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In this paper, we present the first Assignment-Dial-A-Ride problem motivated by a real-life problem faced by medico-social institutions in France. Every day, disabled people use ride-sharing services to go to an appropriate institution where they receive personal care. These institutions have to manage their staff to meet the demands of the people they receive. They have to solve three interconnected problems: the routing for the ride-sharing services; the assignment of disabled people to institutions; and the staff size in the institutions. We formulate a general Assignment-Dial-A-Ride problem to solve all three at the same time. We first present a matheuristic that iteratively generates routes using a large neighborhood search in which these routes are selected with a mixed integer linear program. After being validated on two special cases in the literature, the matheuristic is applied to real instances in three different areas in France. Several managerial results are derived. In particular, it is found that the amount of cost reduction induced by the people assignment is equivalent to the amount of cost reduction induced by the sharing of vehicles between institutions.

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PURPOSE: To compare the interreader agreement of a novel quality score, called the Radiological Image Quality Score (RI-QUAL), to a slighly modified version of the existing Prostate Imaging Quality (mPI-QUAL) score for magnetic resonance imaging (MRI) of the prostate. METHODS: A total of 43 consecutive scans were evaluated by two subspecialized radiologists who assigned scores using both the RI-QUAL and mPI-QUAL methods. The interreader agreement was analyzed using three statistical methods: concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC), and Cohen's kappa. Time needed to arrive at a quality judgment was measured and compared using the Wilcoxon signed rank test. RESULTS: The interreader agreement for RI-QUAL and mPI-QUAL scores was comparable, as evidenced by the high CCC (0.76 vs. 0.77, p = 0.93), ICC (0.86 vs. 0.87, p = 0.93), and moderate Cohen's kappa (0.61 vs. 0.64, p = 0.85) values. Moreover, RI-QUAL assessment was faster than mPI-QUAL (19 vs. 40 s, p = 0.001). CONCLUSION: RI-QUAL is a new quality score that has comparable interreader agreement to the mPI-QUAL score, but with the potential to be applied to different MRI protocols and even different modalities. Like PI-QUAL, RI-QUAL may also facilitate communication about quality to referring physicians, as it provides a standardized and easily interpretable score. Further studies are warranted to validate the usefulness of RI-QUAL in larger patient cohorts and for other imaging modalities.

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This articles provides a short summary of the research topics and latest research results of the European Working Group "Operations Research Applied to Health Services" (ORAHS) organized as an e-conference in Juli 2020 at the University of Vienna, Austria (https://orahs2020.univie.ac.at/). Furthermore, challenges for OR in health care including application areas, decision support systems, general trends, and modelling techniques are briefly illustrated from an European and international perspective by providing selected essential literature reviews.

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This research creates an operations engineering and management methodology to optimize a complex operational planning and coordination challenge faced by sites that perform clinical research trials. The time-sensitive and resource-specific treatment sequences for each of the many trial protocols conducted at a site make it very difficult to capture the dynamics of this unusually complex system. Existing approaches for site planning and participant scheduling exhibit both excessively long and highly variable Time to First Available Visit (TFAV) waiting times and high staff overtime costs. We have created a new method, termed CApacity Planning Tool And INformatics (CAPTAIN) that provides decision support to identify the most valuable set of research trials to conduct within available resources and a plan for how to book their participants. Constraints include (i) the staff overtime costs, and/or (ii) the TFAV by trial. To estimate the site's metrics via a Mixed Integer Program, CAPTAIN combines a participant trajectory forecasting with an efficient visit booking reservation plan to allocate the date for the first visit of every participant's treatment sequence. It also plans a daily nursing staff schedule that is optimized together with the booking reservation plan to optimize each nurse's shift assignments in consideration of participants' requirements/needs.

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Process improvement begins with the process view: understanding patient care from the patient's point of view. Organizations must also clearly articulate for themselves how they define operational excellence so that the tradeoffs taken in process improvement can be clearly made. Constructing a process map allows application of powerful analytical tools, such as Little's law, which in turn uncovers targets for process improvement from the patient's point of view. Often tradeoffs among process performance metrics, such as quality, cost, time, personalization, and innovation, must be made when deciding upon improvements to be made in certain processes.

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The prevailing first-come-first-served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non-urgent patients. This paper develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (1) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non-urgent patients, and (2) increase throughput by 31% with the same service levels and overtime.

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We evaluated the nosocomial outbreak of Middle East Respiratory Syndrome (MERS) Coronavirus (CoV) in the Republic of Korea, 2015, from a healthcare operations management perspective. Establishment of healthcare policy in South Korea provides patients' freedom to select and visit multiple hospitals. Current policy enforces hospitals preference for multi-patient rooms to single-patient rooms, to lower financial burden. Existing healthcare systems tragically contributed to 186 MERS outbreak cases, starting from single "index patient" into three generations of secondary infections. By developing a macro-level health system dynamics model, we provide empirical knowledge to examining the case from both operational and financial perspectives. In our simulation, under base infectivity scenario, high emergency room occupancy circumstance contributed to an estimated average of 101 (917%) more infected patients, compared to when in low occupancy circumstance. Economic patient room design showed an estimated 702% increase in the number of infected patients, despite the overall 98% savings in total expected costs compared to optimal room design. This study provides first time, system dynamics model, performance measurements from an operational perspective. Importantly, the intent of this study was to provide evidence to motivate public, private, and government healthcare administrators' recognition of current shortcomings, to optimize performance as a whole system, rather than mere individual aspects.

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INTRODUCTION: Healthcare leaders need operational strategies that support organizational learning for continued improvement and value generation. The learning health system (LHS) model may provide leaders with such strategies; however, little is known about leaders' perspectives on the value and application of system-wide operationalization of the LHS model. The objective of this project was to solicit and analyze senior health system leaders' perspectives on the LHS and learning activities in an integrated delivery system. METHODS: A series of interviews were conducted with 41 system leaders from a broad range of clinical and administrative areas across an integrated delivery system. Leaders' responses were categorized into themes. FINDINGS: Ten major themes emerged from our conversations with leaders. While leaders generally expressed support for the concept of the LHS and enhanced system-wide learning, their concerns and suggestions for operationalization where strongly aligned with their functional area and strategic goals. DISCUSSION: Our findings suggests that leaders tend to adopt a very pragmatic approach to learning. Leaders expressed a dichotomy between the operational imperative to execute operational objectives efficiently and the need for rigorous evaluation. Alignment of learning activities with system-wide strategic and operational priorities is important to gain leadership support and resources. Practical approaches to addressing opportunities and challenges identified in the themes are discussed. CONCLUSION: Continuous learning is an ongoing, multi-disciplinary function of a health care delivery system. Findings from this and other research may be used to inform and prioritize system-wide learning objectives and strategies which support reliable, high value care delivery.

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PURPOSE: Identifying care needs for newly enrolled or newly insured individuals is important under the Affordable Care Act. Systematically collected patient-reported information can potentially identify subgroups with specific care needs prior to service use. METHODS: We conducted a retrospective cohort investigation of 6,047 individuals who completed a 10-question needs assessment upon initial enrollment in Kaiser Permanente Colorado (KPCO), a not-for-profit integrated delivery system, through the Colorado State Individual Exchange. We used responses from the Brief Health Questionnaire (BHQ), to develop a predictive model for cost for receiving care in the top 25 percent, then applied cluster analytic techniques to identify different high-cost subpopulations. Per-member, per-month cost was measured from 6 to 12 months following BHQ response. RESULTS: BHQ responses significantly predictive of high-cost care included self-reported health status, functional limitations, medication use, presence of 0-4 chronic conditions, self-reported emergency department (ED) use during the prior year, and lack of prior insurance. Age, gender, and deductible-based insurance product were also predictive. The largest possible range of predicted probabilities of being in the top 25 percent of cost was 3.5 percent to 96.4 percent. Within the top cost quartile, examples of potentially actionable clusters of patients included those with high morbidity, prior utilization, depression risk and financial constraints; those with high morbidity, previously uninsured individuals with few financial constraints; and relatively healthy, previously insured individuals with medication needs. CONCLUSIONS: Applying sequential predictive modeling and cluster analytic techniques to patient-reported information can identify subgroups of individuals within heterogeneous populations who may benefit from specific interventions to optimize initial care delivery.

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INTRODUCTION: Care coordination (CC) is an important fulcrum for pursuing a range of health care goals. Current research and policy analyses have focused on aggregated data rather than on understanding what happens within individual cases. At the case level, CC emerges as a complex network of communications among providers over time, crossing and recrossing many organizational boundaries. Micro-level analysis is needed to understand where and how CC fails, as well as to identify best practices and root causes of problems. COORDINATION PROCESS DIAGRAMMING: Coordination Process Diagramming (CPD) is a new framework for representing and analyzing CC arcs at the micro level, separating an arc into its participants and roles, communication structure, organizational structures, and transitions of care, all on a common time line. CONCLUSION: Comparative CPD analysis across a sample of CC arcs identifies common CC problems and potential root causes, showing the potential value of the framework. The analyses also suggest intervention strategies that could be applied to attack the root causes of CC problems, including organizational changes, education and training, and additional health information technology development.

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To ensure that patients receive timely access to care, it has become increasingly important to use existing care provider capacity as efficiently as possible and to make informed capacity planning decisions. To support this decision-making process at a regional cancer center in British Columbia (Canada), we undertook a simulation and optimization based study that investigated the simultaneous impact of the available number of new patient consultation slots, appointment scheduling policies and oncologist specialization configurations on the timeliness of patient access to care and physician workload. The key contribution of this paper is the methodological framework it provides to decision makers who manage specialty clinics to ensure that they are using their resources efficiently and making informed strategic short- and mid-term capacity planning decisions for new patient demand.

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Operating rooms (ORs) in US hospitals are costly to staff, generate about 70 % of a hospital's revenues, and operate at a staffed-capacity utilization of 60-70 %. Many hospitals allocate blocks of OR time to individual or groups of surgeons as guaranteed allocation, who book surgeries one at a time in their blocks. The booking procedure frequently results in unused time between surgeries. Realizing that this presents an opportunity to improve OR utilization, hospitals manually reschedule surgery start times one or two days before each day of surgical operations. The purpose of rescheduling is to decrease OR staffing costs, which are determined by the number of concurrently staffed ORs. We formulate the rescheduling problem as a variant of the bin-packing problem with interrelated items, which are the surgeries performed by the same surgeon. We develop a lower bound (LB) construction algorithm and prove that the LB is at least (2/3) of the optimal staffing cost. A key feature of our approach is that we allow hospitals to have two shift lengths. Our analytical results form the basis of a branch-and-bound algorithm, which we test on data obtained from three hospitals. Experiments show that rescheduling saves significant staffing costs.

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Pragmatic research that compares interventions to improve the organization and delivery of health care may overlap, in both goals and methods, with quality improvement activities. When activities have attributes of both research and quality improvement, confusion often arises about what ethical oversight is, or should be, required. For routine quality improvement, in which the delivery of health care is modified in minor ways that create only minimal risks, oversight by local clinical or administrative leaders utilizing institutional policies may be sufficient. However, additional consideration should be given to activities that go beyond routine, local quality improvement to first determine whether such non-routine activities constitute research or quality improvement and, in either case, to ensure that independent oversight will occur. This should promote rigor, transparency, and protection of patients' and clinicians' rights, well-being, and privacy in all such activities. Specifically, we recommend that (1) health care organizations should have systematic policies and processes for designating activities as routine quality improvement, non-routine quality improvement, or quality improvement research and determining what oversight each will receive. (2) Health care organizations should have formal and explicit oversight processes for non-routine quality improvement activities that may include input from institutional quality improvement experts, health services researchers, administrators, clinicians, patient representatives, and those experienced in the ethics review of health care activities. (3) Quality improvement research requires review by an institutional review board; for such review to be effective, institutional review boards should develop particular expertise in assessing quality improvement research. (4) Stakeholders should be included in the review of non-routine quality improvement and quality improvement-related research proposals. Only by doing so will we optimally leverage both pragmatic research on health care delivery and local implementation through quality improvement as complementary activities for improving health.

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OBJECTIVE: Nowadays, effective scheduling of patients in clinics, laboratories, and emergency rooms is becoming increasingly important. Hospitals are required to maximize the level of patient satisfaction, while they are faced with lack of space and facilities. An effective scheduling of patients in existing conditions is vital for improving healthcare delivery. The shorter waiting time of patients improves healthcare service quality and efficiency. Focusing on real settings, this paper addresses a semi-online patient scheduling problem in a pathology laboratory located in Tehran, Iran, as a case study. METHODS AND MATERIAL: Due to partial precedence constraints of laboratory tests, the problem is formulated as a semi-online hybrid shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem and response surface methodology is used for setting GA parameters. A lower bound is also calculated for the problem, and several experiments are conducted to estimate the validity of the proposed algorithm. RESULTS: Based on the empirical data collected from the pathology laboratory, comparison between the current condition of the laboratory and the results obtained by the proposed approach is performed through simulation experiments. The results indicate that the proposed approach can significantly reduce waiting time of the patients and improve operations efficiency. CONCLUSION: The proposed approach has been successfully applied to scheduling patients in a pathology laboratory considering the real-world settings including precedence constraints of tests, constraint on the number of sites or operators for taking tests (i.e. multi-machine problem), and semi-online nature of the problem.

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BACKGROUND: The purpose of this study was to explore the quality attributes required for effective telemedicine encounters from the perspective of the patient. METHODS: We used a multi-method (direct observation, focus groups, survey) field study to collect data from patients who had experienced telemedicine encounters. Multi-perspectives (researcher and provider) were used to interpret a rich set of data from both a research and practice perspective. RESULTS: The result of this field study is a taxonomy of quality attributes for telemedicine service encounters that prioritizes the attributes from the patient perspective. We identify opportunities to control the level of quality for each attribute (ie, who is responsible for control of each attribute and when control can be exerted in relation to the encounter process). This analysis reveals that many quality attributes are in the hands of various stakeholders, and all attributes can be addressed proactively to some degree before the encounter begins. CONCLUSION: Identification of the quality attributes important to a telemedicine encounter from a patient perspective enables one to better design telemedicine encounters. This preliminary work not only identifies such attributes, but also ascertains who is best able to address quality issues prior to an encounter. For practitioners, explicit representation of the quality attributes of technology-based systems and processes and insight on controlling key attributes are essential to implementation, utilization, management, and common understanding.