Shan WANG

Problem definition: Multistage service is common in healthcare. One widely adopted approach to manage patient visits in multistage service is to provide patients with visit itineraries that specify personalized appointment time for each patient at each service stage. We study how to design such visit itineraries. Methodology/results: We develop the first optimization modeling framework to provide each patient with a personalized visit itinerary in a tandem (healthcare) service system. Due to interdependencies among stages, our model loses those elegant properties (e.g., L-convexity and submodularity) often utilized to solve the classic single-stage models. To address these challenges, we develop two original reformulations. One is directly amenable to off-the-shelf optimization software, and the other is a concave minimization problem over a polyhedron shown to have neat structural properties, based on which we develop efficient solution algorithms. In addition to these exact solution approaches, we propose an approximation approach with a provable optimality bound and numerically validated performance to serve as an easy-to-implement heuristic. A case study populated by data from the Dana-Farber Cancer Institute shows that our approach makes a remarkable 28% cost reduction over practice on average. Managerial implications: Common approaches used in practice are based on simple adjustments to schedules generated by single-stage models, often assuming deterministic service times. Whereas such approaches are intuitive and take advantage of existing knowledge of single-stage models, they can lead to significant loss of operational efficiency in managing multistage services. A well-designed patient visit itinerary that carefully addresses the interdependencies among stages can significantly improve patient experience and provider utilization.

 
@article{liu2024design,
  title={Design of Patient Visit Itineraries in Tandem Systems},
  author={Liu, Nan and Wan, Guohua and Wang, Shan},
  journal={Manufacturing \& Service Operations Management},
  volume={26},
  number={3},
  pages={972--991},
  year={2024},
  publisher={INFORMS}
}
    

Third Prize, Chinese Scholars Association for Management Science and Engineering (CSAMSE) Best Paper Competition, 2022
Selected to MSOM SIG Healthcare Operations Conference

Electronic visits, or “E-visits” for short, have emerged as a promising channel for accessing healthcare and can significantly impact daily operations in healthcare facilities. However, there is a lack of research on how to efficiently manage appointments for outpatient care providers when faced with E-visits that exhibit different waiting cost patterns. Our study investigates how providers can use appointment scheduling as a “passive” control when patients have full access to the E-visit channel, to better utilise resources and reduce patient waiting. Specifically, we demonstrate that multimodularity still applies to the model with E-visits, despite their waiting costs being typically nonlinear. Furthermore, we analyse how providers can “actively” control the arrival of E-visits by scheduling their time windows. By examining the structures of the optimal joint schedule of appointments and E-visit time windows, and reformulating the problem into a two-stage program, we have designed an Accelerated Cut Generation Algorithm, which is shown to be efficient in our numerical study. To the best of our knowledge, this is the first study to explore the optimal scheduling of both appointments and E-visit time windows. By implementing proper scheduling, the negative impact of E-visits can be mitigated, their benefits to the provider can be enhanced, and overall operational efficiency can be improved.

 
@article{cai2024managing,
  title={Managing appointment-based services with electronic visits},
  author={Cai, Yun and Song, Haiqing and Wang, Shan},
  journal={European Journal of Operational Research},
  volume={315},
  number={3},
  pages={863--878},
  year={2024},
  publisher={Elsevier}
}
    

Third Prize, POMS-China Best Student Paper Award, 2023

核磁共振检查（MRI）是现代医学影像诊断的重要手段之一。MRI设备在扫描不同部位时会产生转换成本，为同时提高设备利用率和患者满意度，建立了马尔可夫决策过程模型，以最小化医院的长期成本。该模型是首个对MRI设备转换成本和患者日间等候成本进行权衡的优化模型。通过策略迭代算法可得到该模型的最优调度。为降低计算复杂度，设计了基于单日规则、开放获取规则和短视规则的多项式时间算法。除此之外，通过忽略容量约束，提出了分解算法，在MRI设备检查能力紧张或充足时，其表现与最优解接近。通过对比上海某大型综合医院的实际排程，验证了上述算法能够在检查能力、患者拒绝比例、患者平均等待天数、日平均检查类型数等指标上取得显著改善。

 
@article{林晖2024考虑设备转换成本的,
  title={考虑设备转换成本的 MRI 检查预约调度优化},
  author={林晖 and 王杉},
  journal={系统管理学报},
  volume={33},
  number={1},
  pages={59},
  year={2024}
}
    

Second Prize, National Doctoral Academic Forum on Digital Transformation and Intelligent Management, 2022

Outpatient care providers usually allow patients to access service via scheduling appointments or direct walk-in. Patients choose strategically between these two access channels (and otherwise balking) based on the trade-off of appointment delay and in-clinic waiting. How to manage outpatient care with such dual access channels, taking into account patient strategic choice behavior, is a challenge faced by providers. We study three operational levers to address this management challenge: service capacity allocation between these two channels, appointment delay information revelation via the choice and design of online scheduling systems, and a walk-in triage system that restricts the use of walk-in hours only for acute care. By studying a stylized queueing model, we find that neither a real-time online scheduling system (which offers instant access to appointment delay information at time of booking) nor an asynchronous online system (which does not directly provide delay information) can be universally more efficient. Although real-time systems appear more popular in practice, asynchronous systems sometimes can result in higher operational efficiency. Under the provider’s optimal capacity allocation, which scheduling system is more efficient hinges on two key factors: the patient demand–provider capacity relationship and patient willingness to wait. For the walk-in triage system, we find that it may or may not be beneficial to adopt; the provider’s own cost trade-off between lost demand and overtime work is the key determinant. Our research highlights that there is no one-size-fits-all model for outpatient care management, and the best use of operational levers critically depends on the practice environment.

 
@article{liu2023managing,
  title={Managing outpatient service with strategic walk-ins},
  author={Liu, Nan and van Jaarsveld, Willem and Wang, Shan and Xiao, Guanlian},
  journal={Management Science},
  volume={69},
  number={10},
  pages={5904--5922},
  year={2023},
  publisher={INFORMS}
}
 

Motivated by a real problem in a big hospital in China, we study a daily surgery scheduling problem with operating room eligibility and dedicated surgeon. We model the problem as a parallel machine scheduling problem with machine and resource constraints to minimise the makespan, and innovatively propose an adaptive composite dispatching method to deal with such a strongly NP-hard problem. The dispatching rule is a combination of three popular rules LPT, LFJ and LRW, each of which can deal with some special features of the scheduling problems, and the scaling parameters are estimated through a statistical model learned from historical data. The adaptive composite dispatching method is easy-to-implement, fast, adaptable, robust and flexible. To examine the performance of the proposed solution approach, we first carry out a series of computational experiments showing that the adaptive composite dispatching method works very well compared to the optimal solution. Using a real data set, we further conduct a case study showing that our solution approach can improve the current practice by significantly shortening the makespan and reducing the overnight work.

 
@article{wang2023surgery,
  title={Surgery scheduling in the presence of operating room eligibility and dedicated surgeon: an adaptive composite dispatching method},
  author={Wang, Shan and Su, Huiqiao and Wan, Guohua and Zhong, Liwei},
  journal={International Journal of Production Research},
  volume={61},
  number={6},
  pages={1866--1881},
  year={2023},
  publisher={Taylor \& Francis}
}
    

Despite the prevalence and significance of walk-ins in healthcare, we know relatively little about how to plan and manage the daily operations of a healthcare facility that accepts both scheduled and walk-in patients. In this paper, we take a data-analytics approach and develop an optimization model to determine the optimal appointment schedule in the presence of potential walk-ins. Our model is the first known approach that can jointly handle general walk-in processes and heterogeneous, time-dependent no-show behaviors. We demonstrate that, with walk-ins, the optimal schedules are fundamentally different from those without. Our numerical study reveals that walk-ins introduce a new source of uncertainties to the system and cannot be viewed as a simple solution to compensate for patient no-shows. Scheduling, however, is an effective way to counter some of the negative impact from uncertain patient behaviors. Using data from practice, we predict a significant cost reduction (42%–73% on average) if the providers were to switch from current practice (which tends to overlook walk-ins in planning) to our proposed schedules. Although our work is motivated by healthcare, our models and insights can also be applied to general appointment-based services with walk-ins.

 
@article{wang2020managing,
  title={Managing appointment-based services in the presence of walk-in customers},
  author={Wang, Shan and Liu, Nan and Wan, Guohua},
  journal={Management Science},
  volume={66},
  number={2},
  pages={667--686},
  year={2020},
  publisher={INFORMS}
}
    

Second Prize, Social Science Award of the Ministry of Education, 2024
Winner, POMS-HK Best Student Paper Award, 2019
Winner, INFORMS IBM Service Science Best Student Paper Award, 2018
Winner, POMS College of Healthcare Operations Management (CHOM) Best Paper Competition, 2018
Third Place Winner, INFORMS Healthcare Best Student Paper Competition, 2017
Honorable Mention, Chinese Scholars Association for Management Science and Engineering (CSAMSE) Annual Conference Best Paper Award, 2017

The underutilization of primary care in urban China threatens the efficiency and effectiveness of the Chinese health system. To guide patient flow to primary care, the Chinese government has rolled out a sequence of health care reforms which improve the affordability, the infrastructure and workforce of the primary care system. However, these measures have not yielded the desired effect on the utilization of primary care, which is lowest in urban areas. It is unclear how the factors identified to influence facility choice in urban China are actually impacting choice behaviour. We conducted a discrete choice experiment to elicit the quantitative impact of facility attributes when choosing a health care facility for first visit and analysed how the stated choice varies with these attributes. We found that the respondents placed different weights on the identified attributes, depending on whether they perceived their condition to be minor or severe. For conditions perceived as minor, the respondents valued visit time, equipment and medical skill most. For conditions perceived as severe, they placed most importance on equipment, travel time and facility size. We found that for conditions perceived as minor, only 14% preferred visiting a facility over opting out, a percentage which would more than double to 37% if community health centres were maximally improved. For conditions perceived as severe, improvements in community health centres may almost double first visits to primary care, mostly from patients who would otherwise choose higher-level facilities. Our findings suggest that for both severity conditions, improvements to medical equipment and medical skill at community health centres in urban China can effectively direct patient flow to primary care and promote the efficiency and effectiveness of the urban health system.

 
@article{liu2020impact,
  title={The impact of hospital attributes on patient choice for first visit: evidence from a discrete choice experiment in Shanghai, China},
  author={Liu, Yun and Kong, Qingxia and Wang, Shan and Zhong, Liwei and Van de Klundert, Joris},
  journal={Health policy and planning},
  volume={35},
  number={3},
  pages={267--278},
  year={2020},
  publisher={Oxford University Press}
}
    

In outpatient services, it is critical to schedule patients for physicians to reduce both patients waiting and physicians overtime working. In this paper, we regard the problem as an online scheduling problem and based on analysis of a real data set from a big hospital in China, we develop a dynamic programming model to solve the problem. We propose a Policy Iteration Algorithm to find the optimal solution in the steady state, and obtain the structural properties of the policy. We conduct numerical experiments to compare the performance of the policy with that of the two policies used in practice by simulating various scenarios. The numerical results show that the policy has the best performance across all scenarios, especially when the system is heavily loaded. We also discuss the managerial implications of the study for practitioners. The model and solution method can be easily extended to multi-server case and can be applied to the general service scheduling problems with heterogeneous customers and service providers.

 
@article{su2019online,
  title={Online scheduling for outpatient services with heterogeneous patients and physicians},
  author={Su, Huiqiao and Wan, Guohua and Wang, Shan},
  journal={Journal of Combinatorial Optimization},
  volume={37},
  pages={123--149},
  year={2019},
  publisher={Springer}
}
    

We study a problem arising from surgical operations scheduling and model it as a resource-constrained machine scheduling problem with machine eligibility restriction to minimize the makespan. By decomposing the problem into two sub-problems, we develop effective heuristic algorithms to solve the problem. We test the proposed algorithms on randomly generated instances as well as real data set from a large hospital. The numerical results show the effectiveness and potential practical value of the models and the algorithms.

 
@article{wang2015resource,
  title={Resource-constrained machine scheduling with machine eligibility restriction and its applications to surgical operations scheduling},
  author={Wang, Shan and Su, Huiqiao and Wan, Guohua},
  journal={Journal of Combinatorial Optimization},
  volume={30},
  pages={982--995},
  year={2015},
  publisher={Springer}
}
    

Second Prize, Shanghai Operations Research Society Student Paper Competition, 2015

Background: Robust evidence to understand the choice of health-care facility access level is important to support policy measures to address the problems of misuse of hospital-based services and the underuse of primary care services. We examined the importance of the facility attributes that influence this choice, and the trade-offs made by the general public between these attributes in urban China. Methods: We did discrete choice experiments in Shanghai, China, for perceived mild and severe disease scenarios. We defined and used seven facility attributes: total visit time, out-of-pocket cost per visit, medical skill, personal connections, medical equipment, transportation time, and facility size. We recruited respondents using a stratified cluster sampling method, according to a predefined sample quota in age and sex. Trade-offs made by respondents among the attributes were analysed using mixed logit models. Besides the analysis on the main effects, we did a prespecified analysis to examine disease severity and individual-difference variables. This study was approved by the Shanghai General Hospital Medical Ethical Review Committee. We obtained written informed consent from all participants. Findings: A total of 532 residents completed the experiment (mean age 48·5 years, SD 15·0; 275 (52%) were male respondents). In general, ranked from the largest to smallest relative importance score, the significant attributes were total visit time (25%), medical equipment (25%), medical skill (12%), personal connections (12%), out-of-pocket cost per visit (11%), facility size (9%), and transportation time (7%). When disease was severe, the respondents were more tolerant to longer waiting time than when the disease was minor, and they showed a stronger preference to visiting a facility, especially to facilities with better medical equipment and those that were larger in size. The respondents also showed a strong aversion to prolonged transportation time and poor medical skill. Interpretation: To the best of our knowledge, this is the first discrete choice experiment done in China on the choice process of health-care facilities. People attached high importance to medical equipment in general, and to facility size when the disease was more severe. These findings could inform policy making to increase the use of primary care by improving the access to medical equipment. It also suggests that the public exposure of quality indicators other than the external factors of health-care facilities might help patients' decision making.

 
@article{liu2018preferences,
  title={Preferences for health-care facilities in urban China: a discrete choice experiment},
  author={Liu, Yun and de Bekker-Grob, Esther W and Kong, Qingxia and Wang, Shan and Zhong, Liwei and Van de Klundert, Joris},
  journal={The Lancet},
  volume={392},
  pages={S34},
  year={2018},
  publisher={Elsevier}
}
    

Problem definition: Many outpatient care providers offer virtual service that patients can access via televisits. Televisits allow patients to wait to be seen in the location of their choice, protected from exposure to ill patients and without going through the trouble of physical travel. There is evidence, however, that televisits are more likely to lead to a supplementary in-person visit, consuming additional resources that could have been saved if the patient's initial visit was in-person. Given this trade-off, we study whether an outpatient care provider should adopt virtual service and, if so, how best to manage a practice that simultaneously offers both virtual (or URL) and in-person (real-life, or RL) services. Methodology/results: We develop a stylized queueing-game model, which incorporates patient strategic choice between the two service channels. We study how a revenue-driven provider should allocate capacity between these two channels and how to incentivize patients for in-person visits. We find that the size of the system, measured by the total available service capacity relative to total patient demand, plays a determining role here. Small and large systems are better off focusing on one channel only and have no need to use in-person incentives, whereas medium-sized systems can benefit from offering both channels and in-person incentives. We also find that overall patient access to care may be hurt with the use of in-person incentives, unless the payment gap between the two channels is significantly large. Managerial implications: Despite the growing adoption of telemedicine, offering virtual service may not be the best choice for all providers. Capacity coordination between the virtual and in-person service channels has to be carefully balanced. Furthermore, in-person incentives need to be used with caution, otherwise patient access to care may be impaired. Proper financial incentives set up by the payer may prevent such a negative outcome.

Inventory management for a new product is challenging due to the lack of historical data, and sales data cannot fully capture the actual demand as excess demand is usually unobservable. Meanwhile, demand-related features are available in the era of big data. To minimize the long-term average overage and underage costs, we study how to determine the order-up-to level in a periodic-review inventory system with feature information and limited historical sales data. We allow the mean demand to be any function of the features. To solve the problem, we develop a neural network-based non-parametric online learning method, NeuroVendor. We prove through regret analysis that the cost of NeuroVendor converges at a rate of O(log T /T ) and O(1/√T ) for perishable and nonperishable inventory respectively, to the clairvoyant optimal cost with an approximation error. Additionally, we show that the approximation error is bounded and diminishes when the neural network size increases. We also note that the smaller the network, the faster the algorithm converges, but the larger the approximation error, indicating a bias-variance trade-off. In numerical experiments, NeuroVendor exhibits faster convergence rates and smaller regrets compared to existing methods. The advantage is more significant with more complex demand functions.

While Diagnosis-Related Group (DRG) payment schemes are widely adopted to incentivize cost reductions, many countries and regions persist in using Fee-for-Service (FFS) payment for outpatient services. This hybrid reimbursement system may prompt providers to make strategic responses on reducing treatment cost, shifting mild inpatients to outpatient care, and unbundling a portion of inpatient costs as outpatient expenses. To evaluate the behavioral incentives of this payment structure and the associated impacts, we first examine the regulator's optimal solutions regarding cost reduction, care shifting, and cost unbundling aimed at maximizing social welfare. The analysis reveals that regulatory goals should be different according to providers' outpatient care capabilities. Based on the yardstick competition framework, we then investigate providers' equilibrium decisions. Our findings demonstrate that traditional yardstick competition fails to meet regulatory goals, as providers' cost unbundling behavior diminishes their incentives to reduce costs and shift care. To facilitate regulatory goals and enhance social welfare, we further propose several possible approaches. First, for providers with low outpatient care capabilities, a coarser DRG grouping model is advisable, whereas a finer model is more suitable for high-capability providers. Second, stringent monitoring and regulation of cost unbundling practices are essential. Additionally, for high-capability providers, implementing yardstick competition for minor inpatient treatments yields significant efficiency and should be recommended.

Honorable Mention, POMS-China Best Student Paper Competition, 2025