This paper addresses the problem of correcting medical coding errors with respect to some coding recommendations. The problem consists in clustering medical codings and determining for each cluster the set of features to correct in order to maximize the financial benefits subject to coding correction effort constraints. For this purpose, we model the coding recommendation as a disjunction of hypercubes and introduce the concept of correction sets. A mixed integer linear programming model is then proposed to assign medical codes to correction sets in order to maximize the financial benefits. The miscoding is then explained by characterizing optimal clusters with association rules and coding error distribution. A case study on patient stays associated with malnutrition-related ICD codes is presented, and the performance of the proposed methodology is assessed in regard to the current coding staff practice. A significant increase in health services reimbursement is achieved with a limited number of subjects’ features reviewed. Note to Practitioners—Medical miscoding has a significant negative impact on hospitals with a financial loss for under coding and a penalty for over coding. Whether a medical review is necessary for all descriptive features of a miscoded subject? Is it possible to reduce unnecessary medical reviews without compromising the goal of increasing hospital financial benefits? This article attempts to answer these questions with a data-driven optimization approach to determine a limited number of miscoding clusters and the set of features to review for each in order to best balance the financial benefits and the medical review workload. The application to a real-life case study leads to a significant increase in hospital fiscal revenue of nearly 6,992,489.69 €, while reviewing only a small number of descriptive features (5293 out of 22056 features, or 24% of features). Causes are also provided for each discovered coding error subtype to ameliorate medical coders’ coding practices. Furthermore, the proposed approach allows the decision-maker to balance the cost-benefit and the requirement of public health institutions (i.e., miscoding rate).

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach.Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate.

Contrary to event traces considered in traditional process mining literature, this paper addresses the problem of optimal process mining of traces of events and attributes associated with transitions. The problem is formally defined with rigorous description of the input event logs, the output process model, the event game specifying the images of traces in the model, and a non standard quality metric termed relevance for both the model and all model components. A dynamic programming algorithm is proposed to determine the optimal event game of each trace for a given process model. A multi-start local optimization algorithm built on an original concept of marginal relevance measure is developed for process model optimization. The proposed algorithm is shown to outperform benchmark algorithms on 40 generated test instances and be able to produce near optimal process model with an optimality gap of less than 4.46%. Results of this paper are also applied to a real case study of the care pathways of sarcoma patients. The event log representation is shown to be able to describe accurately the impact of the health state on the care pathways with only minor model relevance degradation. The proposed approach is shown to be able to generate process model at various precision levels and to compare the care pathways of cancer patients. It is also shown to generate better process model than the widely used process mining tools Disco and DFvM on both our relevance and the traditional fitness quality metrics.Note to Practitioners—This paper is motivated by our collaboration with the French cancer centre (Centre Léon Bérard) on data-driven modeling of sarcoma patient care pathways. The primary goal is to investigate the impact of patient health state such as cancer progression on the care pathways. We achieve this by original representation of care pathways by traces of events interleaved by health states. The original concept of “relevance” clearly measures the importance of each element in the process model. The faithfulness of the process model and its complexity can be easily controlled by precision parameters including least significance level of each model element and the number of layers of the model. A case study of Sarcoma patients is presented to show the importance of our care pathway representation, the superiority of our process mining algorithm, the difference of care pathways of four different patient management strategies, and how the health condition intervenes in different strategies.

We are pleased to present this Special Issue of IEEE Transactions on Automation Science and Engineering (TASE), which includes 11 extended articles selected from the technical program of the 17th International Conference on Automation Science and Engineering (CASE 2021). CASE 2021, held on August 23–27, 2021, at the Congress Center of Lyon, France, was primarily a face-to-face conference with online participation for those who could not travel to the beautiful and lively city of Lyon. CASE, as an offspring of TASE, is the flagship automation conference of the IEEE Robotics and Automation Society and constitutes the primary forum for cross-industry and multidisciplinary research in automation. Its goal is to provide a broad coverage and dissemination of foundational research in automation among researchers, academics, and practitioners. The theme of CASE 2021 was Data-Driven Automation.

This paper is devoted to the management of advance admission requests for obstetric care. Pregnant women in China select one hospital and request admission for both antenatal and postnatal care after nine weeks of pregnancy. Schedulers must make the admission decision instantly based on the availability of the most critical resource, that is, hospital beds for postnatal care. The random delay between admission requests and postnatal care has created a distinct advance admission control problem. To address this issue, we propose a basic model that assumes a unit bed requirement for one day. Each admission generates a unit of revenue and each unit of overcapacity use incurs an overcapacity cost. With the objective of maximizing the expected net revenue, we establish an optimal policy for unlimited requests, that is, an expected arrival time quota (EATQ) policy that accepts a fixed quota of advance admission requests with the same expected date of confinement. We then propose an extended model for general capacity requirements. Using the Poisson approximation, we establish the optimality of the EATQ policy, which is shown to be solvable by a simple linear programming model. We compare the numerical results from the different policies and conduct a sensitivity analysis. The EATQ policy is demonstrated to be the best option in all test instances and notably outperforms the current admission rules used in hospitals, which usually accept admission requests according to some empirical monthly quota of the expected delivery month. The Poisson approximation is shown to be effective for determining the optimal EATQ policy for both stationary and nonstationary arrivals. Summary of Contribution: First, this paper investigates the advance admission control problem for obstetric care. Pregnant women in China choose one hospital and request admission for both antenatal and postnatal care after nine weeks of pregnancy but the most critical resource is hospitalization beds needed for postnatal care. The random delay between admission request and postnatal care makes the problem unique and challenging to solve. It belongs to the scope of computing and operations research. Second, this paper formulates a dynamic programming model, analyzes the structural properties of the optimal control policy, and finally proposes a mathematical programming model to determine the optimal quota. Numerical experiments show the validity of the proposed approach. It covers the research contents of theories on dynamic stochastic control, mathematic programming model, and experiments. Moreover, this paper is motivated by the practical problem (advance admission control) in obstetric units of Shanghai. Using these optimality properties, solution approaches, and numerical results, this paper provides guidance on how to manage advance obstetric admission requests.

The growing number of elderly people (EP) has become one of the most important problems in recent years. This part of the population is often dependent and does not tolerate environmental changes well, so long-term care (LTC) structure assignments should be well prepared. This article proposes a stochastic optimization approach to solve the LTC structure assignment problem for a population of EP while taking into account health state changes and uncertain future demand. The objective of this approach is to make the best assignment decision among several structures or to wait for a better solution if no suitable structure is available. The model is validated and calibrated on the basis of stakeholder objectives and territorial special features through simulation. We propose a numerical analysis based on both quantitative and qualitative analyses to compare our model with simple assignment policies.Note to Practitioners—The increase in the elderly and dependent population and the lack of a long-term structure to address this aging population make it difficult to assign EP to the appropriate LTC. This decision often determines the quality of the end of life. Several parameters must be taken into account to choose the best solution: the health situation, geographic location, and financial resources. If the elderly person stays too long in a situation not suited to his/her condition, then his/her risk of a decline in health is significant. The dilemma is whether to choose: 1) to wait to have the structure most adapted to the health of the state or 2) to assign the elderly person to the most suitable structure available. We propose a stochastic optimization approach to help stakeholders assign EP to an LTC structure in a given territory. The model uses projections of the health state of the population over a time horizon to make decisions and considers elderly care requirements and characteristics, such as geographic compatibility and financial resources. The model performs better than a greedy algorithm (based on the first-in-first-out approach) and an optimization model without time projections.

Medical test selection is a recurring problem in health prevention and consists of proposing a set of tests to each subject for diagnosis and treatment of pathologies. The problem is characterized by the unknown risk probability distribution across the population and two contradictory objectives: minimizing the number of tests and giving the medical test to all at-risk populations. This article sets this problem in a general framework of chance-constrained medical test rationing with unknown subject distribution over an attribute space and unknown risk probability but with a given sample population. A new approach combining decision-tree and Bayesian inference is proposed to allocate relevant medical tests according to the subjects’ profile. Case studies on screening of hypertension and diabetes are conducted, and the performance of the proposed approach is evaluated. Significant savings on unnecessary tests are achieved with limited numbers of subjects needing but not receiving necessary tests.Note to Practitioners—Whether a medical test is needed for all subjects in health prevention? Is it possible to reduce unnecessary tests without jeopardizing the goal of screening at-risk populations? This article attempts to answer these questions by proposing a data-driven approach combining decision trees for subject profiling, Bayesian inference for unknown probability distribution estimation, and combinatorial optimization for test allocation. The application of this approach to a real-case study reduces the number of electrocardiogram (ECG) tests by 90% while keeping the number of hypertensive subjects needing but not receiving ECG tests small (five out of 230). A significant cut of unnecessary tests is also achieved in a second case study of diabetes screening. This approach allows decision-makers to better balance the cost-saving and the level of public health objective. Furthermore, the combination with decision trees makes the practical implementation quite straightforward.

This article addresses the admission control of a loss queueing network, or shortly loss network, of$N$parallel multiserver stations with no waiting rooms.$M$classes of customers arrive at a Poisson rate and require an exponential service time with common mean. A customer can be served by any free server of a station in the set of stations determined by the class of the customer. Admission of a customer to a station brings different rewards that depend on customer class and the preference order of that station. Our objective is to find the optimal admission control policy that maximizes the average reward. We adopt a research strategy that evolves from simpler networks to more complicated ones. First, we consider a one-station$M$-class loss network whose results form a fundamental basis for the analysis of more complicated networks. Second, we consider a two-station$M$-class loss network for which we establish the existence of an optimal threshold admission policy. Finally, we consider a general$N$-station$M$-class loss network and propose an iterative approximation policy (IAP) and a mixed-integer linear programming model that is proven to compute an upper bound. We demonstrate the near-optimal performance of the proposed IAP and the tightness of the upper bound on several numerical instances motivated by real-life networks, such as healthcare emergency service networks and emergency call centers. Note to Practitioners—This article is mainly motivated by real-time scheduling of emergency service networks, such as emergency healthcare and telecommunication networks. In such networks, emergent demands need to be served promptly and are lost otherwise. They can be served by multiple hospitals/operator groups but have their own preferences. The decision maker has to decide dynamically whether to keep the capacity of a hospital/operator for its own customers to meet customer preference or serve the incoming demand to improve the resource utilization. We set the problem as the admission control of a multiclass multistation loss network, study the properties of the optimal policy, and propose an iterative approximation policy that is proven to be near-optimal for large-size loss networks. The proposed policy is found to improve by up to 16% and 52% the no-control policy admitting the customer to the most preferred available station and the no-overflow policy with admission to only the most preferred station, respectively. Managerial insights and extensions are also discussed.

This article describes two versions of the chance-constrained stochastic bin-packing (CCSBP) problem that consider item-to-bin allocation decisions in the context of chance constraints on the total item size within the bins. The first version is a stochastic CCSBP (SP-CCSBP) problem, which assumes that the distributions of item sizes are known. We present a two-stage stochastic mixed-integer program (SMIP) for this problem and a Dantzig–Wolfe formulation suited to a branch-and-price (B&P) algorithm. We further enhance the formulation using coefficient strengthening and reformulations based on probabilistic packs and covers. The second version is a distributionally robust CCSBP (DR-CCSBP) problem, which assumes that the distributions of item sizes are ambiguous. Based on a closed-form expression for the DR chance constraints, we approximate the DR-CCSBP problem as a mixed-integer program that has significantly fewer integer variables than the SMIP of the SP-CCSBP problem, and our proposed B&P algorithm can directly solve its Dantzig–Wolfe formulation. We also show that the approach for the DR-CCSBP problem, in addition to providing robust solutions, can obtain near-optimal solutions to the SP-CCSBP problem. We implement a series of numerical experiments based on real data in the context of surgery scheduling, and the results demonstrate that our proposed B&P algorithm is computationally more efficient than a standard branch-and-cut algorithm, and it significantly improves upon the performance of a well-known bin-packing heuristic.

Process mining is a suitable method for knowledge extraction from patient pathways. Structured in event logs, medical events are complex, often described using various medical codes. An efficient labeling of these events before applying process mining analysis is challenging. This paper presents an innovative methodology to handle the complexity of events in medical event logs. Based on autoencoding, accurate labels are created by clustering similar events in latent space. Moreover, the explanation of created labels is provided by the decoding of its corresponding events. Tested on synthetic events, the method is able to find hidden clusters on sparse binary data, as well as accurately explain created labels. A case study on real healthcare data is performed. Results confirm the suitability of the method to extract knowledge from complex event logs representing patient pathways.

This paper addresses the problem of emergency surgery insertion into a given elective surgery schedule of an operating theater (OT) composed of multiple operating rooms (ORs). Emergency surgeries with different emergency levels characterized by waiting time targets (WTTs) arrive according to a nonhomogeneous Poisson process and can be inserted into any OR. An event-based stochastic programming model is proposed to minimize the total cost incurred by exceeding WTTs of emergency surgeries, elective surgery delay, and surgery team overtime. A perfect information-based lower bound is proposed and the properties of the optimal policies are proved. Simple heuristic policies and a stochastic optimization (SO) approach derived from the simple policies by policy improvement are proposed. Numerical experiments show that the SO significantly outperforms the others and efficient emergency insertion significantly improves the system performance. A principal component analysis is performed to show how near-optimal policies differ from simple heuristic policies.

There has been growing interest in healthcare delivery systems worldwide coupled with a recent influx of funding into the area. Due to rapid development in information and network technology, smartness and interconnectivity have become a central issue in healthcare delivery. Automation is important for healthcare delivery systems engineering. In recent years, the significant changes in healthcare delivery and the rapid development in data analytics, artificial intelligence, robotics, and wearable devices have generated numerous opportunities for innovation in automation for smart and interconnected healthcare delivery systems. In addition, many new challenges have emerged in order to apply and implement these innovations. Such opportunities and challenges have significantly expanded the scopes of traditional automation science and engineering. Therefore, to show the state-of-the-art research and applications in the general area of healthcare delivery systems automation and to address the needs and challenges for the integration of new automation technologies in healthcare delivery, this Special Issue serves as a forum to bring together researchers, clinicians, and healthcare practitioners to present efficient scientific and engineering solutions and to provide visions for future research and development.

Respite care services constitute a new service to decrease burnout risk of caregivers. Pre-identification of caregivers with severe burnout is crucial to better manage respite care services through smart admission policies and health resources management. In this article we propose an analytic experiment to predict the exhaustion level of caregivers using automatic learning methods and several target variables. We also propose an automated extraction of burnout predictors. Results show that decision tree performs well on a data-set of 240 caregivers with two target variable. Using decision trees, we are able to propose an explicit medical decision aid tools to practitioners in order to detect efficiently pre burnout situation for caregivers at risk.

Fluctuations in emergency department (ED) patient arrivals during the day are one of the main causes of the long waiting times that are frequently encountered, and ED staffing is one of the key drivers of ED service quality improvement. This paper first proposes discrete-time models for approximating the patient waiting times for any given ED staffing. The waiting time approximation is based on three simple ideas: the separation of patients served in a period and patients overflowed, the combination of M/M/c approximation for patients served and waiting time analysis of overflow patients, and the transformation of the performance evaluation into an optimization problem with the number of overflow patients as decision variables. The resulting waiting time approximations are then integrated into ED staffing optimization models, and variable neighborhood search algorithms are developed to solve the ED staffing models. Numerical experiments with real-life data from Chinese hospitals are performed to validate the proposed models and algorithms. The results show that the proposed methodology is able to significantly reduce the total waiting time of patients without increasing staff capacity. The online appendix is available at https://doi.org/10.1287/ijoc.2017.0799 .

This paper addresses the problem of process discovery from large and complex event logs. We depart from the existing literature and formulate the problem of optimal process discovery. A formal mathematical programming model is given based on a novel hierarchical structuration of the event logs. Desired properties of event trace score functions are described, and the properties of optimal process models are proved. A combination of Monte Carlo optimization and tabu search is proposed to overcome the complexity related to the huge size of the event logs and the combinatorial solution space. Numerical results show that our approach is suitable for large event logs and that it performs better than the state-of-the-art approaches. We also demonstrate the applicability of our method on a real case study in health care. This paper illustrates the benefits of combining techniques from the operational research and the process mining fields.

Diagnostic facility is one of the most important critical resources in the hospital. Patient scheduling plays an important role in managing these facilities, especially when they are shared between regular and emergency patients. Due to the non-stationary emergency arrival and waiting time targets of emergency patients, it is challenging for hospital managers to make appointment scheduling decision and real time scheduling decisions, i.e., how many regular patients could reserve the service and how to coordinate the service of both types of patients. To deal with this problem, this paper proposes a stochastic integer programming model by considering the uncertainty of emergency patients and their waiting time requirement. The objective is to minimize the weighted idle time, overtime, and patients' waiting time. Monte Carlo optimization is used to solve this model. Numerical experiments are proposed to show the usefulness of the proposed model for investigation of the influence of different parameters.

In this paper, we propose a two-step bloodmobile collection planning framework. The first step is the annual planning to determine weeks of collection at each mobile site in order to ensure regional self-sufficiency of blood supply. The second step is the detailed weekly planning to determine days of collections at each mobile site and to form corresponding transfusion teams. Only key resource requirements are considered for annual planning while detailed resource requirements and transportation times are considered for weekly planning. Two Mixed Integer Programming models are proposed for annual planning by assuming fixed or variable mobile collection frequencies. A new donation forecast model is proposed based on population demographics, donor generosity, and donor availability. A new concept of bloodmobile collection configurations is proposed for compact and efficient mathematical modeling of weekly planning in order to minimize the total working time. Field data from the French Blood Service (EFS) in the Auvergne-Loire Region are used to design numerical experiments and to assess the efficiency of the proposed models.

This paper addresses the problem of joint facility location and capacity planning of hierarchical service networks in order to determine when and where to open/close service units, their capacity and the demand-to-facility allocation. We propose a new hierarchical service network model in which both the facilities and customers have nested hierarchies, i.e., a higher level facility provides all services provided by a lower level facility and a customer requiring a certain level of service will additionally require lower level services. Poisson customer arrivals and random service times are assumed. Each service unit is modeled as an Erlang-loss system and its service level, defined as its customer acceptance probability, is given by the so-called Erlang-loss function. A nonlinear programming model is proposed to minimize the total cost, while keeping the service level of all service units above some given level. Different linearization models of the Erlang-loss function and their properties are proposed. Linearization transforms the nonlinear model into compact mixed integer programs solvable to optimality with standard solvers. Application to a real-life perinatal network is then presented.

This paper addresses the dynamic assignment of a given set of surgeries to multiple identical operating rooms (ORs). Surgeries have random durations and planned surgeon arrival times. Surgeries are assigned dynamically to ORs at surgery completion events. The goal is to minimize the total expected cost incurred by surgeon waiting, OR idling, and OR overtime. We first formulate the problem as a multistage stochastic programming model. An efficient algorithm is then proposed by combining a two-stage stochastic programming approximation and some look-ahead strategies. A perfect information-based lower bound of the optimal expected cost is given to evaluate the optimality gap of the dynamic assignment strategy. Numerical results show that the dynamic scheduling and optimization with the proposed approach significantly improve the performance of static scheduling and First Come First Serve (FCFS) strategy.

In this paper, we propose a new modeling methodology named MedPRO for addressing organization problems of health care systems. It is based on a metamodel with three different views: process view (care pathways of patients), resource view (activities of relevant resources), and organization view (dependence and organization of resources). The resulting metamodel can be instantiated for a specific health care system and be converted into an executable model for simulation by means of a special class of Petri nets (PNs), called Health Care Petri Nets (HCPNs). HCPN models also serve as a basis for short-term planning and scheduling of health care activities. As a result, the MedPRO methodology leads to a fast-prototyping tool for easy and rigorous modeling and simulation of health care systems. A case study is presented to show the benefits of the MedPRO methodology.

This paper addresses the design of contract for reserving the capacity of a shared critical resource from the perspective of a given class of customers. The contract is composed of three parts: contracted time slots (CTSs) reserved by the service provider for the class of customers, advance cancellation of contracted time slots, and requests for regular time slots (RTSs). The problem of CTS cancellation and RTS assignment is formulated as an average cost Markov Decision Process in order to minimize the total cost including customer waiting times, unused CTS, and CTS cancellation. Structural properties of the optimal control policies are established via the discounted cost problem. A local optimization algorithm is proposed to improve a given initial contract. Numerical results show that advance CTS cancellation significantly reduces the ratio of unused CTS with slight increase of customer waiting time.

This paper addresses the design of single commodity stochastic distribution networks. The distribution network under consideration consists of a single supplier serving a set of retailers through a set of distribution centers (DCs). The number and location of DCs are decision variables and they are chosen from the set of retailer locations. To manage inventory at DCs, the economic order quantity (EOQ) policy is used by each DC, and a safety stock level is kept to ensure a given retailer service level. Each retailer faces a random demand of a single commodity and the supply lead time from the supplier to each DC is random. The goal is to minimize the total location, shipment, and inventory costs, while ensuring a given retailer service level. The introduction of inventory costs and safety stock costs leads to a nonlinear NP-hard optimization problem. A Lagrangian relaxation approach is proposed. Computational results are presented and analyzed showing the effectiveness of the proposed approach.

e-Procurement is an Internet based business process for sourcing direct or indirect materials. In this paper we propose an auction mechanism called as discount auctions for procuring multiple items. The bid from a supplier consists of individual costs for each of the items and a discount function, which specifies the discount over the number of items. We show that such a bid is more meaningful and cost effective in terms of bid preparation and communication in common procurement scenarios. The bid evaluation problem is modeled as a mixed integer linear program and various structures in the problem that can be exploited for developing algorithms are explored. A heuristic based on linear programming relaxation is proposed to determine a feasible solution to the problem and its closeness to optimality is studied with computational experiments.

This paper exploits the potential of siphons for the analysis of Petri nets, It generalizes the well-known Commoner condition and is based on the notion of potential deadlocks which are siphons that eventually become empty. A linear programming based sufficient condition under which a siphon is not a potential deadlock is obtained. Based on the new sufficient condition, a mathematical programming approach and a mixed-integer programming approach are proposed for checking general Petri nets and structurally bounded Petri nets respectively without explicitly generating siphons. Stronger results are obtained for asymmetric choice nets and augmented marked graphs. In particular, we show that an asymmetric choice net is live iff it is potential-deadlock-free and an augmented marked graph is live and reversible iff it is potential-deadlock-free.