PROFITABILITY ANALYSIS WITH THE FUZZY LOGIC: A HOSPITAL EXAMPLE

South African Journal of Industrial Engineering, 2020

The basic determining elements of healthcare services are the patient's satisfaction with the service provided by hospitals, which includes behavioural and sentimental aspects and the quality and efficiency of the hospitals themselves. Patients are sometimes very confused, and so express their views very vaguely. These imprecise responses of patients add to the complexity of evaluating quality. The involvement of multiple criteria, uncertainty, and qualitative factors significantly complicates the evaluation of the quality of a healthcare service. Fuzzy logic is a method by which indistinct or hazy responses can be taken up for quality analysis, such as the prioritisation of hospitals, departments, dimensions, etc. A pilot study was carried out in this study comprising 18 private hospitals with more than 100 beds that were selected in the twin city of Bhubaneswar and Cuttack in the state of Odisha, India. Nine quality dimensions were also selected from those used in the literature. A questionnaire survey was conducted in different departments of the hospitals using the nine dimensions. Patients' responses on a five-point Likert scale were first analysed statistically. Then ranking the dimensions and the hospitals was carried out using fuzzy analysis. The results could be used by healthcare service providers continually to improve their organisation.

Knowledge-Based Systems, 2016

Interest in the field of performance assessment of health care structures has grown in recent decades. In fact, the possibility of determining overall performances of health care structures plays a key role in the optimization of resource allocation and investment planning, as it contributes to reducing the uncertainty of future performance. In this context, key performance indicator (KPI) tools have been developed to assess the performance of health care structures from process, organizational, cost, financial, and output points of view. In practice, they are periodically calculated, and the effect of several KPIs on the overall performance of health care structures is determined by management through human judgment or software that provides synthetic dashboards. Given their non-stationary nature, performance assessment and forecasting are generally tackled by employing adaptive models, but these approaches cannot reflect the holistic nature of performance itself, nor take into account the impact of KPIs on the overall performances. In order to overcome these shortcomings, this study presents an expert system whose engine relies on fuzzy sets, in which the input-output relations and correlations have been modeled through inference rules based on time-series trends. The focus is on the financial performance assessment of a health care structure, such as a hospital. The approach is of an interdisciplinary kind, as several indicators were taken as inputs that relate to output, process, and cost KPIs, and their impact on the output measure, which is of a financial kind (namely the total reimbursement). The output measure calculated by the expert system was then compared with that predicted using only adaptive forecasting models, and the error with respect to the actual value was determined. Results showed that measures determined by fuzzy inference, able to effectively model actual input-output relations, outperform those of adaptive models.

2016

This paper presents the implementation of a performance evaluation procedure (PD) supported by quantitative techniques, using diffuse mathematics to reduce uncertainty. Is applies to a process key of a laboratory clinical, obtaining is a model that based its evaluation in indicators of performance related with them objectives of the company, allowing to the evaluator an alternative clear in the definition of criteria, in the weighting of each one of them and in the analysis of each result, decreasing its uncertainty for be used as tool of management and takes of decisions.

AJIT-e Online Academic Journal of Information Technology, 2017

Evaluation is an important subject for medical informatics domain. The investors and managers need to know the success level and poor sides of their information system to make improvements. There are many evaluation frameworks proposed for healthcare in the literature. Although healthcare is very suitable, none of the existing evaluation frameworks employ fuzzy logic methodologies. Our proposed expectation based evaluation framework in the previous work for hospital information systems is used for examining the difference and contribution of fuzzy logic use in evaluation. The results of the framework are recomputed both by crisp computation method. The difference between fuzzy and crisp computation is examined. The study shows that use of fuzzy logic makes a difference. Eight (8) of the 17 variables appeared to have statistically significant difference. Using crisp values in evaluation may result in loss of precision. We believe that fuzzy logic helps to obtain a more realistic evaluation by taking blurred boundaries into consideration.

Abstract : This article develops an estimation of a model for non-surgical and surgical medical service value for informal workers, under the social security system in Thailand. By using the data for the year 2010, provided by the Social Security Office, we analyzed and established the medical service value model. The data obtained from the fuzzy clustering analysis is used in creating a membership function in fuzzy logic. Subsequently, the result from this model, which is compensation for medical expenses, will be considered in the estimation of the monetary value of medical services, for informal workers. Moreover, the result of this method gave closer estimates to the real expenses comparing to the regression method. Keywords : fuzzy clustering; fuzzy logic; informal workers; medical service value;regression.

Advances in Healthcare Information Systems and Administration, 2020

One of the major concerns of the healthcare industry throughout the world is to provide better hospital service quality. Management and delivery of hospital healthcare services are achieved in a competitive environment in Turkey. For this reason, to make better decisions, the services provided by the public and private hospitals should be monitored and evaluated according to the viewpoint of medical stakeholders. This chapter presents a cause-and-effect, decision-making model in evaluating hospital service quality criteria. Since the decision-making process involves the vagueness of human judgments, a combination of fuzzy sets and decision-making trial and evaluation laboratory (DEMATEL) is used. Results of the study demonstrate that medical equipment level of the hospital, the attitude of nurses and medical staff to patients, pharmacists' advice on medicine preservation, medical staff with professional abilities, outpatient waiting time for medical treatment, and number and qua...

Journal of Biomedical Informatics, 2018

Hospital traditional cost accounting systems have inherent limitations that restrict their usefulness for measuring the exact cost of healthcare services. In this regard, new approaches such as Time Driven-Activity based Costing (TDABC) provide appropriate information on the activities needed to provide a quality service. However, TDABC is not flawless. This system is designed for conditions of relatively accurate information that can accurately estimate the cost of services provided to patients. In this study, the fuzzy logic in the TDABC model is used to resolve the inherent ambiguity and uncertainty and determine the best possible values for cost, capacity, and time parameters to provide accurate information on the costs of the healthcare services. This approach has not yet been tested and used in determining the costs of services of a healthcare setting. Therefore, the aim of this study is to present a new Fuzzy Logic-TDABC (FL-TDABC) model for estimating healthcare service costs based on uncertainty conditions in hospitals. The proposed model is implemented in a sample of the hospital laboratory section and the results are compared with the TDABC system. The TDABC model, by allocating the activity costs including fixed costs and not considering the uncertainty regarding the cost, capacity, and time required for each patient, often estimates the unused capacity and costs with a higher margin of error. The results show that the maximum difference in the prescribed costs was 4.75%, 3.72%, and 2.85% in blood bank, microbiology, and hematology tests, respectively, mostly due to uncertainty in the costs of consumables, equipment and manpower (on average 4.54%, 3.8%, and 3.59%, respectively). Also, The TDABC system, in comparison with the proposed system, estimates the unused capacity of the resource with more error. Cost of unused capacity derived using FL-TDABC were 80% of costs derived using TDABC. In conditions where the information is ambiguous, using the new system in hospitals can lead to a more accurate estimate of the cost compared to the TDABC system. Moreover, it helps hospital managers to make appropriate decisions about the use of capacity, capital budgeting, cost control, and etc.

Mathematics

Apart from the effects of treating those infected with COVID-19, the pandemic has also affected treatment for other diseases, which has been either interrupted or canceled. The aim of this paper is to provide a financial model for obtaining the cost overrun resulting from the worsening of illnesses and deaths for each of the causes considered. To achieve this, first deaths have been classified into causes of death and for each of these causes, an estimation has been made of the worsening condition of patients due to delay in treatment. Through these data, a fuzzy relation between deaths and the worsening condition of patients can be obtained. Next, the expertise process has been used to estimate cost overrun in relation to patients’ pathologies. The experts’ opinions have been aggregated using ordered weighted average (OWA). Lastly, using fuzzy logic again, a correction coefficient has been determined, which optimizes the future implementation of the proposed model without the need ...

IVT Network, 2020

The advantage of fuzzy logic is that it provides valuable flexibility for reasoning by considering the uncertainties of the situation. Sometimes where there is too much certainty at the outset, this involves important data or events from being overlooked. With fuzzy logic, the outcome of an operation can be expressed as a probability rather than as a certainty. When the scope is too narrow and something important is inadvertently excluded, this can undermine root cause analysis and hence the assessment of all hazards that require assessing when undertaking a risk assessment.

Open Journal of Business and Management, 2020

The aim of the paper is to show the implementation of fuzzy logic in business, administration and accounting, through the research published in Scopus. The results of the document focus on the following sections: 1) Fuzzy set theory, in business, administration and accounting. 2) Analysis of fuzzy logic bibliometrics in business, administration and accounting. 3) Identification and characterization of the documents or seminal documents most cited in applications of fuzzy logic in business, administration and accounting. The method used in this contribution is documentary research using the Scopus database and the VOSviewer science bibliometric analysis and mapping tool. In the future, this computational practice will focus on new diffuse models and the combination of these with other artificial intelligence techniques such as neural networks, genetic algorism, forage bacteria, among others.