approximation demonstrates an effect of system load on work that grows much faster than the traditional 1/(1 - ρ) relationship seen in most queueing systems. As such, it is imperative that the relationship of delays and LOS be better understood by hospital managers so that they can make capacity decisions that prevent even seemingly moderate delays from causing dire operational consequences. Key words: Delay effects, queueing, HealthcareNational Science Foundation (U.S.) (CAREER Grant CMMI-1054034