A discrete-event simulation model for assessing operating room efficiency of thoracic, gastrointestinal, and orthopedic surgeries

BACKGROUND

In hospital management, pinpointing steps that most enhance operating room (OR) throughput is challenging. While prior literature has utilized discrete event simulation (DES) to study specific strategies such as scheduling and resource allocation, our study examines an earlier planning phase, assessing all workflow stages to determine the most impactful steps for subsequent strategy development.

METHODS

DES models real-world systems by simulating sequential events. We constructed a DES model for thoracic, gastrointestinal, and orthopedic surgeries summarized from a tertiary Chinese hospital. The model covers preoperative preparations, OR occupation, and OR preparation. Parameters were sourced from patient data and staff experience. Model outcome is OR throughput. Post-validation, scenario analyses were conducted for each department, including: (1) improving preoperative patient preparation time; (2) increasing PACU beds; (3) improving OR preparation time; (4) use of new equipment to reduce the operative time of a selected surgery type; three levels of improvement (slight, moderate, large) were investigated.

RESULTS

The first three improvement scenarios resulted in a 1%-5% increase in OR throughput across the three departments. Large reductions in operative time of the selected surgery types led to approximately 12%, 33%, and 38% increases in gastrointestinal, thoracic, and orthopedic surgery throughput, respectively. Moderate reductions resulted in 6%-17% increases in throughput and slight reductions of 1%-7%.

CONCLUSIONS

The model could reliably reflect OR workflows of the three departments. Among the options investigated, model simulations suggest that improving OR preparation time and operative time are the most effective.

Does the implementation of a trauma system affect injury-related morbidity and economic outcomes? A systematic review

BACKGROUND

Trauma accounts for a huge burden of disease worldwide. Trauma systems have been implemented in multiple countries across the globe, aiming to link and optimise multiple aspects of the trauma care pathway, and while they have been shown to reduce overall mortality, much less is known about their cost-effectiveness and impact on morbidity.

METHODS

We performed a systematic review to explore the impact the implementation of a trauma system has on morbidity, quality of life and economic outcomes, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. All comparator study types published since 2000 were included, both retrospective and prospective in nature, and no limits were placed on language. Data were reported as a narrative review.

RESULTS

Seven articles were identified that met the inclusion criteria, all of which reported a pre-trauma and post-trauma system implementation comparison in high-income settings. The overall study quality was poor, with all studies demonstrating a severe risk of bias. Five studies reported across multiple types of trauma patients, the majority describing a positive impact across a variety of morbidity and health economic outcomes following trauma system implementation. Two studies focused specifically on traumatic brain injury and did not demonstrate any impact on morbidity outcomes.

DISCUSSION

There is currently limited and poor quality evidence that assesses the impact that trauma systems have on morbidity, quality of life and economic outcomes. While trauma systems have a fundamental role to play in high-quality trauma care, morbidity and disability data can have large economic and cultural consequences, even if mortality rates have improved. The sociocultural and political context of the surrounding healthcare infrastructure must be better understood before implementing any trauma system, particularly in resource-poor and fragile settings.

PROSPERO REGISTRATION NUMBER

CRD42022348529 LEVEL OF EVIDENCE: Level III.

Robust optimization of a radiotherapy pretreatment preparation workflow

Objective. Increasing cancer incidence, staff shortage and high burnout rate among radiation oncologists, medical physicists and radiation technicians are putting many departments under strain. Operations research (OR) tools could optimize radiotherapy processes, however, clinical implementation of OR-tools in radiotherapy is scarce since most investigated optimization methods lack robustness against patient-to-patient variation in duration of tasks. By combining OR-tools, a method was developed that optimized deployment of radiotherapy resources by generating robust pretreatment preparation schedules that balance the expected average patient preparation time (Fmean) with the risk of working overtime (RoO). The method was evaluated for various settings of an one-stop shop (OSS) outpatient clinic for palliative radiotherapy.Approach. The OSS at our institute sees, scans and treats 3-5 patients within one day. The OSS pretreatment preparation workflow consists of a fixed sequence of tasks, which was manually optimized for radiation oncologist and CT availability. To find more optimal sequences, with shorterFmeanand lowerRoO, a genetic algorithm was developed which regards these sequences as DNA-strands. The genetic algorithm applied natural selection principles to produce new sequences. A decoder translated sequences to schedules to find the conflicting fitness parametersFmeanandRoO. For every generation, fitness of sequences was determined by the distance to the estimated Pareto front ofFmeanandRoO. Experiments were run in various OSS-settings.Main results. According to our approach, the expectedFmeanof the current clinical schedule could be reduced with 37%, without increasingRoO. Additional experiments provided insights in trade-offs betweenFmean,RoO, working shift length, number of patients treated on a single day and staff composition.Significance. Our approach demonstrated that OR-tools could optimize radiotherapy resources by robust pretreatment workflow scheduling. The results strongly support further exploration of scheduling optimization for treatment preparation also outside a one-stop shop or radiotherapy setting.

Reducing waiting times from 65 to under 40 days for children and adolescents receiving mental health services using a new scheduling policy

The aim of this study is to conduct an intervention that tests whether a new scheduling policy designed to reduce waiting times actually will lead to a reduction in waiting times. The new scheduling policy was developed using mixed methods. Qualitative data was gathered to fully understand current planning processes, while quantitative methods were used to model and predict future waiting times. If current planning practices are continued, waiting times will only increase. Additionally, the findings show that simulation modeling can be used to predict the capacity needed for intakes (first appointment) to reduce and maintain target waiting times over time. In our study, this meant a slight increase in capacity for intakes. This new scheduling policy led to a reduction in waiting times from 65 days in 2016, to under 40 days post-intervention in 2017. Waiting times have been held under 40 days since implementation of the new policy, 2017-2020. Our study shows that setting appropriate (weekly) intake goals, will lead to maintaining acceptable levels of variation in waiting times. This theory was tested and proven to be effective.

Simulation as a tool to model potential workflow enhancements in radiotherapy treatment pathways - A systematic review

This systematic review aimed to synthesize and summarize the use of simulation of radiotherapy pathways. The objective was to establish the suitability of those simulations in modeling the potential introduction of processes and technologies to speed up radiotherapy pathways. A systematic literature search was carried out using PubMed and Scopus databases to evaluate the use of simulation in radiotherapy pathways. Full journal articles and conference proceedings were considered, and the search was limited to the English language only. To be eligible for inclusion, articles had to model multiple sequential processes in the radiotherapy pathway concurrently to demonstrate the suitability of simulation modeling in typical pathways. Papers solely modeling scheduling, capacity, or queuing strategies were excluded. In total, 151 potential studies were identified and screened to find 18 relevant studies in October 2022. Studies showed that various pathways could be modeled, including the entire pathway from referral to end of treatment or the constituent phases such as pre-treatment, treatment, or other subcomponents. The data required to generate models varied from study to study, but at least 3 months of data were needed. This review demonstrates that modeling and simulation of radiotherapy pathways are feasible and that model output matches real-world systems. Validated models give researchers confidence to modify models with potential workflow enhancements to assess their potential effect on real-world systems. It is recommended that researchers follow best practice guidelines when building models to ensure that they are fit for purpose and to enable decision makers to have confidence in their results.

Forecasting Caseload of Critically Ill Patients Who Are Alert and Without Delirium for at Least Two Consecutive Days for the Assessment of Their Psychological Distress

INTRODUCTION

One-quarter of alert, non-delirious patients in critical care units report significant psychological distress. Treatment of this distress depends upon identifying these high-risk patients. Our aim was to characterize how many critical care patients remain alert and without delirium for at least two consecutive days and could thus predictably undergo evaluation for distress.

METHODS

This retrospective cohort study used data from a large teaching hospital in the United States of America, from October 2014 to March 2022. Patients were included if they were admitted to one of three intensive care units, and for >48 hours all delirium and sedation screenings were negative (Riker sedation-agitation scale four, calm and cooperative, and no delirium based on all Confusion Assessment Method for the Intensive Care Unit scores negative and all Delirium Observation Screening Scale less than three). Means and standard deviations of means for counts and percentages are reported among the most recent six quarters. Means and standard deviations of means for lengths of stay were calculated among all N=30 quarters. The Clopper-Pearson method was used to calculate the lower 99% confidence limit for the percentages of patients who would have had at most one assessment of dignity-related distress before intensive care unit discharge or change in mental status.

RESULTS

An average of 3.6 (standard deviation 0.2) new patients met the criteria daily. The percentages of all critical care patients (20%, standard deviation 2%) and hours (18%, standard deviation 2%) meeting criteria decreased slightly over the 7.5 years. Patients spent a mean of 3.8 (standard deviation 0.1) days awake in critical care before their condition or site changed. In the context of assessing distress and potentially treating it before the date of change of condition (e.g., transfer), 66% (6818/10314) of patients would have zero or one assessment, lower 99% confidence limit of 65%.

CONCLUSIONS

Approximately one-fifth of critically ill patients are alert and without delirium and thus could be evaluated for distress during their intensive care unit stay, mostly during a single visit. These estimates can be used to guide workforce planning.

A comparative analysis of outpatient nutrition clinic scheduling outcomes based on in-person and telehealth patient care delivery modalities☆

During the start of the global COVID-19 pandemic in March 2020, patient care modalities changed from in-person to telehealth to comply with physical distancing guidelines. Our study uniquely examines operations data from three distinct periods: before the transition to telehealth, early transition from in-person care to telehealth, and the eventual adoption of telehealth. We present a comparative analysis of outpatient nutrition clinic scheduling outcomes based on care delivery modality. We used descriptive statistics to report means and variance and frequencies. We used inferential statistics to make comparisons: categorical data were compared using chi- square analysis with post-hoc comparisons using a z-test with alpha at 0.05. Means of continuous variables were compared using ANOVA with Tukey HSD post-hoc analysis. We found patient demographics remained widely unchanged across the three distinct periods as the demand for telehealth visits increased, with a notable rise in return patient visits, signaling both adaptability across the patient population and acceptance of the telehealth modality. These analyses along with evidence from the included literature review point to many the benefits of telehealth, thus telehealth as a healthcare delivery modality is here to stay. Our work serves as a foundation for future studies in this field, provides information for decision-makers in telehealth-related strategic planning, and can be utilized in advocacy for the extension of telehealth coverage.

Advancing interdisciplinary science for disrupting wildlife trafficking networks

Wildlife trafficking, whether local or transnational in scope, undermines sustainable development efforts, degrades cultural resources, endangers species, erodes the local and global economy, and facilitates the spread of zoonotic diseases. Wildlife trafficking networks (WTNs) occupy a unique gray space in supply chains-straddling licit and illicit networks, supporting legitimate and criminal workforces, and often demonstrating high resilience in their sourcing flexibility and adaptability. Authorities in different sectors desire, but frequently lack knowledge about how to allocate resources to disrupt illicit wildlife supply networks and prevent negative collateral impacts. Novel conceptualizations and a deeper scientific understanding of WTN structures are needed to help unravel the dynamics of interaction between disruption and resilience while accommodating socioenvironmental context. We use the case of ploughshare tortoise trafficking to help illustrate the potential of key advancements in interdisciplinary thinking. Insights herein suggest a significant need and opportunity for scientists to generate new science-based recommendations for WTN-related data collection and analysis for supply chain visibility, shifts in illicit supply chain dominance, network resilience, or limits of the supplier base.

Operations Research to Solve Kidney Allocation Problems: A Systematic Review

BACKGROUND

Operations research techniques enable health care administrators to optimize resource allocation and to find solutions to staff and patient scheduling problems. We aimed to conduct the first systematic review of the international literature on the use of operations research for allocating deceased-donor kidneys.

METHODS

We searched the MEDLINE, EMBASE, and PubMed databases from inception to February 2023. Two reviewers independently screened the title/abstract and subsequently the full text of potentially eligible articles and abstracted the data. Quality assessment of the final set of studies was conducted using Subben's checklist.

RESULTS

Of the 302 citations identified, 5 studies were included. These studies covered three themes, including (1) provider-facing decision aids to determine the timing of transplant for single or multiple patients; (2) system-level planning on kidney allocation based on blood type matching rules; and (3) patient-facilitated wait times estimation using incomplete information. Markov models, sequential stochastic assignment models, and queuing models were amongst the most used techniques. Although we found all included studies to meet Subben's criteria, we believe the checklist in its current form lacks items to assess the validity of model inferences. As such, we ended this review with a set of practical recommendations.

CONCLUSIONS

Our review demonstrated the utility of operations research techniques in assisting the system, healthcare providers, and patients in the transplantation process. More research is needed to reach a consensus on a model that can be used to support the decision-making of different stakeholders for efficient kidney allocation, with the ultimate goal of reducing the gap between kidney supply and demand and enhancing the population's well-being.

On integrating patient appointment grids and technologist schedules in a radiology center

Optimal patient appointment grid scheduling improves medical center performance and reduces pressure from excess demand. Appointment scheduling efficiency depends on resource management, and staff are a key resource. Personnel scheduling takes into account union rules, skills, contract types, training, leave, illness, etc. When combined with appointment scheduling constraints, the complexity of the problem increases. In this paper, we study the combination of the patient appointment grid and technologist scheduling. We present a well-detailed framework outlining our approach. We develop two versions of a mixed-integer programming model: integrated and sequential. In the first version, we elaborate the appointment grid and the technologist schedules simultaneously, while in the second version we generate them sequentially. We evaluate the proposed approach using real data from the MRI department of the Centre hospitalier de l'Université de Montréal (CHUM) radiology center. We study different scenarios by testing several technologist rules and planning construction methods. Obtained solutions are compared to the current CHUM scheduling approach.

Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework

We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana.

Modeling disease progression and treatment pathways for depression jointly using agent based modeling and system dynamics

Introduction

Depression is a common mental health condition that affects millions of people worldwide. Care pathways for depression are complex and the demand across different parts of the healthcare system is often uncertain and not entirely understood. Clinical progression with depression can be equally complex and relates to whether or not a patient is seeking care, the care pathway they are on, and the ability for timely access to healthcare services. Considering both pathways and progression for depression are however rarely studied together in the literature.

Methods

This paper presents a hybrid simulation modeling framework that is uniquely able to capture both disease progression, using Agent Based Modeling, and related care pathways, using a System Dynamics. The two simulation paradigms within the framework are connected to run synchronously to investigate the impact of depression progression on healthcare services and, conversely, how any limitations in access to services may impact clinical progression. The use of the developed framework is illustrated by parametrising it with published clinical data and local service level data from Wales, UK.

Results and discussion

The framework is able to quantify demand, service capacities and costs across all care pathways for a range of different scenarios. These include those for varying service coverage and provision, such as the cost-effectiveness of treating patients more quickly in community settings to reduce patient progression to more severe states of depression, and thus reducing the costs and utilization of more expensive specialist settings.

A linear programming based method for designing menus for controlled feeding trials

BACKGROUND

Controlled feeding trials are an important method to determine cause-effect relationships between dietary intake and metabolic parameters, risk factors, or health outcomes. Participants of a controlled feeding trial receive full-day menus during a prespecified period of time. The menus have to comply with the nutritional and operational standards of the trial. Levels of nutrients under investigation should differ sufficiently between intervention groups, and be as similar as possible for all energy levels within intervention groups. Levels of other key nutrients should be as similar as possible for all participants. All menus have to be varied and manageable. Designing these menus is both a nutritional and a computational challenge that relies largely on the expertise of the research dietician. The process is very time consuming, and last-minute disruptions are very hard to manage.

OBJECTIVE

This paper demonstrates a mixed integer linear programming model to support the design of menus for controlled feeding trials.

METHODS

The model is demonstrated for a trial that involved consumption of individualized, isoenergetic menus with either a low or a high protein content.

RESULTS

All menus generated by the model comply with all standards of the trial. The model allows for including tight ranges on nutrient composition, and complex design features. The model is very helpful in managing contrast and similarity of key nutrient intake levels between groups and energy levels, and in coping with many energy levels and nutrients. The model helps to propose several alternative menus and to manage last-minute disruptions. The model is flexible; it can easily be adapted to suit trials with other components or different nutritional requirements.

CONCLUSIONS

The model helps to design menus in a fast, objective, transparent, and reproducible way. It greatly facilitates the design procedure for menus in controlled feeding trials and lowers development costs.

Prevalence and antimicrobial resistance of Escherichia coli and Salmonella spp. in animal feed in Colombia

Objective

To determine the prevalence and antimicrobial resistance of Escherichia coli and Salmonella spp. in animal feed samples collected between 2018 and 2021 in Colombia.

Methods

This was a laboratory-based cross-sectional study using routine data from the program for inspection, surveillance, and control of animal feed at the Colombian Agriculture Institute. Samples of animal feed for swine, poultry, canine, feline, leporine, piscine, and equine species were processed for detection of E. coli and Salmonella spp. using enrichment and selective culture methods. Isolates were tested for antimicrobial susceptibility using an automated microdilution method.

Results

Of 1 748 animal feed samples analyzed, 83 (4.7%) were positive for E. coli and 66 (3.8%) for Salmonella spp. The presence of E. coli and Salmonella spp. was highest in feed for poultry (6.4% and 5.5%) and swine (6.1% and 4.3%). Antimicrobial resistance testing was performed in 27 (33%) E. coli isolates and 26 (39%) Salmonella isolates. Among E. coli, resistance was most frequently observed to ampicillin (44.5%) followed by cefazolin (33.3%), ciprofloxacin (29.6%), ampicillin/sulbactam (26%), and ceftriaxone (11.1%). The highest resistance levels in Salmonella spp. isolates were against cefazolin (7.7%) and piperacillin/tazobactam (7.7%).

Conclusions

This is the first study from Colombia reporting on the prevalence and antimicrobial resistance of E. coli and Salmonella spp. in animal feed samples. Its results establish a baseline over a wide geographical distribution in Colombia. It highlights the need to integrate antimicrobial resistance surveillance in animal feed due to the emergence of resistant bacteria in this important stage of the supply chain.

High levels of antimicrobial resistance in Escherichia coli and Salmonella from poultry in Ecuador

Objective

To describe antimicrobial resistance profiles of Escherichia coli and Salmonella spp. isolated from chicken carcasses and the antimicrobials commonly used in animals in Ecuador and provide information on antimicrobial resistance patterns for implementing evidence-based corrective measures.

Methods

Meat samples were collected from chicken carcasses in 199 slaughterhouses across Ecuador as part of a national pilot study for monitoring antimicrobial resistance in agricultural sources in 2019. Samples were tested for E. coli and Salmonella spp. Sensitivity to 10 critically important and three highly important antimicrobials (from a human health perspective) was assessed. The country report submitted to the World Organization for Animal Health was accessed to extract the quantity of antimicrobials produced or imported for use in animals.

Results

Of 383 samples, E. coli was isolated from 148 (39%) and Salmonella spp. from 20 (5%) samples. Ninety percent of the isolates were resistant to at least one critically important antimicrobial. Resistance was highest to erythromycin (E. coli 76%; Salmonella spp. 85%) and tetracycline (E. coli 71%; Salmonella spp. 90%). Critically or highly important antimicrobials (colistin, tetracycline, trimethoprim/sulfamethoxazole) formed the bulk (87%) of antimicrobials used in animals as per the World Organization for Animal Health report.

Conclusions

High prevalence of antimicrobial resistance in poultry in Ecuador calls for the development of guidelines and regulations on the use of antimicrobials and for engagement with livestock producers. The existing surveillance system needs to be strengthened to improve the monitoring of antimicrobial use and evolving resistance patterns.

Wait!What does that mean?: Eliminating ambiguity of delays in healthcare from an OR/MS perspective

Waiting time in healthcare is a significant problem that occurs across the world and often has catastrophic effects. There are various terms used for waiting time ("sojourn", "throughput" etc.) and there is no consensus on how these terms are defined. Ambiguous definitions of waiting time make it difficult to compare and measure the problems related to waiting times and delays in healthcare. We present a systematic search and review of the Operations Research and Management Science (ORMS) literature on delays in healthcare services. We search for articles from 2004 to 2019 and base our search strategy on a well-known healthcare planning and control decision taxonomy. An important step towards reducing the ambiguity in the definitions is to distinguish between access time and waiting time. We provide clear definitions and examples of access time and waiting time, and we classify our search results according to three categories: article type, healthcare service investigated and ORMS technique used to solve the delay problem. We find that half of the ORMS research on the waiting and access time problem is done on Ambulatory Care services. We provide tables for each healthcare service that highlight key definitions, the techniques that are used most often and the healthcare environment where the research is done. This research highlights the significant ORMS research that is done on access and waiting time in healthcare as well as the remaining research opportunities. Moreover, it provides a common language for the ORMS community to solve critical waiting time issues in healthcare.

Antibiotic consumption in secondary and tertiary hospitals in Colombia: national surveillance from 2018-2020

Objective

To assess the compliance in secondary and tertiary level hospitals with monthly reporting of antibiotic consumption to the Colombian National Public Health Surveillance System (SIVIGILA-INS), and to describe reported antibiotic consumption during 2018-2020.

Methods

This study involved a secondary analysis of antibiotic consumption data reported to SIVIGILA-INS. Frequency of hospital reporting was assessed and compared against expected reports, disaggregated by intensive care units (ICU)/non-ICU wards and geographical regions. Consumption was expressed as defined daily dose (DDD) per 100 occupied beds for seven antibiotics.

Results

More than 70% of hospitals reported antibiotic consumption at least once in each of the three years (79% in ICU and 71% in non-ICU wards). Of these, ICU monthly reporting was complete (12 monthly reports per year) for 59% in the period 2018-2019 but only 4% in 2020. Non-ICU reporting was complete for 52% in 2019 and for 2% in 2020. Most regions had an overall decrease in reporting in 2020. Analysis of antibiotic consumption showed an increase for piperacillin/tazobactam, ertapenem, and cefepime from 2019 to 2020.

Conclusions

There were gaps in the consistency and frequency of reporting. Efforts are needed to improve compliance with monthly reporting, which declined in 2020, possibly due to the COVID-19 pandemic. Non-compliance on reporting and data quality issues should be addressed with the hospitals to enable valid interpretation of antibiotic consumption trends.

Vaccine Allocation and Distribution: A Review with a Focus on Quantitative Methodologies and Application to Equity, Hesitancy, and COVID-19 Pandemic

This review focuses on vaccine distribution and allocation in the context of the current COVID-19 pandemic. The implications discussed are in the areas of equity in vaccine distribution and allocation (at a national level as well as worldwide), vaccine hesitancy, game-theoretic modeling to guide decision-making and policy-making at a governmental level, distribution and allocation barriers (in particular in low-income countries), and operations research (OR) mathematical models to plan and execute vaccine distribution and allocation. To conduct this review, we adopt a novel methodology that consists of three phases. The first phase deploys a bibliometric analysis; the second phase concentrates on a network analysis; and the last phase proposes a refined literature review based on the results obtained by the previous two phases. The quantitative techniques utilized to conduct the first two phases allow describing the evolution of the research in this area and its potential ramifications in future. In conclusion, we underscore the significance of operations research (OR)/management science (MS) research in addressing numerous challenges and trade-offs connected to the current pandemic and its strategic impact in future research.

Maximizing clinical rotation placements for US medical students: exploring an optimization model

BACKGROUND

For years, US medical schools have relied on community-based, private clinicians to educate medical students. There has been a steady decline in the number of physicians willing to take on medical students in their clinical practices. Recent issues related to the pandemic raise questions about how many patients students should see to have a meaningful clinical experience.

METHODS

As part of a 16-week longitudinal clinical experience, medical students spend 2 days each week in a family medicine or internal medicine clinic. As repetition enhances learning, maximizing the number of patients students see is important. Using a mixed integer linear program, we sought to determine the optimal schedule that maximizes the number of patients whom students see during a rotation. Patient visits were collected from January to April 2018 for clinics used by the medical school. By maximizing the minimum number of patients per learner over all non-empty day-clinic combinations, we deliver equitable rotation plans based on our assumptions.

RESULTS

For this pilot study, multiple experiments were performed with different numbers of students assigned to clinics. Each experiment also generated a weekly rotation plan for a given student. Based on this optimization model, the minimum number of patients per student over 16 weeks was 87 (3 patients per day) and actually increased the number of students who could be assigned to one of the clinics from 1 student per rotation to 8 students.

CONCLUSIONS

The mixed integer linear program assigned more students to clinics that have more total visits in order to achieve the optimal and fairest learning quality. In addition, by conducting various experiments on different numbers of students, we observed that we were able to allocate more students without affecting the number of patients students see.

"More is not always better": Park's sweet spot theory-driven implementation strategy for viable optimal safe nurse staffing policy in practice

AIM

This paper aims to propose Park's sweet spot theory-driven implementation strategy, which makes optimal safe staffing policy really work in nursing practice.

BACKGROUND

For the last 40 years, mainstream nursing workforce research has emphasized that having more registered nurses leads to better patient outcomes, and yet staffing policies have failed to implement this crucial concept. Meanwhile, global nursing shortages have become rampant, a problem that only dilutes the skill-mix ratios in the nursing workforce. Low fertility and an aging population worldwide are then accelerating these shortages. These dire circumstances may be persisting because of unclear, unsubstantiated cost-efficiency in the nursing workforce. For this reason, there remains a dearth of well-researched evidence for a clear threshold on optimal safe staffing levels that could maximize quality of care relative to cost given limited healthcare financial budgets and which could also be fitted into each care setting. Along with that, an implementation strategy for optimal safe staffing levels is nonexistent.

SOURCES OF EVIDENCE

An implementation strategy has been developed through interdisciplinary consilience-based theory synthesis of both prospective theory and regulatory focus theory combined with Park's optimized nursing staffing (sweet spot) estimation theory.

DISCUSSION/CONCLUSIONS

A theory-driven novel implementation strategy is proposed, which functions as a nudge strategy that uses the synchronous balance of quality of care, nurse staffing, and cost. It illustrates (1) how to create shared value among patients, nurses, and stakeholders through robust evidence-based, informed shared decision-making rationales on the optimal safe nurse staffing levels and (2) how to induce stakeholders to overcome resistance to innovation and improve their nursing workforce through value chain in management science.

IMPLICATIONS FOR NURSING WORKFORCE POLICY

This novel implementation strategy may be a viable solution to mitigate the nursing shortage by leading stakeholders (1) to compete with each other (on the basis of nursing sufficiency) and (2) to competitively demonstrate the patient-centered value (patient-perceived care quality relative to cost) of their institutions.

Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization

Quantifying the population sizes of distinct neuron types in different anatomical regions is an essential step towards establishing a brain cell census. Although estimates exist for the total neuronal populations in different species, the number and definition of each specific neuron type are still intensively investigated. Hippocampome.org is an open-source knowledge base with morphological, physiological and molecular information for 122 neuron types in the rodent hippocampal formation. While such framework identifies all known neuron types in this system, their relative abundances remain largely unknown. This work quantitatively estimates the counts of all Hippocampome.org neuron types by literature mining and numerical optimization. We report the number of neurons in each type identified by main neurotransmitter (glutamate or GABA) and axonal-dendritic patterns throughout 26 subregions and layers of the dentate gyrus, Ammon's horn, subiculum and entorhinal cortex. We produce by sensitivity analysis reliable numerical ranges for each type and summarize the amounts across broad neuronal families defined by biomarkers expression and firing dynamics. Study of density distributions indicates that the number of dendritic-targeting interneurons, but not of other neuronal classes, is independent of anatomical volumes. All extracted values, experimental evidence and related software code are released on Hippocampome.org.

Identifying how GPs spend their time and the obstacles they face: a mixed-methods study

Background

Although problems that impair task completion — known as operational failures — are an important focus of concern in primary care, they have remained little studied.

Aim

To quantify the time GPs spend on different activities during clinical sessions; to identify the number of operational failures they encounter; and to characterise the nature of operational failures and their impact for GPs.

Design and setting

Mixed-method triangulation study with 61 GPs in 28 NHS general practices in England from December 2018 to December 2019.

Method

Time–motion methods, ethnographic observations, and interviews were used.

Results

Time–motion data on 7679 GP tasks during 238 hours of practice in 61 clinical sessions suggested that operational failures were responsible for around 5.0% (95% confidence interval [CI] = 4.5% to 5.4%) of all tasks undertaken by GPs and accounted for 3.9% (95% CI = 3.2% to 4.5%) of clinical time. However, qualitative data showed that time–motion methods, which depend on pre-programmed categories, substantially underestimated operational failures. Qualitative data also enabled further characterisation of operational failures, extending beyond those measured directly in the time–motion data (for example, interruptions, deficits in equipment/supplies, and technology) to include problems linked to GPs’ coordination role and weaknesses in work systems and processes. The impacts of operational failures were highly consequential for GPs’ experiences of work.

Conclusion

GPs experience frequent operational failures, disrupting patient care, impairing experiences of work, and imposing burden in an already pressurised system. This better understanding of the nature and impact of operational failures allows for identification of targets for improvement and indicates the need for coordinated action to support GPs.

Quantifying and interpreting inequality of surgical site infections among operating rooms

PURPOSE

The incidence of surgical site infection differs among operating rooms (ORs). However, cost effectiveness of interventions targeting ORs depends on infection counts. The purpose of this study was to quantify the inequality of infection counts among ORs.

METHODS

We performed a single-centre historical cohort study of elective surgical cases spanning a 160-week period from May 2017 to May 2020, identifying cases of infection within 90 days using International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes. We used the Gini index to measure inequality of infections among ORs. As a reference, the Gini index for inequality of household disposable income in the US in 2017 was 0.39, and 0.31 for Canada.

RESULTS

There were 3,148 (3.67%) infections among the 85,744 cases studied. The 20% of 57 ORs with the most and least infections accounted for 44% (99% confidence interval [CI], 36 to 52) and 5% (99% CI, 2 to 8), respectively. The Gini index was 0.40 (99% CI, 0.31 to 0.50), which is comparable to income inequality in the US. There were more infections in ORs with more minutes of cases (Spearman correlation ρ = 0.68; P < 0.001), but generally not in ORs with more total cases (ρ = 0.11; P = 0.43). Moderately long (3.3 to 4.8 hr) cases had a large effect, having greater incidences of infection, while not being so long as to have just one case per day per OR. There was substantially greater inequality in infection counts among the 557 observed combinations of OR specialty (Gini index 0.85; 99% CI, 0.81 to 0.88).

CONCLUSIONS

Inequality of infections among ORs is substantial and caused by both inequality in the incidence of infections and inequality in the total minutes of cases. Inequality in infections among OR and specialty combinations is due principally to inequality in total minutes of cases.

High Prevalence of Methicillin-Resistant Staphylococcus aureus among Healthcare Facilities and Its Related Factors in Myanmar (2018-2019)

BACKGROUND

Antimicrobial resistance (AMR) is a growing global health problem. Staphylococcus aureus (SA) is a common bacterium associated with a variety of community and hospital infections. Methicillin-resistant Staphylococcus aureus (MRSA) accounts for most SA related morbidity and mortality. In this study, we determined the prevalence and factors associated with SA and MRSA in Myanmar.

METHODS

We collected the data retrospectively by reviewing an electronic register containing the results of bacterial culture and antibiotic susceptibility testing of biological specimens received from healthcare facilities during 2018-2019.

RESULTS

Of the 37,798 biological specimens with bacterial culture growth, 22% (8244) were Gram-positive. Among the Gram-positive bacteria, 42% (2801) were SA, of which 48% (1331) were judged as MRSA by phenotypic methods. The prevalence of MRSA was higher in the older age groups, in female patients, in urine specimens and specimens received from the intensive care unit and dermatology departments. One site (Site F) had the highest MRSA prevalence of the seven AMR sentinel sites. Most SA isolates were sensitive to vancomycin (90%) by phenotypic methods.

CONCLUSIONS

The high prevalence of MRSA indicates a major public health threat. There is an urgent need to strengthen the AMR surveillance and hospital infection control program in Myanmar.

Workforce Planning for Community-Based Palliative Care Specialist Teams Using Operations Research

CONTEXT

Many countries have aging populations. Thus, the need for palliative care will increase. However, the methods to estimate optimal staffing for specialist palliative care teams are rudimentary as yet.

OBJECTIVES

To develop a population-need workforce planning model for community-based palliative care specialist teams and to apply the model to forecast the staff needed to care for all patients with terminal illness, organ failure, and frailty during the next 20 years, with and without the expansion of primary palliative care.

METHODS

We used operations research (linear programming) to model the problem. We used the framework of the Canadian Society of Palliative Care Physicians and the Nova Scotia palliative care strategy to apply the model.

RESULTS

To meet the palliative care needs for persons dying across Nova Scotia in 2019, the model generated an estimate of 70.8 nurses, 23.6 physicians, and 11.9 social workers, a total of 106.3 staff. Thereby, the model indicated that a 64% increase in specialist palliative care staff was needed immediately, and a further 13.1% increase would be needed during the next 20 years. Trained primary palliative care providers currently meet 3.7% of need, and with their expansion are expected to meet 20.3% by 2038.

CONCLUSION

Historical, current, and projected data can be used with operations research to forecast staffing levels for specialist palliative care teams under various scenarios. The forecast can be updated as new data emerge, applied to other populations, and used to test alternative delivery models.

Seat Assignments With Physical Distancing in Single-Destination Public Transit Settings

While the importance of physical (social) distancing in reducing the spread of COVID-19 has been well-documented, implementing similar controls in public transit remains an open question. For instance, in the United States, guidance for maximum seating capacity in single-destination public transit settings, such as school buses, is only dependent on the physical distance between passengers. In our estimation, the available models/guidance are suboptimal/inefficient since they do not account for the possibility of passengers being from the same household. This paper discusses and addresses the aforementioned limitation through two types of physical distancing models. First, a mixed-integer programming model is used to assign passengers to seats based on the reported configuration of the vehicle and desired physical distancing requirement. In the second model, we present a heuristic that allows for household grouping. Through several illustrative scenarios, we show that seating assignments can be generated in near real-time, and the household grouping heuristic increases the capacity of the transit vehicles (e.g., airplanes, school buses, and trains) without increasing the risk of infection. A running application and its source code are available to the public to facilitate adoption and to encourage enhancements.

Leadership Perspectives on Implementing Health Information Exchange: Qualitative Study in a Tertiary Veterans Affairs Medical Center

Background

The US Department of Veterans Affairs (VA) seeks to achieve interoperability with other organizations, including non-VA community and regional health information exchanges (HIEs).

Objective

This study aims to understand the perspectives of leaders involved in implementing information exchange between VA and non-VA providers via a community HIE.

Methods

We interviewed operational, clinical, and information technology leaders at one VA facility and its community HIE partner. Respondents discussed their experiences with VA-HIE, including barriers and facilitators to implementation, and the associated impact on health care providers. Transcribed interviews were coded and analyzed using immersion-crystallization methods.

Results

VA and community HIE leaders found training to be a key factor when implementing VA-HIE and worked cooperatively to provide several styles and locations of training. During recruitment, a high-touch approach was successfully used to enroll patients and overcome their resistance to opting in. Discussion with leaders revealed the high levels of complexity navigated by VA providers and staff to send and retrieve information. Part of the complexity stemmed from the interconnected web of information systems and human teams necessary to implement VA-HIE information sharing. These interrelationships must be effectively managed to guide organizational decision making.

Conclusions

Organizational leaders perceived information sharing to be of essential value in delivering high-quality, coordinated health care. The VA continues to increase access to outside care through the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act. Along with this increase in non-VA medical care, there is a need for greater information sharing between VA and non-VA health care organizations. Insights by leaders into barriers and facilitators to VA-HIE can be applied by other national and regional networks that seek to achieve interoperability across health care delivery systems.

An application of PCA-DEA with the double-bootstrap approach to estimate the technical efficiency of New Zealand District Health Boards

Using yearly panel data from 2011 to 2017 on New Zealand District Health Boards (DHBs), this study combines principal component analysis and data envelopment intertemporal analysis with the double-bootstrap approach to estimate the technical efficiency of health care providers along with the trend of efficiency performances. The results show that although most large DHBs have improved their level of technical efficiency between 2011 and 2017, the majority of medium- and small-sized DHBs have not seen any noticeable improvement in their level of technical efficiency. The results also show that large and tertiary DHBs operate at a high level of technical efficiency. In contrast, most of the medium- and small-sized DHBs posted some of the lowest technical efficiency scores. Furthermore, the results show that medium- and small-sized DHBs have a higher average length of hospital stays which is found to be associated with decreasing levels of technical efficiency scores.

Optimization Models for HIV/AIDS Resource Allocation: A Systematic Review

OBJECTIVE

This study reviews optimization models for human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) resource allocation.

METHODS

We searched 2 databases for peer-reviewed articles published from January 1985 through August 2019 that describe optimization models for resource allocation in HIV/AIDS. We included models that consider 2 or more competing HIV/AIDS interventions. We extracted data on selected characteristics and identified similarities and differences across models. We also assessed the quality of mathematical disease transmission models based on the best practices identified by a 2010 task force.

RESULTS

The final qualitative synthesis included 23 articles that used 14 unique optimization models. The articles shared several characteristics, including the use of dynamic transmission modeling to estimate health benefits and the inclusion of specific high-risk groups in the study population. The models explored similar HIV/AIDS interventions that span primary and secondary prevention and antiretroviral treatment. Most articles were focused on sub-Saharan African countries (57%) and the United States (39%). There was notable variation in the types of optimization objectives across the articles; the most common was minimizing HIV incidence or maximizing infections averted (87%). Articles that utilized mathematical modeling of HIV disease and transmission displayed variable quality.

CONCLUSIONS

This systematic review of the literature identified examples of optimization models that have been applied in different settings, many of which displayed similar features. There were similarities in objective functions across optimization models, but they did not align with global HIV/AIDS goals or targets. Future work should be applied in countries facing the largest declines in HIV/AIDS funding.

[Factors associated with unsuccessful treatment of patients with drug-sensitive tuberculosis in Paraguay]

Objetivo.

Determinar los factores asociados con el tratamiento no exitoso en pacientes diagnosticados con tuberculosis (TB) sensible en Paraguay.

Métodos.

Estudio de cohorte retrospectivo, con datos recolectados del programa nacional entre enero de 2016 y marzo de 2017. Se definió tratamiento no exitoso como pacientes que se perdieron en el seguimiento, fracasaron en el tratamiento o fallecieron. Los factores asociados con tratamiento no exitoso se determinaron con regresión de Poisson; se estimó el riesgo relativo (RR) y el intervalo de confianza de 95% (IC95%) ajustados por región sanitaria.

Resultados.

Se estudiaron 3 034 casos: 2 189 (72,1%) tuvieron tratamiento exitoso (curados: 1 221 [40,2%], tratamiento completo: 968 [31,9%]) y 845 (27,9%) presentaron tratamiento no exitoso (fracaso: 40 [1,3%], pérdida de seguimiento: 466 [15,4%] y fallecidos: 339 [11,2%]). Los factores asociados a tratamiento no exitoso fueron hombres 1,28 (1,14- 1,42), indígenas 1,30 (1,09- 1,54), sin reporte de área de residencia 1,27 (1,02- 1,57), coinfección TB/VIH 1,97 (1,63- 2,38), adicción a drogas ilícitas 1,38 (1,16- 1,63), consumo de alcohol 1,25 (1,02- 1,52), ser previamente tratado 1,23 (1,10- 1,38) y sin datos de supervisión del tratamiento 4,92 (3,69- 6,56). Como factores protectores se consideran ser persona privada de libertad 0,65 (0,47- 0,89), así como la comorbilidad TB/diabetes 0,80 (0,67- 0,95).

Conclusión.

Paraguay presenta un alto porcentaje de tratamiento no exitoso en casi todo el país, sin alcanzar la meta propuesta por la Organización Mundial de la Salud. Los factores de riesgo asociados como infección por el virus de la inmunodeficiencia humana (VIH), consumo de drogas licitas e ilícitas y el ser indígena revela que es necesario revisar las estrategias dirigidas con abordaje interinstitucional.

Evaluation of an advanced practice provider emergency department critical care step-down unit

OBJECTIVE

In response to concerns about patient care and safety, our urban, tertiary care, Level 1 trauma center adult emergency department (ED) created an advanced practice provider-staffed critical care step-down unit (CCSU). We conducted a comprehensive evaluation of the CCSU's impact on patient care, safety, and ED operations.

METHODS

We compared ED length of stay, return visits to the ED within 72 hours, billing code assignments (current procedural terminology evaluation and management [CPT E&M] codes), and quality of electronic health record documentation per QNOTE for the 2 years after the CCSU was initiated (CCSU period) versus before its initiation (pre-CCSU period).

RESULTS

There were 31,418 critical care ED patient visits in the pre-CCSU period and 33,396 in the CCSU period. Median ED length of stay did not change overall between the CCSU versus pre-CCSU period (∆1 [95% confidence interval (CI) = -2.4, 4.4] minutes), but decreased for patients who remained in the critical care suites (∆-4 [95% CI = -7.8, -0.2] minutes). 72-hour return ED visits also did not change overall (∆0% [95% CI = -0.1, 0]), but decreased for patients who remained in the critical care suites (∆0.4% [95% CI = -0.05, -0.4]). CPT E&M billing increased for highest-level visits (99,291: ∆1.3% [95% CI= 0.5, 2.0]). Quality of electronic health record documentation as measured by QNOTE also improved (∆11.5% [95% CI = 4.9, 18.1]).

CONCLUSION

This ED's CCSU performance metrics indicate at least moderate improvement in ED length of stay, 72-hour return visits, critical care patient billing, and electronic health record documentation. EDs elsewhere can consider implementation of this advanced practice provider-staffed solution to improvement in critical care in ED.

Reverse Logistics Network Design for Effective Management of Medical Waste in Epidemic Outbreaks: Insights from the Coronavirus Disease 2019 (COVID-19) Outbreak in Wuhan (China)

The outbreak of an epidemic disease may pose significant treats to human beings and may further lead to a global crisis. In order to control the spread of an epidemic, the effective management of rapidly increased medical waste through establishing a temporary reverse logistics system is of vital importance. However, no research has been conducted with the focus on the design of an epidemic reverse logistics network for dealing with medical waste during epidemic outbreaks, which, if improperly treated, may accelerate disease spread and pose a significant risk for both medical staffs and patients. Therefore, this paper proposes a novel multi-objective multi-period mixed integer program for reverse logistics network design in epidemic outbreaks, which aims at determining the best locations of temporary facilities and the transportation strategies for effective management of the exponentially increased medical waste within a very short period. The application of the model is illustrated with a case study based on the outbreak of the coronavirus disease 2019 (COVID-19) in Wuhan, China. Even though the uncertainty of the future COVID-19 spread tendency is very high at the time of this research, several general policy recommendations can still be obtained based on computational experiments and quantitative analyses. Among other insights, the results suggest installing temporary incinerators may be an effective solution for managing the tremendous increase of medical waste during the COVID-19 outbreak in Wuhan, but the location selection of these temporary incinerators is of significant importance. Due to the limitation on available data and knowledge at present stage, more real-world information are needed to assess the effectiveness of the current solution.

Impacts of Operational Failures on Primary Care Physicians' Work: A Critical Interpretive Synthesis of the Literature

PURPOSE

Operational failures are system-level errors in the supply of information, equipment, and materials to health care personnel. We aimed to review and synthesize the research literature to determine how operational failures in primary care affect the work of primary care physicians.

METHODS

We conducted a critical interpretive synthesis. We searched 7 databases for papers published in English from database inception until October 2017 for primary research of any design that addressed problems interfering with primary care physicians' work. All potentially eligible titles/abstracts were screened by 1 reviewer; 30% were subject to second screening. We conducted an iterative critique, analysis, and synthesis of included studies.

RESULTS

Our search retrieved 8,544 unique citations. Though no paper explicitly referred to "operational failures," we identified 95 papers that conformed to our general definition. The included studies show a gap between what physicians perceived they should be doing and what they were doing, which was strongly linked to operational failures-including those relating to technology, information, and coordination-over which physicians often had limited control. Operational failures actively configured physicians' work by requiring significant compensatory labor to deliver the goals of care. This labor was typically unaccounted for in scheduling or reward systems and had adverse consequences for physician and patient experience.

CONCLUSIONS

Primary care physicians' efforts to compensate for suboptimal work systems are often concealed, risking an incomplete picture of the work they do and problems they routinely face. Future research must identify which operational failures are highest impact and tractable to improvement.

A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling

In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.

Towards the integration of lean principles and optimization for agricultural production systems: a conceptual review proposition

Operative planning in agricultural production has historically had the objective of improving yields and quality. Sowing, cropping, and harvesting are usually treated independently, and waste and the sustainability of operations are generally not integrated into operational planning methodologies for agricultural production. This study shows the need to have a clear and precise methodology to minimize waste in agricultural production systems to ensure sustainability. This need is addressed with a novel methodological guide to minimizing waste in agricultural operations, crop maintenance, and harvesting. The proposed methodology is founded on the use of lean manufacturing as a waste-management tool. Lean manufacturing principles allow agricultural operations and the variables that represent wastes to be identified, mathematical models to be built, constraints to be defined, and the cost of waste to be illustrated, as well as its minimization through an objective function. To guide implementation, we propose a conceptual model to explain the construction of a mathematical model that represents the development of decision variables on agricultural operations with the elements to consider and the constraints and theoretical proposal of the necessary objective function. The proposed conceptual model and the constructed methodology constitute a novel development within agricultural production systems that could be used by decision makers and farmers. © 2019 Society of Chemical Industry.

[Factors associated with completion of isoniazid preventive therapy in children in Quito, Ecuador (2014-2016 and 2018)Fatores associados à adesão à terapia preventiva com isoniazida em crianças de Quito, Equador (2014-2016 e 2018)]

Objetivo.

Conocer el porcentaje de cumplimiento de la terapia preventiva con isoniacida (TPI) en los establecimientos de salud de Quito, Ecuador y sus factores asociados en los niños menores de 5 años.

Métodos.

Investigación operativa con diseño de cohorte, en la que se obtuvo datos de informes y tarjetas de administración de tratamiento de los niños en TPI de los años 2014 al 2016 y de encuestas ad hoc aplicadas a cuidadores de los niños que recibieron TPI durante el año 2018.

Resultados.

Los niños menores de 5 años correspondieron a 29,3% del total de los contactos de los casos índices; 73% cumplieron TPI y 88,9% completaron al menos 6 meses de terapia. Se encontró asociación con la carga bacilar del caso índice, con la condición de pertenecer a un determinado distrito y su año de inicio. Se realizaron encuestas a 9 personas, funcionarios de los establecimientos salud y a 9 tutores de los niños; se registraron respuestas diversas sobre el agente causal de la tuberculosis, su transmisión y las características de la terapia preventiva.

Conclusiones.

La mayoría de los niños menores de 5 años que iniciaron TPI cumplieron con al menos 80% de las dosis prescritas, con determinadas asociaciones y percepciones en los cuidadores. En este contexto, surge la necesidad de realizar nuevas investigaciones operativas, para indagar más ampliamente sobre la adherencia y sobre los conocimientos, actitudes y prácticas de los profesionales de salud, los afectados por tuberculosis y su entorno.

Operations research models and methods for safety stock determination: A review

•A review on operations research (OR) models and methods for safety stock determination is conducted. •No work has yet systematized research focusing on the safety stock determination problem. •Articles are classified and discussed regarding the modeling approach, industrial application, solution technique and main performance criteria used. •Research opportunities, promising research directions and trends are identified.

In supply chain inventory management it is generally accepted that safety stocks are a suitable strategy to deal with demand and supply uncertainty aiming to prevent inventory stock-outs. Safety stocks have been the subject of intensive research, typically covering the problems of dimensioning, positioning, managing and placement. Here, we narrow the scope of the discussion to the safety stock dimensioning problem, consisting in determining the proper safety stock level for each product. This paper reports the results of a recent in-depth systematic literature review (SLR) of operations research (OR) models and methods for dimensioning safety stocks. To the best of our knowledge, this is the first systematic review of the application of OR-based approaches to investigate this problem. A set of 95 papers published from 1977 to 2019 has been reviewed to identify the type of model being employed, as well as the modeling techniques and main performance criteria used. At the end, we highlight current literature gaps and discuss potential research directions and trends that may help to guide researchers and practitioners interested in the development of new OR-based approaches for safety stock determination.

Outcomes of Community-Based Systematic Screening of Household Contacts of Patients with Multidrug-Resistant Tuberculosis in Myanmar

Screening of household contacts of patients with multidrug-resistant tuberculosis (MDR-TB) is a crucial active TB case-finding intervention. Before 2016, this intervention had not been implemented in Myanmar, a country with a high MDR-TB burden. In 2016, a community-based screening of household contacts of MDR-TB patients using a systematic TB-screening algorithm (symptom screening and chest radiography followed by sputum smear microscopy and Xpert-MTB/RIF assays) was implemented in 33 townships in Myanmar. We assessed the implementation of this intervention, how well the screening algorithm was followed, and the yield of active TB. Data collected between April 2016 and March 2017 were analyzed using logistic and log-binomial regression. Of 620 household contacts of 210 MDR-TB patients enrolled for screening, 620 (100%) underwent TB symptom screening and 505 (81%) underwent chest radiography. Of 240 (39%) symptomatic household contacts, 71 (30%) were not further screened according to the algorithm. Children aged <15 years were less likely to follow the algorithm. Twenty-four contacts were diagnosed with active TB, including two rifampicin- resistant cases (yield of active TB = 3.9%, 95% CI: 2.3%-6.5%). The highest yield was found among children aged <5 years (10.0%, 95% CI: 3.6%-24.7%). Household contact screening should be strengthened, continued, and scaled up for all MDR-TB patients in Myanmar.

[Loss to follow-up in patients treated for multidrug-resistant tuberculosis in EcuadorA Perda de seguimento de pacientes tratados para tuberculose multirresistente a medicamentos no Equador]

OBJECTIVE

Determine the incidence of loss to follow-up (LTFU) in patients treated for rifampicin-resistant tuberculosis (RR-TB) or multidrug-resistant tuberculosis (RR/MDR-TB), and the factors associated with this discharge status in Ecuador.

METHODS

Retrospective cohort study of patients with RR/MDR-TB who followed the World Health Organization's 18-24-month treatment regimen in 2014 and 2015, as reported by the Ministry of Health of Ecuador. The incidence of LTFU was determined, and clinical and epidemiological manifestations of cases discharged as LTFU were compared with those discharged as successfully treated. Survival was analyzed with Cox regression in order to evaluate factors associated with LTFU.

RESULTS

Of 328 cases, 270 (82.3%) were analyzed because they had a reported discharge status. Discharge as LTFU accounted for 39.6% of cases, and as successfully treated, 50.4%. The risk factors associated with LTFU were: previous discharge as LTFU in a previous TB episode [hazard ratio (HR): 2.96 (1.53-5.73), P < 0.001]; addiction to alcohol or drugs [HR: 2.82 (1.10-7.23), P = 0.031]; and having an Xpert® diagnosis (TB-RR) [HR: 1.53 (1.0-2.35), P = 0.048]. Of the total LTFU, 43% occurred after nine months of treatment.

CONCLUSION

The incidence of LTFU in patients with RR/MDR-TB in Ecuador is above the average for the Region of the Americas. The three identified factors support implementation of shorter regimens and patient-centered care, in line with the End TB Strategy.

[Factors associated with tuberculosis mortality in Paraguay, 2015-2016Fatores associados à mortalidade por tuberculose no Paraguai, 2015-2016]

OBJECTIVE

To describe the socio-demographic and clinical-epidemiological characteristics and to determine the factors associated with the mortality of people diagnosed with tuberculosis (TB) in Paraguay.

METHODS

Operational research with a retrospective cohort design of cases diagnosed with TB in Paraguay between 2015-2016. The database of the National Tuberculosis Control Program was used. Chi-square and relative risk (RR) tests with a 95% confidence interval (95% CI) were used to determine the factors associated with mortality; in addition, a robust Poisson multiple regression model was adjusted. A significance level of 5% was used.

RESULTS

Five hundred and forty-one cases of TB were studied, of which 11.5% died. The factors increasing the risk of death were male sex (RR 1.26; 95% CI 1.1-1.50), infection with human immunodeficiency virus (RR 4.78; 95% CI 4.04-5.65) and chronic obstructive pulmonary disease (RR 1.70; 95% CI 1.19-2.42). Being deprived of one's liberty was a protective factor (RR 0.37; 95% CI 0.24-0.61).

CONCLUSIONS

The highest risk of death is presented by men and people with TB/HIV coinfection and the lowest risk is presented by people deprived of liberty. There is a need to improve diagnosis and follow-up of TB cases, with effective implementation of directly observed treatment (DOTS) and timely management of associated diseases such as HIV and chronic obstructive pulmonary disease.

Optimizing Tele-ICU Operational Efficiency Through Workflow Process Modeling and Restructuring

Little is known on how to best prioritize various tele-ICU specific tasks and workflows to maximize operational efficiency. We set out to: 1) develop an operational model that accurately reflects tele-ICU workflows at baseline, 2) identify workflow changes that optimize operational efficiency through discrete-event simulation and multi-class priority queuing modeling, and 3) implement the predicted favorable workflow changes and validate the simulation model through prospective correlation of actual-to-predicted change in performance measures linked to patient outcomes.

SETTING

Tele-ICU of a large healthcare system in New York State covering nine ICUs across the spectrum of adult critical care.

PATIENTS

Seven-thousand three-hundred eighty-seven adult critically ill patients admitted to a system ICU (1,155 patients pre-intervention in 2016Q1 and 6,232 patients post-intervention 2016Q3 to 2017Q2).

INTERVENTIONS

Change in tele-ICU workflow process structure and hierarchical process priority based on discrete-event simulation.

MEASUREMENTS AND MAIN RESULTS

Our discrete-event simulation model accurately reflected the actual baseline average time to first video assessment by both the tele-ICU intensivist (simulated 132.8 ± 6.7 min vs 132 ± 12.2 min actual) and the tele-ICU nurse (simulated 128.4 ± 7.6 min vs 123 ± 9.8 min actual). For a simultaneous priority and process change, the model simulated a reduction in average TVFA to 51.3 ± 1.6 min (tele-ICU intensivist) and 50.7 ± 2.1 min (tele-ICU nurse), less than the added simulated reductions for each change alone, suggesting correlation of the changes to some degree. Subsequently implementing both changes simultaneously resulted in actual reductions in average time to first video assessment to values within the 95% CIs of the simulations (50 ± 5.5 min for tele-intensivists and 49 ± 3.9 min for tele-nurses).

CONCLUSIONS

Discrete-event simulation can accurately predict the effects of contemplated multidisciplinary tele-ICU workflow changes. The value of workflow process and task priority modeling is likely to increase with increasing operational complexities and interdependencies.

Capitalizing on specialists' services in rural family health unit: Interventional study

For achieving universal health coverage in Egypt, Ministry of Health aims to improve the accessibility to quality specialized health care.

OBJECTIVES

The objectives of the study were to explore legal and regulatory environment for the policy of specialists' visits to unit and assess the impact of gynecology specialist's visits on utilization pattern of gynecological services and clients' satisfaction.

METHODS

Settings The study settings were Ministry of Health/Headquarters, Giza Health Directorate, El-Badrashin Health District and Hospital, and Met-Rahinah Unit. Study design The study includes qualitative research (in-depth interviews with policymakers at all Ministry of Health levels and focus group discussions with service providers) and quantitative research (interventional operation research using separate sample pretest (n = 210) and posttest (n = 209) and family-planning service statistics).

RESULTS

Analysis of specialist outreach services indicated integration absence between preventive and curative sectors. Capitalizing on the policy of specialists' outreach visits to the unit could support clients' access quality services and reduce referral to get specialist services in hospitals. There is a 6-month intervention of scheduled visits of a female gynecologist to a rural health unit. The intervention led to 12% increase in efficiency and utilization of services compared with the previous 6 months. Clients' satisfaction increased from 27% to 73% after the intervention (.001, OR = 7.5, CI = 4.9-11.6).

CONCLUSION

Scheduled specialists' visits increase services' efficiency and clients' satisfaction.

An Internet of Things Buttons to Measure and Respond to Restroom Cleanliness in a Hospital Setting: Descriptive Study

Background

Restroom cleanliness is an important factor in hospital quality. Due to its dynamic process, it can be difficult to detect the presence of dirty restrooms that need to be cleaned. Using an Internet of Things (IoT) button can permit users to designate restrooms that need cleaning and in turn, allow prompt response from housekeeping to maintain real-time restroom cleanliness.

Objective

This study aimed to describe the deployment of an IoT button–based notification system to measure hospital restroom cleanliness reporting system usage and qualitative feedback from housekeeping staff on IoT button use.

Methods

We deployed IoT buttons in 16 hospital restrooms. Over an 8-month period, housekeeping staff received real-time notifications and responded to button presses for restroom cleaning. All button presses were recorded. We reported average button usage by hospital area, time of day, and day of week. We also conducted interviews with housekeeping supervisors and staff to understand their acceptance of and experience with the system.

Results

Over 8 months, 1920 requests to clean restrooms in the main hospital lobby and satellite buildings were received. The hospital lobby IoT buttons received over half (N=1055, 55%) of requests for cleaning. Most requests occurred in afternoon hours from 3 PM to midnight. Requests for cleaning remained stable throughout the work week with fewer requests occurring over weekends. IoT button use was sustained throughout the study period. Interviews with housekeeping supervisors and staff demonstrated acceptance of the IoT buttons; actual use was centered around asynchronous communication between supervisors and staff in response to requests to clean restrooms.

Conclusions

An IoT button system is a feasible method to generate on-demand request for restroom cleaning that is easy to deploy and that users will consistently engage with. Data from this system have the potential to enable responsive scheduling for restroom service and anticipate periods of high restroom utilization in a hospital.

Analysis of the Influence of Standard Time Variability on the Reliability of the Simulation of Assembly Operations in Manufacturing Systems

OBJECTIVE

The aim of this article is to analyze the influence of the variability of the standard time in the simulation of the assembly operations of manufacturing systems.

BACKGROUND

Discrete event simulation (DES) has been used to provide efficient analysis during the design of a process or scenario. However, the modeling activities of new configurations face the problem of data availability and reliability when it comes to seeking standard times that are effective in representing the actual process under analysis, especially when the process cannot be monitored.

METHOD

The methods-time measurement (MTM) is used as a source of standard times for simulation. Assembly activities were performed at a Learning Factory facility, which provided the necessary structure for simulating real production processes. Simulation performances using different variability of standard times were analyzed to define the impact of data characteristics.

RESULTS

The MTM standard time presented an error of approximately 5%. The definition of the data variability of standard times and the statistical distribution impacts were shown in the simulation results, with errors above 6% being observed, interfering with the model reliability.

CONCLUSION

Based on the study, to increase the adherence of a simulation to represent a real process, it is recommended to use triangular distributions with central values greater than those established via the MTM for the representation of the standard times of new assembly processes or scenarios using DES.

APPLICATION

The study contributions can be applied in assembly line design, providing a reliable model representing real processes and scenarios.

A Metamodeling Framework for Quantifying Health Damages of Power Grid Expansion Plans

In this paper, we present an analytical framework to establish a closed-form relationship between electricity generation expansion planning decisions and the resulting negative health externalities. Typical electricity generation expansion planning models determine the optimal technology-capacity-investment strategy that minimizes total investment costs as well as fixed and variable operation and maintenance costs. However, the relationship between these long-term planning decisions and the associated health externalities is highly stochastic and nonlinear, and it is computationally expensive to evaluate. Thus, we developed a closed-form metamodel by executing computer-based experiments of a generation expansion planning model, and we analyzed the resulting model outputs in a United States Environmental Protection Agency (EPA) screening tool that approximates the associated human health externalities. Procedural guidance to verify the accuracy and to select key metamodel parameters to enhance its prediction capability is presented. Specifically, the metamodel presented in this paper can predict the resulting health damages of long-term power grid expansion decisions, thus, enabling researchers and policy makers to quickly assess the health implications of power grid expansion decisions with a high degree of certainty.

Emergency Logistics in a Large-Scale Disaster Context: Achievements and Challenges

There is growing research interest in emergency logistics within the operations research (OR) community. Different from normal business operations, emergency response for large scale disasters is very complex and there are many challenges to deal with. Research on emergency logistics is still in its infancy stage. Understanding the challenges and new research directions is very important. In this paper, we present a literature review of emergency logistics in the context of large-scale disasters. The main contributions of our study include three aspects: First, we identify key characteristics of large-scale disasters and assess their challenges to emergency logistics. Second, we analyze and summarize the current literature on how to deal with these challenges. Finally, we discuss existing gaps in the relevant research and suggest future research directions.

Looking at the bigger picture: how the wider health financing context affects the implementation of the Tanzanian Community Health Funds

In Tanzania, the health financing system is extremely fragmented with strategies in place to supplement funds provided from the central level. One of these strategies is the Community Health Fund (CHF), a voluntary health insurance scheme for the informal rural sector. As its implementation has been challenging, we investigated different CHF implementation practices and how these practices and the wider health financing context affect CHF implementation and potentially enrolment. Two councils were purposively selected for this study. Routine data relevant for understanding CHF implementation in the wider health financing context were collected at council and public health facility level. Additionally, an economic costing approach was used to estimate CHF administration cost and analyse its financing sources. Our results showed the importance of considering different CHF implementation practices and the wider health financing context when looking at CHF performance. Exemption policies and healthcare-seeking behaviour influenced negatively the maximum potential enrolment rate of the voluntary CHF scheme. Higher revenues from user fees, user fee policies and fund pooling mechanisms might have furthermore set incentives for care providers to prioritize user fees over CHF revenues. Costing results clearly pointed out the lack of financial sustainability of the CHF. The financial analysis however also showed that thanks to significant contributions from other health financing mechanisms to CHF administration, the CHF could be left with more than 70% of its revenues for financing services. To make the CHF work, major improvements in CHF implementation practices would be needed, but given the wider health financing context and healthcare-seeking behaviours, it is questionable whether such improvements are feasible, scalable and value for money. Thus, our results call for a reconsideration of approaches taken to address the challenges in health financing and demonstrate that the CHF cannot be looked at as a stand-alone system.

How to train your oracle: The Delphi method and its turbulent youth in operations research and the policy sciences

Delphi is a procedure that produces forecasts on technological and social developments. This article traces the history of Delphi's development to the early 1950s, where a group of logicians and mathematicians working at the RAND Corporation carried out experiments to assess the predictive capacities of groups of experts. While Delphi now has a rather stable methodological shape, this was not so in its early years. The vision that Delphi's creators had for their brainchild changed considerably. While they had initially seen it as a technique, a few years later they reconfigured it as a scientific method. After some more years, however, they conceived of Delphi as a tool. This turbulent youth of Delphi can be explained by parallel changes in the fields that were deemed relevant audiences for the technique, operations research and the policy sciences. While changing the shape of Delphi led to some success, it had severe, yet unrecognized methodological consequences. The core assumption of Delphi that the convergence of expert opinions observed over the iterative stages of the procedure can be interpreted as consensus, appears not to be justified for the third shape of Delphi as a tool that continues to be the most prominent one.

Application of Constrained Optimization Methods in Health Services Research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force

BACKGROUND

Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity.

OBJECTIVES

In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available.

CONCLUSIONS

Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods.