Successful implementation of discrete event simulation: the case of an
Italian emergency department
- URL: http://arxiv.org/abs/2006.13062v1
- Date: Tue, 23 Jun 2020 14:38:05 GMT
- Title: Successful implementation of discrete event simulation: the case of an
Italian emergency department
- Authors: Arthur Kramer and Clio Dosi and Manuel Iori and Matteo Vignoli
- Abstract summary: This paper focuses on the study of a practical management problem faced by a healthcare it emergency department (ED) located in the north of Italy.
The objective of our study was to propose organisational changes in the selected ED, aiming at improving key performance indicators related to patient satisfaction, such as the waiting time.
- Score: 1.7205106391379026
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper focuses on the study of a practical management problem faced by a
healthcare {\it emergency department} (ED) located in the north of Italy. The
objective of our study was to propose organisational changes in the selected
ED, which admits approximately 7000 patients per month, aiming at improving key
performance indicators related to patient satisfaction, such as the waiting
time. Our study is based on a design thinking process that adopts a {\it
discrete event simulation} (DES) model as the main tool for proposing changes.
We used the DES model to propose and evaluate the impact of different improving
scenarios. The model is based on historical data, on the observation of the
current ED situation, and information obtained from the ED staff. The results
obtained by the DES model have been compared with those related to the existing
ED setting, and then validated by the ED managers. Based on the results we
obtained, one of the tested scenarios was selected by the ED for
implementation.
Related papers
- Recent Advances in Predictive Modeling with Electronic Health Records [73.31880579203012]
utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics.
Deep learning has demonstrated its superiority in various applications, including healthcare.
arXiv Detail & Related papers (2024-02-02T00:31:01Z) - MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data
Augmentation [58.93221876843639]
This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion.
It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space.
It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
arXiv Detail & Related papers (2023-10-04T01:36:30Z) - Textual Data Augmentation for Patient Outcomes Prediction [67.72545656557858]
We propose a novel data augmentation method to generate artificial clinical notes in patients' Electronic Health Records.
We fine-tune the generative language model GPT-2 to synthesize labeled text with the original training data.
We evaluate our method on the most common patient outcome, i.e., the 30-day readmission rate.
arXiv Detail & Related papers (2022-11-13T01:07:23Z) - Benchmarking Predictive Risk Models for Emergency Departments with Large
Public Electronic Health Records [7.928862476020428]
There is an absence of widely accepted ED benchmarks based on large-scale public EHR.
We proposed a public ED benchmark suite and obtained a benchmark dataset containing over 500,000 ED visits episodes from 2011 to 2019.
Our codes are open-source so that anyone with access to MIMIC-IV-ED could follow the same steps of data processing, build the benchmarks, and reproduce the experiments.
arXiv Detail & Related papers (2021-11-22T06:51:11Z) - Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration [55.88616573143478]
Outcome prediction from clinical text can prevent doctors from overlooking possible risks.
Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets.
We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
arXiv Detail & Related papers (2021-02-08T10:26:44Z) - Emergency Department Optimization and Load Prediction in Hospitals [9.90154803957148]
We developed a tool powered by machine learning models to forecast ED arrivals and ED patient volume.
In this paper, we discuss the results from our predictive models, the challenges, and the learnings from users' experiences with the tool in active clinical deployment.
arXiv Detail & Related papers (2021-02-06T21:52:51Z) - Effect of different patient peak arrivals on an Emergency Department via
discrete event simulation [0.0]
We propose a model to study the patient flows through a medium-size ED located in a region of Central Italy recently hit by a severe earthquake.
In particular, our aim is to simulate unusual ED conditions, corresponding to critical events (like a natural disaster) that cause a sudden spike in the number of patient arrivals.
The model provides a valid decision support system for the ED managers also in defining specific emergency plans to be activated in case of mass casualty disasters.
arXiv Detail & Related papers (2021-01-29T06:55:53Z) - Adversarial Sample Enhanced Domain Adaptation: A Case Study on
Predictive Modeling with Electronic Health Records [57.75125067744978]
We propose a data augmentation method to facilitate domain adaptation.
adversarially generated samples are used during domain adaptation.
Results confirm the effectiveness of our method and the generality on different tasks.
arXiv Detail & Related papers (2021-01-13T03:20:20Z) - Modeling patient flow in the emergency department using machine learning
and simulation [0.0]
This paper presents a novel application of machine learning (ML) within the simulation to improve patient flow within an emergency department (ED)
An ML model used within a real ED simulation model to quantify the effect of detouring a patient out of the ED on the length of stay (LOS) and door-to-doctor time (DTDT)
The used policy combined with adding specific ED resources achieve 9.39% and 8.18% reduction in LOS and DTDT, respectively.
arXiv Detail & Related papers (2020-11-22T17:42:53Z) - Predicting Clinical Diagnosis from Patients Electronic Health Records
Using BERT-based Neural Networks [62.9447303059342]
We show the importance of this problem in medical community.
We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence.
We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
arXiv Detail & Related papers (2020-07-15T09:22:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.