Operations Research for Health Care

André Cintas Donizette

Cleber Damião Rocco

Thiago Alves de Queiroz

Predicting leishmaniasis outbreaks in Brazil using machine learning models based on disease surveillance and meteorological data

基于疾病监测和气象数据的机器学习模型预测巴西利什曼病暴发

During the first wave of the COVID-19 pandemic it emerged that the nature and magnitude of demand for mental health services was changing. Considerable increases were expected to follow initial lulls as treatment was sought for new and existing conditions following relaxation of 'lockdown' measures. For this to be managed by the various services that constitute a mental health system, it would be necessary to complement such projections with assessments of capacity, in order to understand the propagation of demand and the value of any consequent mitigations. This paper provides an account of exploratory modelling undertaken within a major UK healthcare system during the first wave of the pandemic, when actionable insights were in short supply and decisions were made under much uncertainty. In understanding the impact on post-lockdown operational performance, the objective was to evaluate the efficacy of two considered interventions against a baseline 'do nothing' scenario. In doing so, a versatile and purpose-built discrete time simulation model was developed, calibrated and used by a multi-disciplinary project working group. The solution, representing a multi-node, multi-server queueing network with reneging, is implemented in open-source software and is freely and publicly available.

B.J. Murch

J.A. Cooper

T.J. Hodgett

E.L. Gara

J.S. Walker

R.M. Wood

Modelling the effect of first-wave COVID-19 on mental health services

第一波COVID-19对心理健康服务影响的建模

Disasters such as major floods affect all part of the globe. Hospital and healthcare structures are critical resources during such event and do not always benefit of emergency preparedness. When hospitals are impacted by the disaster, it puts a strain on the system and a reorganization of all available hospitals on a given territory is necessary. As part of case study applied to the impact of floods on the Île-De-France region's health system, we present in this paper a simulation model that evaluates healthcare emergency plan by combining the healthcare process with the flood dynamics. The results can be used to elaborate an optimized strategy for evacuation and transfer operations. We provide a case study including several medical specialties and quantify the impact of several flood scenarios on the healthcare system.

Afafe Zehrouni

Vincent Augusto

Thierry Garaix

Raksmey Phan

Xiaolan Xie

Sophie Denis

Michel Gentile

Hospital flood emergency management planning using Markov models and discrete-event simulation

医院洪水应急管理规划：基于马尔可夫模型和离散事件模拟

We analyze the two recent Medicare alternative payment models, the comprehensive primary care plus (CPC+) and the primary care first (PCF). Both models comprise fee-for-service, traditional capitation, and pay-for-performance (P4P) components. The main objective of these reimbursement models is to advance toward value-based care. However, the models confer some hesitations since the P4P component is based on factors not entirely controlled by the practice, increasing the potential admission of healthier patients and affecting the profit of small primary care practices. We have modified the P4P component in both models to include a non-controllable agent (the hierarchical condition category score) and a controllable factor (the Bice–Boxerman continuity of care index) through a probabilistic classification model to predict hospital admissions. This study aims to determine the impact of adjusting the P4P component, in the CPC+ and PCF reimbursement models, on the profit per team, revenue for performance per team, and severity of admitted patients. We develop a mixed-integer programming formulation and analyze, using a 2k factorial design, the reimbursement models and the main elements of their adjusted P4P components (i.e., the probabilistic classification model coefficients and hospital admission threshold). The results indicate that the coefficients of the probabilistic classification model and the hospital admission threshold have a significant effect on the profit and revenue for performance per team. There is also a tendency of the PCF to admit less severe patients than the CPC+. Yet, the effects are more notable in the PCF payment model because the proportion of P4P in the total revenue under the CPC+ is minimal (16.5% versus <1%). Similarly, the PCF's downside is its sensitivity to P4P changes, displaying high variability in the output variables under analysis.

Jennifer Mendoza-Alonzo

José Zayas-Castro

Armin Lüer-Villagra

Controllable and non-controllable factors to measure performance in primary care practices under Medicare alternative payment models

医疗保险替代支付模式下初级保健实践中可控与不可控因素对绩效测量的影响

We consider the panel size management problem that aims to balance workloads among providers within a health care system, where the ratio of a primary care provider’s load to their own daily full capacity is used as a measure for overload or underload. While transferring patients from their existing providers to others is the means to achieve workload balance, several practical restrictions prohibit transfer if patients have multiple chronic conditions or should there be other reasons to discourage assignment changes. We also consider patients requests for specific characteristics of providers (e.g., same gender, same geographic location). In case the current system is greatly stressed even with patient panel reassignment, we allow for hiring new providers strategically at appropriate practice groups so that load balancing is achieved for the system (with new hires) and the utilization of no provider, existing or newly hired, exceeds a threshold value. Data analysis on provider panels from a Louisville regional health care system is performed and is used in developing an integer linear program model for the problem. Three case studies based on the data for the regional health care system show that the proposed model is effective in achieving load balancing and preventing physician burnout.

William Harrington

Paul A. Rubin

Lihui Bai

An optimization approach to panel size management

面板尺寸管理的优化方法

In a home care setting, high-quality care is typically associated with continuity of care. In addition, the increasing pressure due to labor shortages calls for cost-efficient operations. This paper focuses on obtaining cost-efficient daily schedules over a longer time horizon, with balanced shift lengths, while ensuring continuity of care (using the continuity of care index). To address this challenge, we propose a novel method based on blueprint routes. This method generates daily schedules by constructing optimized shifts and routes with regard to travel time, (time window) waiting time, and shift costs based on hourly wages. To ensure continuity of care, the daily scheduling decisions are strategically guided using the concept named blueprint routes. The blueprint routes are pre-optimized (partial) routes that help to align the daily schedules to achieve continuity of care in the subsequent nurse-to-shift assignment. Model-based evolutionary algorithms are employed to overcome the NP-hardness of the routing problem and nurse-to-shift assignment. Real-life-based numerical experiments demonstrate that continuity of care does not have to compromise home care schedule costs significantly.

Yoram Clapper

René Bekker

Joost Berkhout

Dennis Moeke

Balancing continuity of care and home care schedule costs using blueprint routes

使用蓝图路线平衡护理连续性和家庭护理计划成本

This study aims to develop a heuristic for an outpatient appointment system considering patient classification. The proposed heuristic was applied in simulations with eighteen scenarios, combining different environmental factors. Total cost was adopted as a performance metric, composed of the patient's wait time and the service provider's idleness and overtime. The patients were divided into two classes according to their no-show probability, in an arrivals sequence with a binomial distribution. As a significance test of the results, Bonferroni-adjusted repeated measures analysis was applied. Having Dome rule as baseline, an increase in performance in terms of total cost (TC) was observed, which varied between 0.46% and 5.94% among the means of the simulated environments, validated using the proposed significance test. The greatest benefits were obtained in the scenarios with lower ratios between service provider costs and patient costs (CR), as well as lower coefficients of variation for service times (Cv). It was also found that the heuristic is more efficient when patients from the class with the highest no-show rate predominate in the session. The single study identified in the literature that contemplates recalculations adopts deterministic service times to make its model viable. The present research, in turn, makes more realistic assumptions for the simulated environments, considering the variables and probability distributions most commonly observed in practical contexts The proposed heuristic provided a significant increase in performance for some combinations of environmental factors analyzed, preserving flexibility in the choice of appointment slots and covering a wide range of healthcare services found in practice.

Marcelo Oleskovicz

Marcelo Caldeira Pedroso

Jorge Luiz Biazzi

OUTPATIENT APPOINTMENT SYSTEMS: A NEW HEURISTIC WITH PATIENT CLASSIFICATION

门诊预约系统：一种基于患者分类的新启发式方法

R.J. Arntzen

R. Bekker

R.D. van der Mei

Preference-based allocation of patients to nursing homes

基于偏好的养老院患者分配

We develop a modeling framework for rostering, absence and demand uncertainty realization, and rerostering to perform detailed quantitative analyses of the robustness of nurse rosters. The framework reflects a real-life problem observed at the Department of Neonatal Intensive Care (DNIC) at St. Olavs Hospital in Trondheim, Norway, but is general and has a high transfer value with respect to using it to analyze roster robustness at other departments. We present multiple proactive strategies to enhance the stability of a roster and a reactive rerostering problem used to improve the flexibility. An extensive case study is performed using historical data from the department. The results show that there is a great potential to improve the stability and flexibility of the rosters using the best combination of strategies. We show that allowing nurses to trade extra weekend work for extra days off, assign surplus work hours evenly over all work shifts, and consider the absence profile of nurses when making the rosters are key strategies to create robust rosters.

Kjartan Kastet Klyve

Isabel Nordli Løyning

Line Maria Haugen Melby

Henrik Andersson

Anders Nordby Gullhav

A modeling framework for evaluating proactive and reactive nurse rostering strategies — A case study from a Neonatal Intensive Care Unit

评估主动和被动护士排班策略的建模框架——来自新生儿重症监护室的案例研究

Abbreviation	Oper. Res. Health. Care.
Journal Impact	1.54
Quartiles(Global)	HEALTH CARE SCIENCES & SERVICES(Q3)

ISSN	2211-6923, 2211-6931
h-index	37

Publication Information	Publisher: Elsevier B.V.，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2012，Proportion of original research papers: 83.33%，Self Citation Rate:0.00%， Gold OA Rate: 28.95%

Operations Research for Health Care

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Predicting leishmaniasis outbreaks in Brazil using machine learning models based on disease surveillance and meteorological data

Modelling the effect of first-wave COVID-19 on mental health services

Hospital flood emergency management planning using Markov models and discrete-event simulation

Controllable and non-controllable factors to measure performance in primary care practices under Medicare alternative payment models

An optimization approach to panel size management

Predicting leishmaniasis outbreaks in Brazil using machine learning models based on disease surveillance and meteorological data

Balancing continuity of care and home care schedule costs using blueprint routes

OUTPATIENT APPOINTMENT SYSTEMS: A NEW HEURISTIC WITH PATIENT CLASSIFICATION

Preference-based allocation of patients to nursing homes

A modeling framework for evaluating proactive and reactive nurse rostering strategies — A case study from a Neonatal Intensive Care Unit