Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms.

Hospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital.

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources.

A healthcare resource allocation generally plays a vital role in the number of patients treated () and the patient waiting time () in healthcare institutions. This study aimed to estimate and as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient () in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the of resources cost.

A cost analysis with the discrete-event simulation application in nurse and doctor employment management.

Aim: This study aimed to analyse the treatment cost of a patient, depending on the number of patients treated, patient waiting times, and the number of nurses and doctors employed in an emergency department of a private hospital.

Background: Within health systems, changes in health care resources can be very costly, especially if these changes are long-term. The discrete-event simulation method described in this paper allows for the monitoring and analysis of complicated changes in real systems by using computer-based modelling.

Desirability Optimization Models to Create the Global Healthcare Competitiveness Index.

The aim of this research is to enhance desirability optimization models to create a global healthcare competitiveness index (GHCI) covering 53 countries with gross domestic product per capita (GDP PC) of over $10,000. The GHCI is defined as an index that reveals the progress and quality of the healthcare systems in countries providing their patients with easier access opportunities to healthcare services within the scope of this work. Methods of statistical analysis have been adopted together with optimization models and techniques in this research.