Literature DB >> 33525090

Implementation and validation of a new method to model voluntary departures from emergency departments. Running Title: Modeling Voluntary departures from emergency departments.

Carlo Ricciardi1, Alfonso Maria Ponsiglione2, Giuseppe Converso3, Ida Santalucia4, Maria Triassi4,5, Giovanni Improta4,5.   

Abstract

In the literature, several organizational solutions have been proposed for determining the probability of voluntary patient discharge from the emergency department. Here, the issue of self-discharge is analyzed by Markov theory-based modeling, an innovative approach diffusely applied in the healthcare field in recent years. The aim of this work is to propose a new method for calculating the rate of voluntary discharge by defining a generic model to describe the process of first aid using a "behavioral" Markov chain model, a new approach that takes into account the satisfaction of the patient. The proposed model is then implemented in MATLAB and validated with a real case study from the hospital "A. Cardarelli" of Naples. It is found that most of the risk of self-discharge occurs during the wait time before the patient is seen and during the wait time for the final report; usually, once the analysis is requested, the patient, although not very satisfied, is willing to wait longer for the results. The model allows the description of the first aid process from the perspective of the patient. The presented model is generic and can be adapted to each hospital facility by changing only the transition probabilities between states.

Entities:  

Keywords:  Markov Chain ; emergency department ; simulation ; voluntary departure

Mesh:

Year:  2020        PMID: 33525090     DOI: 10.3934/mbe.2021013

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.080


  4 in total

1.  Application of DMAIC Cycle and Modeling as Tools for Health Technology Assessment in a University Hospital.

Authors:  Alfonso Maria Ponsiglione; Carlo Ricciardi; Arianna Scala; Antonella Fiorillo; Alfonso Sorrentino; Maria Triassi; Giovanni Dell'Aversana Orabona; Giovanni Improta
Journal:  J Healthc Eng       Date:  2021-08-17       Impact factor: 2.682

2.  A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor.

Authors:  Atefeh Amindoust; Milad Asadpour; Samineh Shirmohammadi
Journal:  J Healthc Eng       Date:  2021-03-31       Impact factor: 2.682

3.  Lean Six Sigma Approach for Reducing Length of Hospital Stay for Patients with Femur Fracture in a University Hospital.

Authors:  Arianna Scala; Alfonso Maria Ponsiglione; Ilaria Loperto; Antonio Della Vecchia; Anna Borrelli; Giuseppe Russo; Maria Triassi; Giovanni Improta
Journal:  Int J Environ Res Public Health       Date:  2021-03-11       Impact factor: 3.390

4.  Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture.

Authors:  Carlo Ricciardi; Alfonso Maria Ponsiglione; Arianna Scala; Anna Borrelli; Mario Misasi; Gaetano Romano; Giuseppe Russo; Maria Triassi; Giovanni Improta
Journal:  Bioengineering (Basel)       Date:  2022-04-14
  4 in total

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