Dynamic Surgical Waiting List Methodology: A Networking Approach

Silva-Aravena, Fabián; Morales, Jenny

doi:10.3390/math10132307

Cited by 5 publications

(3 citation statements)

References 43 publications

(71 reference statements)

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“…In this way, it will create an alarming situation that the patient is waiting for more time and the health may get affected by delays. Lastly, machine learning techniques can be incorporated into prioritization of patients as demonstrated in papers Silva-Aravena & Morales, 2022).…”

Section: Discussionmentioning

confidence: 99%

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Hassan¹,

Umer²,

Hassan³

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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Prioritizing patients is a growing concern in healthcare. Once resources are limited, prioritization is considered an effective and viable solution in provision of healthcare treatment to awaiting patients. Prioritization is a preferred approach that helps clinicians to apportion scarce resources fairly and transparently. In this study, a novel methodology of prioritizing the patient is formulated using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The objective is based on actual hospital conditions in Pakistan. The proposed methodology has two contributions: objective scoring mechanism that translates the patient’s condition given in human linguistic terms; and second methodology to prioritize patients according to corresponding scores. To validate the proposed methodology, simulation was carried out on actual data collected in real-time by surgeons, while providing consultations to their patients. The proposed methodology outperforms the traditional methodology by reducing average waiting time by 34% (from 4.246 to 2.810 days), minimize wait time and delays by 46.7% (from 15 to 8 days), and number of surgery days by 18%. The majority of the previously presented researched methodologies prioritize the patients subjectively. This study presents an objective methodology to prioritize the patients and decrease wait-times while ensuring transparency and equity.

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Section: Discussionmentioning

confidence: 99%

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Hassan¹,

Umer²,

Hassan³

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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show abstract

“…The structure of the patient classification strategy is based on the Intersectoral Standard Process for the development of Machine Learning applications with the quality assurance methodology (CRISP-ML(Q)), a method widely used in the health sector and which has been mentioned in different works, such as Silva-Aravena et al [17], Kolyshkina and Simoff [81], Silva-Aravena and Morales [82], Silva-Aravena et al [83]. Additionally, we have incorporated an explainability algorithm, XAI, into this strategy to provide betterquality information that favors clinical decision making.…”

Section: New Strategy To Classify Patients With Breast Cancermentioning

confidence: 99%

A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

Silva-Aravena

Delafuente

Gutiérrez-Bahamondes

et al. 2023

Cancers

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Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients’ prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.

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“…Other authors have developed strategies based on measuring the quantity and quality of the waiting list, e.g., Sutherland et al [8] developed multivariate regression models to estimate associations between patient characteristics and their health, pain, and depression. A recent work proposed by Silva-Aravena and Morales [42] showed a method based on multi-linear regression to prioritize patients on surgical waiting lists. Other authors, such as Gutacker et al [43], developed Poisson regression models to evaluate wait times for patients needing hip and knee replacement.…”

Section: Strategies For Patients' Prioritizationmentioning

confidence: 99%

An Intelligent System for Patients’ Well-Being: A Multi-Criteria Decision-Making Approach

et al. 2022

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The coronavirus pandemic has intensified the strain on medical care processes, especially waiting lists for patients under medical management. In Chile, the pandemic has caused an increase of 52,000 people waiting for care. For this reason, a high-complexity hospital (HCH) in Chile devised a decision support system (DSS) based on multi-criteria decision-making (MCDM), which combines management criteria, such as critical events, with clinical variables that allow prioritizing the population of chronic patients on the waiting list. The tool includes four methodological contributions: 1) pattern recognition through the analysis of anonymous patient data that allows critical patients to be characterized; 2) a score of the critical events suffered by the patients; 3) a score based on clinical criteria; and 3) a dynamic–hybrid methodology for patient selection that links critical events with clinical criteria and with the risk levels of patients on the waiting list. The methodology allowed to 1) characterize the most critical patients and triple the evaluation of medical records; 2) save medical hours during the prioritization process; 3) reduce the risk levels of patients on the waiting list; and 4) reduce the critical events in the first month of implementation, which could have been caused by the DSS and medical decision-making. This strategy was effective (even during a pandemic period).

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Dynamic Surgical Waiting List Methodology: A Networking Approach

Cited by 5 publications

References 43 publications

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

An Intelligent System for Patients’ Well-Being: A Multi-Criteria Decision-Making Approach

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