Fuzzy Decision-Making Method based Evaluation of Smart Healthcare Management

Quasim, Mohammad Tabrez; Shaikh, Asadullah; Shuaib, Mohammed; Sulaiman, Adel; Alam, Shadab; Asiri, Yousef

doi:10.21203/rs.3.rs-1504815/v1

Cited by 4 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the help of Equations ( 1)-( 14), the current research applies the fuzzy AHP process to derive fuzzy weight. In addition, a fuzzy decision matrix is created by researchers with the help of Table 3 and Equation (15), and relevant linguistic variables are chosen as alternatives for the criterion. With the help of Equation ( 16), the fuzzy decision matrix is normalized and represented by ∼ D. After that, with the help of Equation ( 17), the normalization process can be achieved.…”

Section: Fuzzy Topsismentioning

confidence: 99%

“…Often, experts in the IT industry have focused on the deployment phase of HWAs to improve security longevity and minimize maintenance costs and time. However, integrating security and unearthing vulnerabilities early in the design phase of web applications can reduce the time and cost of development by minimizing the development team's work [14][15][16]. This reveals the impact of vulnerabilities that can affect healthcare web applications' integrity, violate confidentiality and privacy norms, and exploit loopholes in the design phase.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy-Based Unified Decision-Making Technique to Evaluate Security Risks: A Healthcare Perspective

et al. 2023

View full text Add to dashboard Cite

Neoteric biomedical, technological, and normative shifts have prompted care firms to establish clinical governance as a contrivance to assure high-quality service in an exceedingly intricate milieu. Web security is an epochal concern in the healthcare sector, although it has garnered scant attention since the inception of web applications. The necessity to provide adequate security for healthcare web applications (HWAs) cannot be exaggerated, as umpteen health agencies are contingent on them to carry out their operations. Every healthcare organization renders a humongous volume of data available online to practitioners, pharmacies, and patients. Researchers are continually endeavoring to ameliorate techniques to increase the security and longevity of HWAs. In this context, experts examined certain imperative security risks in HWAs to quantitatively evaluate them in the design phase and covered numerous facets of HWAs, along with their security attributes and risk factors. The authors have proposed a combined approach of fuzzy-based symmetric techniques, i.e., AHP-TOPSIS (Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution), for the assessment of alternative HWAs, leveraging the multi-criteria decision-making (MCDM) approach. Ten consecutive HWAs from local hospitals in Uttar Pradesh, India, have been taken to estimate the security risk, incorporating this methodology to evaluate the priority of weightage and the impact of security attributes. Henceforth, the findings and methodology employed in this study can assist security practitioners in identifying and prioritizing the most influential risk factors to secure HWAs and encourage them to develop revamped or novel methods.

show abstract

Section: Fuzzy Topsismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Unified Decision-Making Technique to Evaluate Security Risks: A Healthcare Perspective

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Especially in the management of drugs, subjective factors (such as managers) significantly impact hospitals' failure to pay full attention to management, resulting in economic losses. Regarding personnel management, hospitals adhere to traditional management concepts, resulting in an imperfect internal management mechanism, low efficiency of communication and cooperation among departments, the lack of targeted training for personnel in different positions, and poor income growth, which seriously affects staff enthusiasm (Quasim et al, 2023). At the same time, the unclear division of various duties and positions leads to overlaps and deficiencies, resulting in mutual shirking of responsibilities and delays in the work.…”

Section: Introductionmentioning

confidence: 99%

Hospital Management Practice of Combined Prediction Method Based on Neural Network

Yang

2024

International Journal of Healthcare Information Systems and Informatics

View full text Add to dashboard Cite

In this article, the outpatient volume, hospitalization income and drug demand in hospital management are taken as the research objects, and a neural network combined prediction model is established to predict the outpatient volume with the fitting prediction results of cubic polynomial regression model and grey model as the input of the network and the actual statistical outpatient volume as the output. Lasso variable selection method is used to determine the main indexes affecting the income of inpatients in hospital, and a prediction model combining grey prediction and artificial neural network is established to predict the income of inpatients in hospital. By studying the key characteristics of hospital drug demand, BP neural network, RBF neural network and GRNN generalized regression neural network are selected to predict the drug demand. By solving the quadratic programming problem and according to the weight rules, a combination forecasting model based on neural network is established to predict the drug demand, and the accuracy and stability of the model are evaluated.

show abstract

“…Recently, fuzzy sets have been used extensively in healthcare MCDM for material and technique selection of best alternative (Mishra et al, 2020;Chakraborty & Saha, 2022b;Chaurasiya & Jain, 2022;Krishankumar et al, 2022;Salimian & Mousavi, 2022;Liu et al, 2022;Chakraborty & Saha, 2023;Quasim et al, 2023;Ghorbani et al, 2023). In the field of medicine, diagnosis of the disease is always accompanied by uncertainties and ambiguities (De Silva, 2018;Uzun Ozsahin et al, 2020).…”

Section: Introductionmentioning

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.

View full text Add to dashboard Cite

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.

show abstract

Fuzzy Decision-Making Method based Evaluation of Smart Healthcare Management

Cited by 4 publications

References 30 publications

Fuzzy-Based Unified Decision-Making Technique to Evaluate Security Risks: A Healthcare Perspective

Fuzzy-Based Unified Decision-Making Technique to Evaluate Security Risks: A Healthcare Perspective

Hospital Management Practice of Combined Prediction Method Based on Neural Network

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

Contact Info

Product

Resources

About