Prioritizing the Surgical Waiting List-Cosine Consistency Index: An Optimized Framework for Prioritizing Surgical Waiting List

Petwal, Hemant; Rani, R. Ranjani

doi:10.1166/jmihi.2020.3224

Cited by 5 publications

(3 citation statements)

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“…Additional methods of management, which include predictive tools, have been used for patient prioritization. For example, Petwal and Rani [32] have exposed a hybrid metaheuristic strategy combined with a natureinspired algorithm. Other authors such as Jiang et al [1] propose the development of tools prioritization based on genetic algorithms and neural networks.…”

Section: Decision Support Systems In Health Servicesmentioning

confidence: 99%

Dynamic Surgical Waiting List Methodology: A Networking Approach

Silva-Aravena

Morales

2022

Mathematics

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In Chile and the world, the supply of medical hours to provide care has been reduced due to the health crisis caused by COVID-19. As of December 2021, the outlook has been critical in Chile, both in medical and surgical care, where 1.7 million people wait for care, and the wait for surgery has risen from 348 to 525 days on average. This occurs mainly when the demand for care exceeds the supply available in the public system, which has caused serious problems in patients who will remain on hold and health teams have implemented management measures through prioritization measures so that patients are treated on time. In this paper, we propose a methodology to work in net for predicting the prioritization of patients on surgical waiting lists (SWL) embodied with a machine learning scheme for a high complexity hospital (HCH) in Chile. That is linked to the risk of each waiting patient. The work presents the following contributions; The first contribution is a network method that predicts the priority order of anonymous patients entering the SWL. The second contribution is a dynamic quantification of the risk of waiting patients. The third contribution is a patient selection protocol based on a dynamic update of the SWL based on the components of prioritization, risk, and clinical criteria. The optimization of the process was measured by a simulation of the total times of the system in HCH. The prioritization strategy proposed savings of medical hours allowing 20% additional surgeries to be performed, thus reducing SWL by 10%. The risk of waiting patients could drop by up to 8% annually. We hope to implement this methodology in real health care units.

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Section: Decision Support Systems In Health Servicesmentioning

confidence: 99%

Dynamic Surgical Waiting List Methodology: A Networking Approach

Silva-Aravena

Morales

2022

Mathematics

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“…Predictive and more automated tools have prioritized patients on waiting lists. For example, hybrid metaheuristic strategies combined with algorithms inspired by nature have been studied by Petwal and Rani [52], whose approach was based on a simulation mixed with a scheme that predicted the evolution of the prioritized patient. Rahimi et al [53], de Almeida et al [54] added the experiences of clinical experts using the DELPHI approach with mathematical tools that attempted to sort patients dynamically as they waited.…”

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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“…Another study used a multilayer clustering approach for partitioning of Alzheimer disease patients into male and female groups [14]. In addition, few studies have used agglomerative hierarchical clustering for partitioning of patients based on presence of comorbidities such as chronic pain and mental illness; obesity and mental illness; cancer; diabetes and renal disease [15,16]. A Bayesian nonparametric clustering approach was applied to divide patients having cancer into sub-groups to measure their anxiety and depression scores before psychotherapy [17].…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Framework for Surgical Team Selection

Petwal¹,

Rani²

2021

Computers, Materials &Amp; Continua

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In the healthcare system, a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery. Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital's patient care. The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them. In this paper, we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given patient. The proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient's subgroups. For this end, two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimization were proposed. The proposed algorithms were tested using different datasets and benchmark functions. Furthermore, the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in India. The results revealed that the proposed framework was efficient in arranging patients in optimal groups as well as selecting optimal surgical teams for a given patient.

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Prioritizing the Surgical Waiting List-Cosine Consistency Index: An Optimized Framework for Prioritizing Surgical Waiting List

Cited by 5 publications

References 0 publications

Dynamic Surgical Waiting List Methodology: A Networking Approach

Dynamic Surgical Waiting List Methodology: A Networking Approach

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

An Optimized Framework for Surgical Team Selection

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