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Problem definition: We quantify the increase in productivity in emergency departments (EDs) from increasing nurse staff. We then estimate the associated revenue gains for the hospital and the associated welfare gains for society. The United States is over a decade into the worst nursing shortage crisis in history fueled by chronic underinvestment. To demonstrate to hospital managers and policymakers the benefits of investing in nursing, we clarify the positive downstream effects of doing so in the ED setting. Methodology/results: We use a high-resolution data set of patient visits to the ED of a major U.S. academic hospital. Time-dependent hazard estimation methods (nonparametric and parametric) are used to study how the real-time service speed of a patient varies with the state of the ED, including the time-varying workloads of the assigned nurse. A counterfactual simulation is used to estimate the gains from increasing nursing staff in the ED. We find that lightening a nurse's workload by one patient is associated with a 14% service speedup for every patient under the nurse's care. Simulation studies suggest that adding one more nurse to the busiest 12-hour shift of each day can shorten stays and avert $160,000 in lost patient wages per 10,000 visits. The reduction in service times also frees up capacity for treating more patients and generates $470,000 in additional net revenues for the hospital per 10,000 visits. Extensive sensitivity analyses suggest that our key message—that investing in nursing will more than pay for itself—is likely to hold across a wide range of EDs. Managerial implications: In determining whether to invest in more nursing resources, hospital managers need to look beyond whether payer reimbursements alone are sufficient to cover the up-front costs to also account for the resulting downstream benefits. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: D. K. K. Lee was supported by the National Heart, Lung, and Blood Institute [Grant R01-HL164405]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0039. [ABSTRACT FROM AUTHOR]

Problem definition: Physicians spend more than five hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. Numerous studies have identified the detrimental effects of excessive EHR use and after-hours work, including physician burnout, physician attrition, and appointment delays. However, EHR time is not purely an exogenous factor because it depends on physician usage behavior that could have important operational consequences. Interestingly, prior literature has not considered this topic rigorously. In this paper, we investigate how physicians' workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work. Methodology/results: Our data comprise around 150,000 appointments from 74 physicians from a large Academic Medical Center Family Medicine unit. Our data set contains detailed, process-level time stamps of appointment progression and EHR use. We find that the effect of working on EHR systems depends on whether the work is done before or after an appointment. Pre-appointment EHR work reduces total EHR workload and after-work hours spent on EHR. Post-appointment EHR work reduces after-work hours on EHR but increases total EHR time. We find that increasing idle time between appointments can encourage both pre- and post-appointment EHR work. Managerial implications: Our results not only help us understand the timing and structure of work on secondary tasks more generally but also will help healthcare administrators create EHR workflows and appointment schedules to reduce physician burnout associated with excessive EHR use. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: The research conducted for this paper received partial funding from the Center of Business for Health at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0028. [ABSTRACT FROM AUTHOR]

Problem definition: We consider a surgery sequencing and scheduling problem with uncertain durations of surgeries in the context of an operating theater. From real data collected from a hospital, we observe the common practice, namely, "to follow," in which surgeries are conducted sequentially and immediately one after another, according to a specific schedule. Methodology/results: Based on this practice, we propose a mathematical framework to balance the risk of delay and idling using the punctuality index, which takes into consideration both the probability and intensity of delay and idle time. We develop a computationally efficient procedure based on Benders decomposition to derive exact solutions for the problem. The scheduling problem is solvable in polynomial time when the sequence is given. The framework can also accommodate a robust setting when the underlying probability distribution is not fully available. For practical use, we propose two effective heuristics for sequencing decisions by approximating the model. Using real data, we demonstrate that our framework is significantly better than the risk-neutral and probability-maximizing approaches in both performance and computational efficiency. Moreover, the robust setting can effectively lessen the risk of extremely long delay and idle time, and the heuristics are efficient with only a small sacrifice of performance. Managerial implications: With the to-follow policy, our sequencing and scheduling model describes the actual practice better. The two heuristics can be applied easily and directly to help managers of operating theaters to make decisions. Funding: J. Qi is supported by the Hong Kong Research Grants Council [Grants 26202016, 16212419, and 16204521]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0029. [ABSTRACT FROM AUTHOR]

Accountable Care Organizations (ACOs) are healthcare collaboration networks comprised of hospitals and other healthcare providers. The motivation behind the formation of ACOs is to improve the quality of care while reducing healthcare costs. Despite these commendable goals, hospitals' participation in ACOs remains low; the most significant barrier being the risk associated with joining. Our research tackles this timely and potentially impactful topic by exploring factors that facilitate hospitals' ACO entry decisions. We apply resource dependence theory to explain that competition network characteristics, organizational network characteristics, and internal organizational characteristics mitigate hospitals' financial risk and are therefore critical to ACO participation. Using survival analysis, we test our research hypotheses using longitudinal data from 3114 hospitals. Our findings suggest that hospitals with a high level of competition network centrality are more likely to join an ACO, while hospitals with a high level of competition network tie diversity are less likely to join. In addition, hospitals with experience in joining other healthcare networks, such as Regional Healthcare Information Organizations, or belonging to a large parent group, are inclined to enter an ACO. Lastly, hospitals with a high level of unabsorbed slack are prone to ACO participation. Given that knowledge of hospitals' participation in ACOs is lacking, this research is relevant for practitioners in the US healthcare industry. Specifically, it provides insights for hospital administrators on the critical resources that need to be strengthened before joining an ACO. Our findings also provide practical guidance for healthcare policy makers on how to profile, target, and efficiently promote ACO participation among hospitals. [ABSTRACT FROM AUTHOR]

Problem definition: Do physicians exhibit predictable behavioral bias when making admission control decisions under patient diagnosis uncertainty with stochastic arrivals and lengths of stay? How can we structure feedback to help improve their decision making? Methodology/results: We use a behavioral model to theorize how a diagnosis anchoring and insufficient adjustment heuristic may lead to an over-rationing bias, and we hypothesize when this bias is greatest. We then propose that feedback for rejected patients—above and beyond feedback for admitted patients—is critical for mitigating this bias. This is because feedback for only admitted patients may suffer from a type of path dependency that prevents decision makers from receiving the most helpful disconfirming feedback. We provide evidence supporting these hypotheses using preregistered experiments in which medical students, Amazon Mechanical Turk workers, or Prolific workers manage admissions for simulated hospital units. Managerial implications: Our results (1) illuminate an important anchoring bias in admission control under diagnosis uncertainty, (2) identify rejected-patient feedback as a critical component for mitigating this bias, and (3) provide insight into the circumstances under which these phenomena are likely to be most significant. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [Grant 2022R1F1A1076045]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0194. [ABSTRACT FROM AUTHOR]

Problem definition: Clients and service providers alike often consider one-on-one service delivery to be ideal, assuming, perhaps unquestioningly, that devoting individualized attention best improves client outcomes. In contrast, in shared service delivery, clients are served in batches and the dynamics of group interaction could lead to increased client engagement, which could improve outcomes. However, the loss of privacy and personal connection might undermine engagement. The engagement dynamics in one-on-one and shared delivery models have not been rigorously studied. To the extent that shared delivery may result in comparable or better engagement than one-on-one delivery, service providers in a broad array of contexts may be able to create more value for clients by delivering service in batches. Methodology/results: We conducted a randomized controlled trial with 1,000 patients who were undergoing glaucoma treatment over a three-year period at a large eye hospital. Using verbatim and behavioral transcripts from more than 20,000 minutes of video recorded during our trial, we examine how shared medical appointments (SMAs), in which patients are served in batches, impact engagement. On average, a patient who experienced SMAs asked 33.3% more questions per minute and made 8.6% more nonquestion comments per minute. Because there were multiple patients in an SMA, this increase in engagement at the individual patient level resulted in patients hearing far more comments in the group setting. Patients in SMAs also exhibited higher levels of nonverbal engagement across a wide array of measures (attentiveness, positivity, head wobbling, or "thalai aattam" in Tamil: a South Indian gesture to signal agreement or understanding, eye contact, and end-of-appointment happiness), relative to patients who attended one-on-one appointments. Managerial implications: These results shed light on the potential for shared service delivery models to increase client engagement and thus enhance service performance. Funding: This work was supported by the Wheeler Institute at London Business School (WIBAD Ramdas_Sonmez CFP19), the Institute of Entrepreneurship and Private Capital at London Business School (IIE_3432_2019), Aravind Eye Hospital, and Harvard Business School. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0012. [ABSTRACT FROM AUTHOR]

We study the potential negative impact of imbalanced compensation schemes on firm performance. We analyze data from a radiology workflow platform that connects off‐site radiologists with hospitals. These radiologists select tasks from a common pool, while service level is defined by priority‐specific turnaround time targets. However, imbalances between pay and workload of different tasks could result in higher priority tasks with low pay‐to‐workload ratio receiving poorer service. We investigate this hypothesis, showing turnaround time is decreasing in pay‐to‐workload for lower priority tasks, whereas it is increasing in workload for high‐priority tasks. Crucially, we find evidence of an externality effect: Having many economically attractive tasks with low priority can lead to longer turnaround times for higher priority tasks, increasing their likelihood of delay, thus partially defeating the purpose of the priority classes. Highlights: It has been studied that salaried hospital radiologists working on emergency images tend to sequence images to minimize their work queue. However, the advent of new radiology workflow platforms that aggregate images from multiple hospitals, where independent radiologists choose which emergency and nonemergency images to read, creates novel operational challenges that are becoming pervasive in the sharing economy.We find that independent radiologists behave as previously established when reading emergency images. However, for nonemergency images, they prefer to process images that provide a higher bang‐for‐the‐buck, meaning they lead to a higher monetary payout for the time spent on processing.Crucially, we also find that this preference exerts a negative externality on nonemergency images with high‐operational priority (for example, that can generate bed‐blocking), resulting in longer turnaround times and a higher frequency of delay.The delayed processing exerts a bed‐blocking and patient dissatisfaction cost on client hospitals. We suggest radiology workflow platforms use their information systems capabilities to hide nonemergency, high bang‐for‐the‐buck images from radiologists until the higher priority studies are cleared out. [ABSTRACT FROM AUTHOR]

Organ transplantation is a crucial task in the healthcare supply chain, which organizes the supply and demand for various vital organs. In this regard, dealing with uncertainty is one of the main challengings in designing an organ transplant supply chain. To address this gap, in the present research, a mathematical formulation and solution method is proposed to optimize the organ transplants supply chain under shipment time uncertainty. A possibilistic programming model and simulation-based solution method are developed for organ transplant center location, allocation, and distribution. The proposed mathematical model optimizes the overall cost by considering the fuzzy uncertainty of organ demands and transportation time. Moreover, a novel simulation-based optimization is applied using the credibility theory to deal with the uncertainty in the optimization of this mathematical model. In addition, the proposed model and solution method are evaluated by implementing different test problems. The numerical results demonstrate that the optimal credibility level is between 0.2 and 0.6 in all tested cases. Moreover, the patient's satisfaction rate is higher than the viability rate in the designed organ supply chain. [ABSTRACT FROM AUTHOR]

In emergency departments (EDs), one of the major reasons behind long waiting times and crowding overall is the time it takes to move admitted patients from the ED to an appropriate bed in the main hospital. In "Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments," Chen et al. develop a methodology that can be used to shorten these times by predicting the likelihood of admission for each patient at the time of triage and starting the process of identifying a suitable hospital bed and making preparations for the patient's eventual transfer to the bed right away if the predicted probability of admission is deemed high enough. A simulation study suggests that the proposed methodology, particularly when it takes into account ED census levels, has the potential to shorten average waiting times in the ED without leading to too many false early bed requests. Long boarding times have long been recognized as one of the main reasons behind emergency department (ED) crowding. One of the suggestions made in the literature to reduce boarding times was to predict, at the time of triage, whether a patient will eventually be admitted to the hospital and if the prediction turns out to be "admit," start preparations for the patient's transfer to the main hospital early in the ED visit. However, there has been no systematic effort in developing a method to help determine whether an estimate for the probability of admit would be considered high enough to request a bed early, whether this determination should depend on ED census, and what the potential benefits of adopting such a policy would be. This paper aims to help fill this gap. The methodology we propose estimates hospital admission probabilities using standard logistic regression techniques. To determine whether a given probability of admission is high enough to qualify a bed request early, we develop and analyze two mathematical decision models. Both models are simplified representations and thus, do not lead to directly implementable policies. However, building on the solutions to these simple models, we propose two policies that can be used in practice. Then, using data from an academic hospital ED in the southeastern United States, we develop a simulation model, investigate the potential benefits of adopting the two policies, and compare their performances with that under a simple benchmark policy. We find that both policies can bring modest to substantial benefits, with the state-dependent policy outperforming the state-independent one particularly under conditions when the ED experiences more than usual levels of patient demand. Funding: This work was supported by the National Science Foundation [Grants CMMI-1234212 and CMMI-1635574]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2405. [ABSTRACT FROM AUTHOR]

Problem definition: Algorithm-enabled decision support has an increasingly important role in supporting the day-to-day operations of healthcare organizations. Yet, fully realizing the value of algorithmic decision support lies critically in the opportunity to re-engineer the related processes and redefine roles in ways that make organizations more effective. We study how and when algorithm-enabled process innovation (AEPI) creates value in light of dynamic operational environments (i.e., workload) and behavioral responses to algorithmic predictions (i.e., algorithmic accuracy). Our context is an AEPI effort around a rule-based decision-support algorithm for early detection of sepsis—a costly condition that is the leading cause of death for hospitalized patients. We collaborated with a large U.S.-based hospital system and examined whether AEPI developed for sepsis care (sepsis AEPI) impacts patient mortality and when this impact is stronger or weaker. Methodology/results: We utilize a rich set of clinical and nonclinical data in empirically examining the impact of sepsis AEPI on patient mortality. We leverage the staggered implementation of sepsis AEPI across hospital units and conduct our estimation on a carefully matched sample. The matching utilizes data on patient vitals and the logic behind the algorithm to create a robust comparison group consisting of patient visits for which sepsis AEPI would have triggered an alert if it had been in place. Our empirical analysis shows that sepsis AEPI reduces the likelihood of death from sepsis (45% relative reduction in mortality risk due to sepsis). A higher-than-usual workload and an increase in the average number of inaccurate alert experience at a hospital unit (e.g., an oncology unit, which provides care for cancer patients), in general, reduces the effectiveness of AEPI. We also identify diminishing mortality benefits over prolonged periods of adoption; evaluation of the moderators over time helps explain this diminishing impact. Managerial implications: Our findings suggest that streamlining sepsis-care processes through a predictive algorithm (i.e., algorithm-based monitoring of real-time patient data and providing predictions, streamlined communication channels for coordinating care for a patient with sepsis prediction, and a more standardized process for sepsis diagnosis and treatment) can reduce the loss of life from sepsis. For the 3,739 sepsis patients in our study period, AEPI's benefits would translate to 181 lives saved. We show that such value, however, is sensitive to operational and behavioral factors as the algorithm becomes a routine part of the day-to-day operations of the hospital. Funding: Financial support from University Hospitals is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1226. [ABSTRACT FROM AUTHOR]

Problem definition: Hospital-acquired conditions (HACs) represent undesirable complications that occur during a hospital stay. HACs can compromise patient safety and care outcomes and result in unnecessary socio-economic costs. Although hospitals are expected to reduce the incidence of HACs, few studies have examined the implications of HACs on other clinical outcomes and measures of hospital performance. This study contributes to the literature by exploring the relationship between exposure to a set of target HACs, length of stay (LOS) performance, and 30-day readmission risk. Methodology/results: To estimate the effects of HACs, we conduct econometric analyses using patient-visit-level data for heart attack, heart failure, and pneumonia patients hospitalized in the U.S. state of Florida during 2010–2014. We define LOS performance as the deviation of LOS from the Geometric Mean LOS (GMLOS), a standard LOS set by the Centers for Medicare and Medicaid Services. First, we find that exposure to HACs leads to a 37% increase in the odds of readmission and a 79% increase in LOS. Second, an increase in LOS is associated with a decrease in readmission risk, and this decrease is stronger for patients exposed to HACs. Third, LOS performance mediates the HACs-readmission risk relationship, such that the increase in the readmission risk of a HAC patient can be fully suppressed by the patient's LOS. Fourth, we find that for patients exposed to HACs, the benefits of a longer LOS are almost entirely capitalized when the LOS becomes 65% longer than the GMLOS. Managerial implications: We demonstrate that, when addressing the consequences of HACs, clinicians also face indirectly a trade-off between reducing readmissions and controlling costs. We proffer LOS as a potential mechanism under hospitals' control for mitigating the adverse effects of HACs on readmission risk. Thus, this study offers guidance to clinicians having to decide when to discharge patients with exposure to HACs. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0088. [ABSTRACT FROM AUTHOR]

In order to perform a surgical procedure, substantial numbers of sterile instruments should be readily available to surgeons through the containers referred to as surgical trays and peel packs. After the procedure, all instruments in the opened containers, regardless of whether they have been used or not, must go through the labor-intensive re-sterilization process. Empirical studies have shown that the utilization rate of instruments within trays is very low due to not having optimized tray configurations. Additionally, surgical trays often include instruments that are not likely to be used but are included "just in case", which imposes an additional cost on hospitals through unnecessary instrument re-sterilization. This study is the first analytical attempt to address the issue of configuring surgical trays based on the likelihood of instrument usage through formulating and solving a probabilistic tray optimization problem (PTOP). The PTOP model can serve as a decision support for surgeons by providing them with the tray's probability of being used for optimally configured trays and its associated reprocessing costs. The PTOP is constructed upon an integer non-linear programming model. A decomposition-based heuristic and metaheuristic method coupled with two novel local search algorithms are developed to solve the PTOP. The application of this model can be illustrated through a case study. We discuss how hospitals could benefit from our model in reducing the costs associated with opening trays unnecessarily before a procedure. Additionally, we conducted a risk analysis to estimate the level of confidence for the recommended solution. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID‐19) pandemic has disrupted normal operating procedures at transplant centers. With the possibility that COVID‐19 infection carries an overall 4% mortality rate and potentially a 24% mortality rate among the immunocompromised transplant recipients, many transplant centers considered the possibility of slowing down and even potentially pausing all transplants. Many proposals regarding the need for pausing organ transplants exist; however, much remains unknown. Whereas the impact of the COVID‐19 pandemic on the overall healthcare system is unknown, the potential impact of pausing organ transplants over a period can be estimated. This study presents a model for evaluating the impact of pausing liver transplants over a spectrum of model for end‐stage liver disease‐sodium (MELD‐Na) scores. Our model accounts for two potential risks of a pause: (1) the waitlist mortality of all patients who do not receive liver transplants during the pause period, and (2) the impact of a longer waiting list due to the pause of liver transplants and the continuous accrual of new patients. Using over 12 years of liver transplant data from the United Network for Organ Sharing and a system of differential equations, we estimate the threshold probability above which a decision maker should pause liver transplants to reduce the loss of patient life months. We also compare different pause policies to illustrate the value of patient‐specific and center‐specific approaches. Finally, we analyze how capacity constraints affect the loss of patient life months and the length of the waiting list. The results of this study are useful to decision makers in deciding whether and how to pause organ transplants during a pandemic. The results are also useful to patients (and their care providers) who are waiting for organ transplants. [ABSTRACT FROM AUTHOR]

Problem definition: Delay announcements have become an essential tool in service system operations: They influence customer behavior and network efficiency. Most current delay announcement methods are designed for relatively simple environments with a single service station or stations in tandem. However, complex service systems, such as healthcare systems, often have fork-join (FJ) structures. Such systems usually suffer from long delays as a result of both resource scarcity and process synchronization, even when queues are fairly short. These systems may thus require more accurate delay estimation techniques than currently available. Methodology/results: We analyze a network comprising a single-server queue followed by a two-station FJ structure using a recursive construction of the Laplace–Stieltjes transform of the joint delay distribution, conditioning on customers' movements in the network. Delay estimations are made at the time of arrival to the first station. Using data from an emergency department, we examine the accuracy and the robustness of the proposed approach, explore different model structures, and draw insights regarding the conditions under which the FJ structure should be explicitly modeled. We provide evidence that the proposed methodology is better than other commonly used queueing theory estimators such as last-to-enter-service (which is based on snapshot-principle arguments) and queue length, and we replicate previous results showing that the most accurate estimations are obtained when using our model result as a feature in state-of-the-art machine learning estimation methods. Managerial implications: Our results allow management to implement individual, real-time, state-dependent delay announcements in complex FJ networks. We also provide rules of thumb with which one could decide whether to use a model with an explicit FJ structure or to reduce it to a simpler model requiring less computational effort. Funding: This work was supported by the Dutch Research Council (NWO) Gravitation Programme NETWORKS [Grant 024.002.003], the Israel Ministry of Science and Technology [Grant 880011], and the Israel Science Foundation [Grant 1955/15]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1167. [ABSTRACT FROM AUTHOR]

Trade-offs Caused by Batching Inpatient Admissions from Emergency Departments In "To Batch or Not to Batch? Impact of Admission Batching on Emergency Department Boarding Time and Physician Productivity," Feizi and coauthors tackle the important problem of identifying causes of emergency department (ED) boarding with the goal of identifying a managerial lever to reduce it. They investigate the impact of batching admissions of ED patients. The authors empirically show that batching occurs frequently at the end of shifts when physicians wrap up their tasks. Interestingly, Feizi et al. find a trade-off. Batching improves individual physician productivity, which explains its prevalence. However, it increases boarding times, an outcome that negatively impacts patients and the hospital. A counterfactual analysis comparing empirical results to theoretical queuing models finds that eliminating batching reduces boarding times by 15%. The paper highlights that boarding can be reduced by physicians completing admissions work as it occurs rather than delaying to the end of shift. We study the behavior of batching by discretionary workers in the first stage of a two-stage queuing system and explore the trade-off it causes between their productivity and second stage wait times. Specifically, we focus on the behavior of batching admissions by emergency department (ED) physicians. Using data from a large hospital, we show that the probability of batching admissions is increasing in the hour of an ED physician's shift, and that batched patients experience a 4.7% longer delay from hospital admission to receiving an inpatient bed. Using a mediation analysis, we show that this effect is partially due to the increase in the coefficient of variation of inpatient bed requests caused by batching. However, we also find that batching admissions is associated with an average of 10.0% more patients seen in a shift, and a 2.6 minute reduction in a physician's average throughput time. An important implication of our work is that workers may induce delays in downstream stages, caused by practices that increase their productivity. History: This paper has been accepted for the Operations Research Special Issue on Behavioral Queueing Science. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2335. [ABSTRACT FROM AUTHOR]

Rising healthcare costs force healthcare organizations worldwide to reconsider their business strategies. Hospital managers are tasked with lowering operating costs while upholding the high quality of care, two goals perceived as contradictory by many healthcare professionals. Attempting to contribute towards reaching these goals, the study explores the digitalization of the healthcare supply chain through the adoption of enterprise resource planning (ERP) systems and its impact on hospital supply chain costs. Established technology adoption theories are synthesized to develop a research model that investigates the adoption of hospital ERP systems and its impact on supply chain cost performance. The framework is validated via structural equation modeling (SEM) with data collected from 107 Greek public hospitals. The results indicate that technological and organizational readiness, hospital size, governmental policies, and perceived benefits have a significant influence on the extent of ERP systems adoption. They also demonstrate a statistically significant association between the use of ERP systems and hospital supply chain costs. The present study highlights the influencing factors of ERP systems adoption in hospitals and provides empirical evidence on the resulting cost benefits., This implies greater urgency for hospitals to digitalize their supply chain by implementing and fully exploiting the ERP systems' functionality in order to reach their goal of delivering quality care at a lower cost. [ABSTRACT FROM AUTHOR]

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Medical errors are recognized as major threats to patient safety worldwide. Lack of streamlined communication and an inability to share and exchange data are among the contributory factors affecting patient safety. To address these challenges, blockchain can be utilized to ensure a secure, transparent and decentralized data exchange among stakeholders. In this study, we discuss six use cases that can benefit from blockchain to gain operational effectiveness and efficiency in the patient safety context. The role of stakeholders, system requirements, opportunities and challenges are discussed in each use case in detail. Connecting stakeholders and data in complex healthcare systems, blockchain has the potential to provide an accountable and collaborative milieu for the delivery of safe care. By reviewing the potential of blockchain in six use cases, we suggest that blockchain provides several benefits, such as an immutable and transparent structure and decentralized architecture, which may help transform health care and enhance patient safety. While blockchain offers remarkable opportunities, it also presents open challenges in the form of trust, privacy, scalability and governance. Future research may benefit from including additional use cases and developing smart contracts to present a more comprehensive view on potential contributions and challenges to explore the feasibility of blockchain-based solutions in the patient safety context. [ABSTRACT FROM AUTHOR]

Home care provides personalized medical care and social support to patients within their own homes. Our work proposes a dynamic scheduling framework to assist in the assignment of health practitioners (HPs) to patients who arrive stochastically over time and are heterogeneous with respect to their health requirements, service duration, and region of residence. We model the decision of which patients to assign to HPs as a discrete‐time, rolling‐horizon, infinite‐stage Markov decision process. Due to the curse of dimensionality and the combinatorial structure associated with an HP's travel, we propose an approximate dynamic programming (ADP) approach based on a one‐step policy improvement heuristic. Four policies are investigated: The first two prioritize HP fairness by balancing service and travel times, respectively, while the other two are based on fluid approximations of the system. We show that the first fluid model is optimal if the number of patient arrivals is sufficiently large while the second performs better experimentally; both approaches leverage pricing and decomposition strategies. We compare our framework to more commonly implemented policies—constrained versions of the classical vehicle routing problem—in a simulation study using data collected from a Canadian home care provider. We show that, in contrast to these approaches, by accounting for future uncertainty, substantial cost savings can be obtained while a fewer number of referrals are rejected. We also find that well‐performing policies assign patients to HPs operating within a small set of adjacent regions while considering the number of periods that a patient requires care for. Otherwise, HP workload may not be appropriately balanced over the long‐term even if travel time is minimized. [ABSTRACT FROM AUTHOR]

Problem definition: We investigate how the degree of standardization (DoS) across service chain-belonging units impacts units' performance outcomes. We study this question in the nursing home industry, focusing on the DoS in three operational dimensions—customer mix, service offering, and service delivery—and its impact on three nursing home outcomes: financial performance, clinical outcome, and resident welfare. Academic/practical relevance: All service chains must decide how unique or standardized each of their units should be, but it is unclear how this decision affects units' performance outcomes. We assess standardization strategies in multiple operational dimensions in chain-belonging nursing homes and their impact on important operational outcomes, and generalize our findings to other service industries. Methodology: We use a panel data set covering 11 years (2005–2015) of comprehensive measurements of all nursing homes in the United States. We estimate the relationship between the DoS and important nursing home outcomes using fixed-effect models with instrumental variables and clustered standard errors. Results: We find that chain-belonging nursing homes that (1) customize service delivery and standardize customer mix relative to the chain norm tend to experience improved financial performance; (2) standardize customer mix tend to experience improved clinical outcome; and (3) customize service delivery tend to experience enhanced resident welfare. Managerial implications: To our knowledge, this study is the first systematic multidimensional assessment of multiunit service firms' standardization strategies from a chain perspective. Our results guide nursing home chains in their (1) strategies of within-chain standardization, and (2) investment decisions in acquiring new properties. Our results also provide broader managerial insights for service chains' standardization strategies and merging and acquisition activities. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1111. [ABSTRACT FROM AUTHOR]

Problem definition: Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into or right after overbooked slots. That scheduling decision maximizes the clinic performance, as measured by a weighted sum of all patients' waiting time and the provider's overtime and idle time. However, if a racial group is characterized by a higher no-show risk, then the patients belonging to that racial group will be scheduled into or right after overbooked slots disproportionately to the general population. Academic/Practical Relevance: That scheduling decision is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Thus, the challenge becomes minimizing the schedule cost while avoiding racial disparities. Methodology: Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we analytically study racial disparity in this context. Then, we propose new objective functions that minimize both schedule cost and racial disparity and that can be readily adopted by researchers and practitioners. We develop a race-aware objective, which instead of minimizing the waiting times of all patients, minimizes the waiting times of the racial group expected to wait the longest. We also develop race-unaware methodologies that do not consider race explicitly. We validate our findings both on simulated and real-world data. Results: We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Our race-aware methodology achieves both goals of eliminating racial disparity and obtaining a similar schedule cost as that obtained by the state-of-the-art scheduling method, whereas the race-unaware methodologies fail to obtain both efficiency and fairness. Managerial implications: Our work uncovers that the traditional objective of minimizing schedule cost may lead to unintended racial disparities. Both efficiency and fairness can be achieved by adopting a race-aware objective. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Funding: M. Samorani's work was partly funded by the 2020 Leavey Research Grant. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0999. [ABSTRACT FROM AUTHOR]

The blood supply chain stands out as a crucial component within a healthcare system, which can significantly improve efficiency and save the health system's costs. This paper presents a multi-objective blood supply chain network design problem that aims to reduce the cost of establishing fixed and temporary facilities, transferring blood products, and the amount of shortage. In order to address the shortfall and boost adaptability, lateral freight across hospitals is suggested due to the uncertainty in supply and demand. A novel robust possibilistic mixed-integer linear programming method is proposed in this work in order to deal with distribution and locational decisions. Two well-known solution approaches of lexicographic and Torabi-Hassini methods are then utilized to treat the multi-objectiveness of the robust possibilistic optimization model. Lateral freight between various blood supply chain demands significantly affects load balancing, declining both delivery time and costs. According to the obtained outcomes, the overall delivery time and total cost decrease by 10% and 15%, respectively. Moreover, it is revealed that the lexicographic approach outperforms the Torabi-Hassini method in this research. [ABSTRACT FROM AUTHOR]

In order to perform a surgical procedure, substantial numbers of sterile instruments should be readily available to surgeons through the containers referred to as surgical trays and peel packs. After the procedure, all instruments in the opened containers, regardless of whether they have been used or not, must go through the labor-intensive re-sterilization process. Empirical studies have shown that the utilization rate of instruments within trays is very low due to not having optimized tray configurations. Additionally, surgical trays often include instruments that are not likely to be used but are included “just in case”, which imposes an additional cost on hospitals through unnecessary instrument re-sterilization. This study is the first analytical attempt to address the issue of configuring surgical trays based on the likelihood of instrument usage through formulating and solving a probabilistic tray optimization problem (PTOP). The PTOP model can serve as a decision support for surgeons by providing them with the tray’s probability of being used for optimally configured trays and its associated reprocessing costs. The PTOP is constructed upon an integer non-linear programming model. A decomposition-based heuristic and metaheuristic method coupled with two novel local search algorithms are developed to solve the PTOP. The application of this model can be illustrated through a case study. We discuss how hospitals could benefit from our model in reducing the costs associated with opening trays unnecessarily before a procedure. Additionally, we conducted a risk analysis to estimate the level of confidence for the recommended solution.

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility. [ABSTRACT FROM AUTHOR]

Problem definition: Diabetes is a highly prevalent and expensive chronic disease that affects millions of Americans and is associated with multiple comorbidities. Clinical research has found long-term variation in a patient's glycated hemoglobin levels to be linked with adverse health outcomes, such as increased hospitalizations. Consequently, there is a need for innovative approaches to reduce long-term glycemic variability and efficient ways to implement them. Academic/practical relevance: Although the operations literature has extensively explored ways to manage variability across patients, relatively little attention has been paid to within-patient variability. We draw on the management and healthcare literatures to hypothesize and then show that a key operational lever—continuity of care (CoC)—can be used to reduce glycemic variability, which in turn, improves patient health. In the process, we explore the moderating role of a key demographic characteristic: patient's marital status. We also shed light on an important mechanism through which CoC reduces variability—adherence of patients to prescribed medications—thereby advancing the compliance literature. Academically, our study adds to the understanding of the importance of managing variability (via continuity in service) in settings where customers repeatedly interact with service providers. Methodology: We use a detailed and comprehensive data set from the Veterans Health Administration, the largest integrated healthcare delivery system in the United States. This permits us to control for potential sources of heterogeneity. We analyze more than 300,000 patients—over an 11-year period—with diabetes, a chronic disease whose successful management requires managing glycemic variability. We use an empirical approach to, first, quantify the relationship between CoC and glycemic variability and second, show how this relationship differs based on patient's marital status. Third, we estimate the mediation effect of patients' adherence to medications. Finally, we quantify how glycemic variability mediates the relationship between CoC and three important outcomes. Our findings are validated by extensive robustness checks and sensitivity analyses. Results: We find that CoC is related to reductions in glycemic variability, more so for patients who are not married. However, this reduction is not linear in continuity; we find evidence of curvilinearity but with a sufficiently high stationary point so that benefits almost always accrue, albeit at a diminishing rate. Additionally, we find that one mechanism through which CoC may reduce variability is through patients' adherence to medications. We also find evidence of partial mediation for glycemic variability in the CoC outcomes process chain. Our counterfactual analysis reveals the extent of improvement that enhanced continuity can bring, depending on where it is targeted. Managerial implications: Identifying the process measures through which continuity of care reduces variability is of interest to practitioners and policy makers as it can help design appropriate policies and pathways in terms of both processes and staffing/work allocation. [ABSTRACT FROM AUTHOR]

Problem definition: We consider the quality competition between two hospitals under the plan of one insurer. The insurer aims to maximize the overall achievable quality in the system by selecting either the fee-for-service (FFS) or the bundled payment (BP) scheme to compensate each hospital. The demand, the costs, and the probability of successfully treating patients depend on a hospital's chosen quality. Under such a setting, we use game theoretic models to study the choice of insurer and the impact of payment schemes on the equilibrium qualities of hospitals. Our models seek answers to the following questions. Is BP (FFS) payment scheme always associated with high (low) equilibrium quality? What factors affect the equilibrium qualities of competing hospitals and the choice of the insurer and how? Academic/practical relevance: Competing hospitals under same or different payment models from an insurer is rather common for healthcare delivery in the United States. However, there is a lack of understanding about the impact of payment schemes on quality outcomes from both the hospital's and the insurer's perspective. To the best of our knowledge, ours is the first paper to incorporate competition and the insurer's perspective to study the impact of different payment models on quality. Methodology: Game theory, equilibrium analysis. Results: We show that BP (FFS) is not always associated with high (low) equilibrium quality and that the equilibrium qualities of the two hospitals are strategic complements. We characterize when the insurer might assign the BP to both hospitals, the FFS to both hospitals, or the BP to one and the FFS to the other hospital. Managerial implications: Our findings provide prescriptive guidelines to an insurer choosing payment models. We also show that a specific quality outcome is achievable not by choosing any payment model but by a careful design of its parameters. Our numerical studies show that hospital readmission reduction initiatives are most effective when run in conjunction with cost reduction initiatives. [ABSTRACT FROM AUTHOR]

Abstract Background Patient satisfaction is a multi-dimensional concept that provides insights into various quality aspects in healthcare. Although earlier studies identified a range of patient and provider-related determinants, their relative importance to patient satisfaction remains unclear. Methods We used a tree-based machine-learning algorithm, random forests, to estimate relationships between patient and provider-related determinants and satisfaction level in two of the main patient journey stages, registration and consultation, through survey data from 411 patients at a hospital in Abu Dhabi, UAE. Radar charts were also generated to determine which type of questions—demographics, time, behaviour, and procedure—influence patient satisfaction. Results Our results showed that the ‘age’ attribute, a patient-related determinant, is the leading driver of patient satisfaction in both stages. ‘Total time taken for registration’ and ‘attentiveness and knowledge of the doctor/physician while listening to your queries’ are the leading provider-related determinants in each model developed for registration and consultation stages, respectively. The radar charts revealed that ‘demographics’ are the most influential type in the registration stage, whereas ‘behaviour’ is the most influential in the consultation stage. Conclusions Generating valuable results, the random forest model provides significant insights on the relative importance of different determinants to overall patient satisfaction. Healthcare practitioners, managers and researchers can benefit from applying the model for prediction and feature importance analysis in their particular healthcare settings and areas of their concern.

Operating rooms (ORs) are key to a hospital's profitability, and increasing their benefits helps decrease surgical costs, minimize patient waiting times, and boost the number of patients admitted for surgery. This study presents a mixed-integer linear programming (MILP) model to address the issue of treating inpatients in operating rooms waiting for admission to the referral department. Moreover, it proposes a practical algorithm that minimizes overtime costs in operating rooms and increases patient satisfaction. Our model uses an integrated machine learning - tabu search (ML-TS) method. The proposed model uses differential ML and heuristic rules to achieve enhanced performance and scalability in solving complex optimization problems. A fairness policy is implemented, combined with an optimization method, to guide patient prioritization. This approach considers factors like urgency, waiting times, or specific patient needs and helps determine the order in which patients are scheduled for surgery. The outcomes imply that patient adaptability has led to savings ranging from 50% to 65% per surgery. Additionally, the Machine Learning-Tabu Search (ML-TS) algorithm has increased performance by about 35%. These algorithms also let the OR manager define the size of the uncertainty set and regulate overtime costs while accommodating surgeons' preferences as managerial implications insight. It adeptly navigates the solution space, identifies feasible solutions, and accelerates convergence. [ABSTRACT FROM AUTHOR]

Maitha Alkaabi,1 Mecit Can Emre Simsekler,1 Raja Jayaraman,1 Abdulqader Al Kaf,1 Hussam Ghalib,2 Dima Quraini,2 Samer Ellahham,2 E Murat Tuzcu,2 Kudret Demirli3 1Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; 2Heart and Vascular Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates; 3Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, CanadaCorrespondence: Mecit Can Emre SimseklerDepartment of Industrial and Systems Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab EmiratesTel +9712 501 8410Fax +971 2 447 2442Email emre.simsekler@ku.ac.aePurpose: Waste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes.Methods: This study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi.Results: VSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste.Conclusion: As behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.Keywords: lean healthcare, value stream map, system modelling techniques, waste identification, kaizen, system approaches, healthcare operations

Although medical research has addressed the clinical management of chronic opioid users, little is known about how operational interventions shortly after opioid initiation can impact a patient's likelihood of long-term opioid use. Using a nationwide U.S. database of medical and pharmaceutical claims, we investigate the care delivery process at the most common entry point to opioid use: the primary care setting. For patients who return to primary care for a follow-up appointment within 30 days of opioid initiation, we ask who should revisit and potentially revise the opioid-based treatment plan: the initial prescriber (provider concordance) or an alternate clinician (provider discordance)? First, using a fully controlled logistic model, we find that provider discordance reduces the likelihood of long-term opioid use 12 months after opioid initiation by 31% (95% Confidence Interval: [18%, 43%]). Both the instrumental variable analysis technique and propensity-score matching (utilizing the minimum-bias estimator approach) account for omitted variable bias and indicate that this is a conservative estimate of the true causal effect. Second, looking at patient activities immediately after the follow-up appointment, we find that this long-term reduction is at least partially explained by an immediate reduction in opioids prescribed after the follow-up appointment. Third, the data suggest that the benefit associated with provider discordance remains significant regardless of whether the patient's initial prescriber was their regular primary care provider or another clinician. Overall, our analysis indicates that systematic, operational changes in the early stages of managing new opioid patients may offer a promising, and hitherto overlooked, opportunity to curb the opioid epidemic. This paper was accepted by David Simchi-Levi, healthcare management. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is critical for data-driven decision making to increase resource utilization, operational performance, and service quality in various industry domains, particularly in healthcare. Using AI in healthcare operations can significantly improve treatment outcomes and enhance patient satisfaction while reducing costs. In this paper, we propose a multi-stage framework to build an AI-based decision support tool that can predict the 5-year survivability of lung cancer patients. We evaluate the proposed framework using the Surveillance, Epidemiology, and End Results dataset pertaining to the 1973–2015 period obtained from the National Institutes of Health. The first stage entails data preprocessing and target creation. The second stage applies six AI algorithms with feature selection through Particle Swarm Optimization and hyperparameter tuning with Cross-Validation. These Algorithms include Logistic Regression, Decision Trees, Random Forests (RF), Adaptive Boosting (AdaBoost), Artificial Neural Network, and Naïve Bayes. The results show that RF and AdaBoost models yield an AUC rate of 0.94 and outperform the other models. Stage 3 utilizes permutation importance to interpret the RF and AdaBoost models and applies Tree-based Augmented Naïve Bayes to gain insights regarding the interrelations among important features. The results of Stage 3 delineate that the number of lymph nodes containing metastases), the number of tumors that patients have had in their lifetime, the patient's age, and the microscopic composition of cells rank among the topmost important features and can significantly impact patient survivability. We think this study has significant practical implications in helping physicians predict prognosis and develop treatment plans for lung cancer patients. [ABSTRACT FROM AUTHOR]

Existing literature suggests that hospital occupancy matters for quality of care, as measured by various patient outcomes. However, estimating the causal effect of increased hospital busyness on in-hospital mortality remains an elusive task due to statistical power challenges and the difficulty in separating shocks to occupancy from changes in patient composition. Using data from a large public hospital system in Mexico, we estimate the impact of congestion on in-hospital mortality by exploiting the shock in hospitalizations induced by the 2009 H1N1 pandemic, instrumenting hospital admissions due to acute respiratory infections (ARIs) with measures of ARI cases at nearby healthcare facilities as a proxy for the size of the local outbreak. Our instrumental-variables estimates show that a 1% increase in ARI admissions in 2009 led to a 0.25% increase in non-ARI in-hospital mortality. We show that these effects are nonlinear in the size of the local outbreak, consistent with the existence of tipping points. We further show that effects are concentrated at hospitals with limited infrastructure, suggesting that supply-side policies that improve patient assignment across hospitals and strategically increase hospital capacity could mitigate some of the negative impacts. We discuss managerial implications, suggesting that up to 25%–30% of our estimated deaths at small and non-intensive-care-unit hospitals could have been averted by reallocating patients to reduce congestion. This paper was accepted by Carri Chan, healthcare management. [ABSTRACT FROM AUTHOR]

Type 2 diabetes is a chronic disease that affects millions of Americans and puts a significant burden on the healthcare system. The medical community sees screening patients to identify and treat prediabetes and diabetes early as an important goal; however, universal population screening is operationally not feasible, and screening policies need to take characteristics of the patient population into account. For instance, the screening policy for a population in an affluent neighborhood may differ from that of a safety‐net hospital. The problem of optimal diabetes screening—whom to screen and when to screen—is clearly important, and small improvements could have an enormous impact. However, the problem is typically only discussed from a practical viewpoint in the medical literature; a thorough theoretical framework from an operational viewpoint is largely missing. In this study, we propose an approach that builds on multiple methods—partially observable Markov decision process (POMDP), hidden Markov model (HMM), and predictive risk modeling (PRM). It uses available clinical information, in the form of electronic health records (EHRs), on specific patient populations to derive an optimal policy, which is used to generate screening decisions, individualized for each patient. The POMDP model, used for determining optimal decisions, lies at the core of our approach. We use HMM to estimate the cohort‐specific progression of diabetes (i.e., transition probability matrix) and the emission matrix. We use PRM to generate observations—in the form of individualized risk scores—for the POMDP. Both HMM and PRM are learned from EHR data. Our approach is unique because (i) it introduces a novel way of incorporating predictive modeling into a formal decision framework to derive an optimal screening policy; and (ii) it is based on real clinical data. We fit our model using data on a cohort of more than 60,000 patients over 5 years from a large safety‐net health system and then demonstrate the model's utility by conducting a simulation study. The results indicate that our proposed screening policy outperforms existing guidelines widely used in clinical practice. Our estimates suggest that implementing our policy for the studied cohort would add one quality‐adjusted life year for every patient, and at a cost that is 35% lower, compared with existing guidelines. Our proposed framework is generalizable to other chronic diseases, such as cancer and HIV. [ABSTRACT FROM AUTHOR]

We study a generalized operating room planning and scheduling (GORPS) problem at the Toronto General Hospital (TGH) in Ontario, Canada. GORPS allocates elective patients and resources (i.e., operating rooms, surgeons, anesthetists) to days, assigns resources to patients, and sequences patients in each day. We consider patients' due‐date, resource eligibility, heterogeneous performances of resources, downstream unit requirements, and lag times between resources. The goal is to create a weekly surgery schedule that minimizes fixed‐ and over‐time costs. We model GORPS using mixed‐integer and constraint programming models. To efficiently and effectively solve these models, we develop new' multi‐featured logic‐based Benders decomposition approaches. Using data from TGH, we demonstrate that our best algorithm solves GORPS with an average optimality gap of 2.71% which allows us to provide our practical recommendations. First, we can increase daily OR utilization to reach 80%—25% higher than the status quo in TGH. Second, we do not require to optimize for the daily selection of anesthetists—this finding allows for the development of effective dominance rules that significantly mitigate intractability. Third, solving GORPS without downstream capacities (like many papers in literature) makes GORPS easier to solve, but such OR schedules are only feasible in 24% of instances. Finally, with existing ORs' safety capacities, TGH can manage 40% increase in its surgical volumes. We provide recommendations on how TGH must adjust its downstream capacities for varying levels of surgical volume increases (e.g., current urgent need for more capacity due to the current Covid‐19 pandemic). [ABSTRACT FROM AUTHOR]

We analyzed the planning problem for HIV screening, testing, and care. This problem consists of determining the optimal fraction of patients to be screened in every period as well as the optimum staffing level at each part of the healthcare system to maximize the total health benefits to the patients measured by quality-adjusted life-years (QALYs) gained. We modeled this problem as a nonlinear mixed integer programming program comprising disease progression (the transition of the patients across health states), system dynamics (the flow of patients in different health states across various parts of the healthcare delivery system), and budgetary and capacity constraints. We applied the model to the Greater Los Angeles (GLA) station in the Veterans Health Administration system. We found that a Centers for Disease Control and Prevention recommended routine screening policy in which all patients visiting the system are screened for HIV irrespective of risk factors may not be feasible because of budgetary constraints. Consequently, we used the model to develop and evaluate managerially relevant policies within existent capacity and budgetary constraints to improve upon the current risk based screening policy of screening only high risk patients. Our computational analysis showed that the GLA station can achieve substantial increase (20% to 300%) in the QALYs gained by using these policies over risk based screening. The GLA station has already adapted two of these policies that could yield better patient health outcomes over the next few years. In addition, our model insights have influenced the decision making process at this station.

Balancing nurse workload while scheduling outpatient chemotherapy appointments under uncertainty is a challenging problem. In this study, a two-stage stochastic mixed-integer programming model is proposed for setting appointment times of patients and assigning patients to chairs and nurses without distorting the workload balance among nurses. The uncertainty in pre-medication and infusion durations is considered. The objective function of the model minimizes the expected weighted sum of patient waiting time and nurse overtime. Computational experiments are conducted based on data from an outpatient chemotherapy unit of a large hospital. The trade-off between the two competing criteria of the study is investigated. The relationship between the numbers of chairs and nurses in the clinic with the performance measures is examined. The benefit of considering uncertainty is assessed by calculating the value of stochastic solution. [ABSTRACT FROM AUTHOR]

We partnered with a leading US academic medical center to empirically examine the impact of operational process changes designed to improve preoperative flow of patients through the perioperative environment. We focus on the implementation of centralized decision‐making and the introduction of an information technology (IT)‐enabled intraoperative prompt, on efficiency, as measured by preoperative patient processing time. We analyze over 33,000 individual surgical cases in a unique field experimental setting to conduct an empirical investigation of the effects of each intervention on patients' time spent in preoperative processing. To identify the causal effect of each process change, we leverage our field experimental research design and cast our analyses in the difference‐in‐differences modeling framework. We compare the preoperative patient processing time of two distinct patient groups, a treatment group that is impacted by the implemented operational changes and a control group that was not impacted by the changes, before and after each process change. Our results suggest a 3.4% reduction in preoperative processing time with only centralized decision‐making in place, yet suggest a 10.8% reduction in preoperative processing time when centralized decision‐making is paired with the IT‐enabled intraoperative prompt. We also find evidence of a complementarity effect between our process changes and surgeon prior process experience. Our study contributes to the healthcare operations literature by demonstrating the benefits of coordinated information flow within the patient supply chain. We offer insights for hospital managers and healthcare operations scholars alike on the role of information coordination in improving preoperative patient flow with minimal impact on existing resources and staff. [ABSTRACT FROM AUTHOR]

Inpatient flow management plays a critical role in care delivery, patient outcomes, and hospital operational and financial costs. Modeling and performance analysis of inpatient flow present unique features and challenges that differ from operations in other service industries. In this study, we review recent modeling and analytical advances in the setting of inpatient flow management, with a particular focus on service time models motivated from the observations of inpatient discharges. We first compare two new service time models developed to capture the time‐of‐day inpatient flow dynamics, and reveal interesting connections between the two models. We then review analytical methods developed to analyze systems with the new service time models. Based on one method, which is amenable to a one‐dimensional exact analysis under certain conditions, we further introduce its approximations that have explicit analytical forms and enable efficient computations in large systems. In particular, we showcase how to leverage a powerful tool, Stein's method framework, in the hospital setting for steady‐state approximations and characterizing error bounds. We conclude this study by a literature review on other important aspects in inpatient flow management and propose future research directions, from both the modeling and analytical perspectives. [ABSTRACT FROM AUTHOR]

Background: Patient satisfaction is a multi-dimensional concept that provides insights into various quality aspects in healthcare. Although earlier studies identified a range of patient and provider-related determinants, their relative importance to patient satisfaction remains unclear.Methods: We used a tree-based machine-learning algorithm, random forests, to estimate relationships between patient and provider-related determinants and satisfaction level in two of the main patient journey stages, registration and consultation, through survey data from 411 patients at a hospital in Abu Dhabi, UAE. Radar charts were also generated to determine which type of questions-demographics, time, behaviour, and procedure-influence patient satisfaction.Results: Our results showed that the 'age' attribute, a patient-related determinant, is the leading driver of patient satisfaction in both stages. 'Total time taken for registration' and 'attentiveness and knowledge of the doctor/physician while listening to your queries' are the leading provider-related determinants in each model developed for registration and consultation stages, respectively. The radar charts revealed that 'demographics' are the most influential type in the registration stage, whereas 'behaviour' is the most influential in the consultation stage.Conclusions: Generating valuable results, the random forest model provides significant insights on the relative importance of different determinants to overall patient satisfaction. Healthcare practitioners, managers and researchers can benefit from applying the model for prediction and feature importance analysis in their particular healthcare settings and areas of their concern. [ABSTRACT FROM AUTHOR]

This paper studies the capacity reallocation in a hierarchical medical ecosystem via sinking high-quality resource from high-level hospitals to low-level hospitals. To facilitate the capacity sinking, we develop two payment schemes: fee-for-capacity (FFC) and performance payment (PP). Under the FFC scheme, the low-level hospital always pays a unit capacity sinking price to the high-level hospital, whereas under the PP scheme, the reallocation price is paid contingent on the increased patient visits at the low-level hospital due to capacity sinking. By considering the profit- and utility-maximizing behaviors of strategic parties, we build a four-stage Stackelberg sequential game model within a queuing framework to derive the equilibrium results in terms of the low-level hospital's capacity, the high-level hospital's capacity sinking rate, and the funder's capacity sinking price. In the absence of funder's coordination, it is shown that any increase in sinking price always reduces the capacity sinking rate. In the presence of funder's coordination, we find that: (1) the payment schemes under study will not alter the efficiency or coordination of the overall healthcare systems; (2) for the setting with a high perceived value by patients, under each payment scheme, the capacity sinking program is efficient to increase the high-level hospital's profit and the social welfare as well, but it lowers the patient visit rate at the low-level hospital; (3) for the setting with a higher difference between the patients' perceived values at the two levels of hospitals, the capacity sinking program is efficient to increase the patient visit rate at the low-level hospital and the social welfare as well, but it sacrifices the high-level hospital's profit. Finally, numerical studies provide more useful managerial insights. [ABSTRACT FROM AUTHOR]

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility.

Functional Resonance Analysis Method (FRAM) is a function-based approach to model complex socio-technical systems and to manage variability. The current FRAM related tools are unable to capture qualitative and quantitative characteristics of variability as well as temporal variations. This study presents in detail a dynamic FRAM-based tool, which is called DynaFRAM. It is introduced to address the variability-related deficiencies of the FRAM related tools. It aims to capture variability in complex operations. It is a dynamic tool developed to capture time related variations in complex operations. This increases the attractiveness of the DynaFRAM for complex operations where specialists and practitioners make decisions in complicated situations. The ability of the DynaFRAM is demonstrated by examining a healthcare related case study. Although the ability of the DynaFRAM is assessed through capturing variations in healthcare operations, it can be applied to other domains in a similar manner. • The DynaFRAM is a dynamic FRAM-based tool. • It is able to captures different characteristics of variability. • It facilitates understanding and analysis of variability in complex operations.

This research develops a methodology for making process improvements that can sustain over time. Working with caregivers at a large U.S. hospital over 3 years, we redesign a process for educating kidney transplant patients with instructions for post‐surgical care. Adopting an intervention‐based research (IBR) framework and based on our actions to overcome challenges in implementation and sustainment of the redesign, we revise the current understanding of organizational learning theory. Follow‐up observations after our intervention show that the process improvements at the hospital are sustained. We supplement the IBR with quantitative analyses and provide evidence of improvements in health outcomes and satisfaction levels of patients associated with the redesign. These analyses are based on difference‐in‐difference estimations using data from transplant patients, including a control group from other transplant units. Overall, our research specifies a methodology for implementing sustainable process improvements, particularly in high interaction service environments such as healthcare delivery, and identifies refinements to organizational learning theory, especially for such environments. [ABSTRACT FROM AUTHOR]