Applying Heuristic Algorithms to Solve Inter-hospital Hierarchical Allocation and Scheduling Problems of Medical Staff

Chen, Ping-Shun; Huang, Wen‐Tzeng; Chiang, Tsung-Huan; Chen, Gary Yu-Hsin

doi:10.2991/ijcis.d.200310.004

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…DRG payments may be slightly more efficient, but they face upgrades in medical record codes and damage the quality and fairness of medical care ( 42 ). Damage to health care rights and interests has been reported, especially for patients who are not supported by this payment plan ( 43 ). Zou et al ( 44 ) evaluated the impact of DRG on health care and health outcomes in China and comprehensively described the expenditure, efficiency, quality, and fairness of health care.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

Wang

et al. 2021

Front. Public Health

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Objective: Given the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem that needs to be urgently solved. In this study a prediction method for calculating the monthly and daily flow of patients based on time series is proposed to provide decision support for government and hospital management.Methods: The historical patient flow data from the Department of Obstetrics and Gynecology of the First Hospital of Jilin University, China, from January 1, 2018, to February 29, 2020, were used as the training set. Seven models such as XGBoost, SVM, RF, and NNAR were used to predict the daily patient flow in the next 14 days. The HoltWinters model is then used to predict the monthly flow of patients over the next year.Results: The results of this analysis and prediction model showed that the obstetric inpatient flow was not a purely random process, and that patient flow was not only accompanied by the random patient flow but also showed a trend change and seasonal change rule. ACF,PACF,Ljung_box, and residual histogram were then used to verify the accuracy of the prediction model, and the results show that the Holtwiners model was optimal. R2, MAPE, and other indicators were used to measure the accuracy of the 14 day prediction model, and the results showed that HoltWinters and STL prediction models achieved high accuracy.Conclusion: In this paper, the time series model was used to analyze the trend and seasonal changes of obstetric patient flow and predict the patient flow in the next 14 days and 12 months. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed, combined with the prediction of patient flow.

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Section: Discussionmentioning

confidence: 99%

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

Wang

et al. 2021

Front. Public Health

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“…Knowing that the management of nursing teams is directly linked to the quality and level of patient satisfaction, it is necessary to plan the scheduling of professionals to promote higher satisfaction rates, productivity, and, consequently, higher approval rates in the care provided (Chen et al, 2020). Some recent studies highlight the importance of information technology in nursing processes and supporting hospital activities in general (Gür & Eren, 2018;Abdalkareem et al, 2021;Camargo et al, 2018;Mendonca & Tachinardi, 2018;Soares et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Uma heurística de busca local para o problema de designação de equipes de enfermagem com preferências pessoais

André,

Silva,

Silva

2024

Braz. J. Prod. Eng.

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Recentemente, a pesquisa em otimização de saúde tem experimentado um crescimento exponencial, despertando o interesse significativo de pesquisadores e organizações de saúde. Esse aumento de interesse é impulsionado pela complexidade e relevância dos desafios enfrentados pela sociedade, estando diretamente relacionado à necessidade crescente de aprimoramento de processos e à busca por maior eficiência nos sistemas de saúde em escala global. O objetivo deste estudo é desenvolver uma abordagem de otimização, baseada em heurística computacional, para realizar o planejamento e a designação de profissionais de enfermagem em setores hospitalares, visando maximizar tanto as preferências pessoais dos profissionais, como a eficiência nos atendimentos de saúde. O método proposto utiliza um algoritmo heurístico de otimização baseado em busca local com mecanismos de perturbação de solução e vizinhanças de busca eficientes. Os resultados computacionais demonstraram que o método é capaz de realizar designações eficientes de profissionais de enfermagem em setores hospitalares, otimizando a satisfação profissional e a qualidade do serviço prestado. Concluindo, o estudo evidenciou que o método desenvolvido permite uma eficiente gestão e designação de profissionais de enfermagem em ambientes hospitalares, alcançando contribuições científicas e práticas para as áreas de healthcare optimization e administração hospitalar.

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“…• the development of a two-stage strategy for allocating medical staff in healthcare center networks and for defining their monthly schedules. The proposal helped decision makers minimize the medical staff required at each center (Chen, P.-S. et al, 2020). • an information system based on analytical models for handling heterogeneous data to help decision makers allocate medical staff minimizing costs (Chaovalitwongse et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Developing Models Based on Machine Learning Algorithms to Estimate Medical Staffing Needs at Intensive Care Units

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Generally, hospitals and health centers have a specialized area called Intensive Care Unit (ICU) aimed to provide treatment for people who are extremely ill. An ICU is staffed with highly trained professionals who operate advance equipment. Patients are referred to ICUs when they have a condition that cannot be treated in a regular hospital room. For example: after being in a serious car accident, after having a heart attack or stroke, when suffering a serious infection such as pneumonia, or after having major surgery. The present research focusses in the development of prediction models based on machine learning algorithms to help planning and allocating medical staff at an ICU. The research was carried out following a classic 4-phase methodology: analysis, design, development, and validation. In the analysis, independent variables such as patient's age, gender, reason for visit, and arrival mode are identified. Data are then pre-processed and prepared for the following phases. Specialize software packages for data mining and machine learning were selected for the investigation. Most of the investigation's activities were planned at this point. During the design, different subsets of the independent variables were selected to be build prediction models. The size of the training and test set as well as the size of the validation set were also defined here. The phases of development and validation were carried out entirely using the software WEKA 3.9.6. By means of executing a battery of experiment and "trial and error" adjustments the prediction models were completed. To accomplish the goals of this research, thousands of records collected from a public hospital were considered. The dataset was divided in a part for training and test (80%) and another part for validation (20%). The approach to predict the medical specialty require for the arriving patients considered the development of models based on artificial neural networks and decision trees. In all cases, instead of a simple hold-out, a 10-fold crossvalidation scheme was applied. Results showed that, in general, prediction models with fewer classes generate higher rates of correct predictions and, therefore, they can be more useful when planning and allocating medical staff at an ICU. Even though correct prediction rates fluctuate significantly from one model to another, between 80% and 83%, some of the proposed models are reliable. In conclusion, results showed that prediction models based on machine learning algorithms can help planning and managing the need for professionals and support staff at ICU. The research might help understand the benefits of using machine learning in hospitals and health centers.

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Applying Heuristic Algorithms to Solve Inter-hospital Hierarchical Allocation and Scheduling Problems of Medical Staff

Cited by 8 publications

References 40 publications

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

Uma heurística de busca local para o problema de designação de equipes de enfermagem com preferências pessoais

Developing Models Based on Machine Learning Algorithms to Estimate Medical Staffing Needs at Intensive Care Units

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