# README for OutPatientDepartment Dataset
**Dataset Title:** Synthetic Dataset of Emergency Healthcare Services - OutPatientDepartment
**Author:** Ferreira, Marco
**Date Created:** 2024
**Citation:** Ferreira, M. (2024). Synthetic Dataset of Emergency Healthcare Services - OutPatientDepartment [Data set].
**File Size:** 18 KB
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### Purpose of the Dataset
The "OutPatientDepartment" dataset is part of a synthetic data project aimed at simulating workflows within emergency healthcare services.
This specific dataset models patient flow and experience in the outpatient department, capturing details on length of stay, satisfaction levels, and whether patients are new or returning.
It is intended to facilitate research and analysis on patient experience, resource allocation, and service efficiency in outpatient settings.
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### Data Summary
This dataset includes detailed information on each patient's visit to a simulated outpatient department, focusing on variables that impact patient satisfaction, length of stay, and departmental efficiency.
#### Key Variables
- **Patient ID**: Unique identifier for each patient in the dataset, represented as an integer.
- **Patient Type**: Category indicating the urgency or type of patient. Possible values are integers representing different patient types.
- **Exiting Time**: Timestamp indicating when a patient exited the simulation.
- **Length of Stay (min)**: Total time, in minutes, the patient spent in the simulation.
- **LOS without Queues (min)**: Calculated time spent in simulation without waiting in queues, in minutes.
- **Satisfaction %**: Satisfaction level of the patient, represented as a percentage.
- **New Patient?**: Indicates whether the patient is a new or returning patient ("Yes" or "No").
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### Usage Notes
This dataset can be utilized for simulation, research, and analysis purposes, particularly in studies related to:
- Patient satisfaction analysis in outpatient settings
- Service time optimization
- Resource management and patient flow studies
- Evaluation of waiting times and their impact on patient experience
### Technical Requirements
This dataset is provided in CSV format, compatible with widely used data analysis software and programming languages, including Python (Pandas), R, and Microsoft Excel.
### Access Rights and Licensing
- **Access Rights**: Open Access
The dataset is freely available for public access without restrictions.
- **License**: Creative Commons Attribution 4.0 International (CC BY 4.0)
Users are allowed to share, adapt, and build upon this dataset, provided proper attribution is given to the original author (Ferreira, M., 2024). For more information on the license, please visit: [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).
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### FAIR Compliance and Metadata
This dataset aligns with the FAIR principles (Findable, Accessible, Interoperable, and Reusable), ensuring ease of access, use, and integration in different analytical contexts.
- **Findability**: This README file provides detailed metadata descriptions and explanations of the dataset variables.
- **Accessibility**: Available openly in standard CSV format for easy access and analysis.
- **Interoperability**: Designed to work with common data analysis tools, with straightforward variable names and values.
- **Reusability**: Licensed under CC BY 4.0, supporting reuse, adaptation, and redistribution with appropriate credit.
### Additional Information
This dataset is part of a larger synthetic dataset project focused on emergency healthcare services.
For further inquiries, comments, or requests for related datasets, please reach out to the author.
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### File Structure and Additional Metadata
- **File Name**: `OutPatientDepartment.csv`
- **File Size**: 18 KB
- **Row Count**: 237
- **Columns**: 7 columns (as described in the Key Variables section above)
This dataset is a component of a broader synthetic dataset project, with each individual dataset in the collection representing different aspects of healthcare service workflows.
The OutPatientDepartment dataset specifically provides insights into patient satisfaction, wait times, and length of stay in outpatient settings.
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