
==== Front
Data Brief
Data Brief
Data in Brief
2352-3409
Elsevier

S2352-3409(24)00791-1
10.1016/j.dib.2024.110827
110827
Data Article
An open-access dataset of emergency department admissions at a large teaching hospital in Iran
BaniHassan Zohreh banihasanz981@mums.ac.ir
a
Kazemi MohammadReza mr.kazemy@gmail.com
b
Jangi Majid JangiM@mng.mui.ac.ir
c
Tabesh Hamed Tabeshh@mums.ac.ir
a⁎
a Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
b Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran, Iran
c Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
⁎ Corresponding author. Tabeshh@mums.ac.ir
09 8 2024
10 2024
09 8 2024
56 11082710 11 2023
30 7 2024
5 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
In recent years, the use of electronic health records (EHRs) has become increasingly prevalent in healthcare settings, including emergency departments (EDs). EHRs offer numerous advantages, such as improved documentation, streamlined communication, and enhanced patient care. Additionally, EHRs contain vital information about patient care and treatment outcomes, which opens up exciting research opportunities. The objective of this study was to present a database comprising information regarding patients admitted to the emergency department of a large hospital.

In this study, we are introducing an open-access database sourced from the electronic health records of a general university hospital in Isfahan, Iran. The data were collected from patients admitted to the emergency department between March 2017 and March 2022, resulting in a database containing 143,582 ED stays. The database includes triage information, ED admission patients, and services. To ensure patient privacy, all patient-specific information has been removed from the records.

Keywords

Health informatics
Electronic health record
Emergency department
Data extraction
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pmcSpecifications TableSubject	Emergency Medicine	
Specific subject area	Electronic health records database	
Data format	Raw, Analyzed, Filtered	
Type of data	Table	
Data collection	Data were collected from patients who were admitted to the emergency department of the large teaching hospital (between March 2017 and March 2022). The developments of these datasets are the result of a 6-step construction process: (i) data extraction, (ii) data cleansing, (iii) data mapping, (iv) data integration, (v) data transformation and (vi) data enrichment.	
Data source location	Isfahan, Iran	
Data accessibility	Repository name: Mendeley data
Data identification number: https://doi.org/10.17632/vhzyyktrz5.1
Direct URL to data: https://doi.org/10.17632/vhzyyktrz5.1
Instructions for accessing these data: The data is hosted on a public repository. Use the direct link to access the data.	

1 Value of the Data

• Large databases of emergency department admissions offer an invaluable resource for research, quality improvement, resource planning, predictive analytics, and public health surveillance, ultimately benefiting both individual patients and the healthcare system as a whole. The purpose of this dataset is to facilitate data analysis in emergency care. It offers a substantial database of admissions to an ED at a large teaching hospital in Iran.

• This dataset not only serves researchers, analysts, and clinicians in the emergency care and epidemiological fields but also holds potential implications for healthcare policy. Insights derived from this data can inform decision-makers on resource allocation, staffing needs, and policy reforms aimed at improving patient outcomes and operational efficiency in emergency departments. Furthermore, analyses of trends and patterns in ED admissions could help shape national health strategies, emergency preparedness plans, and healthcare accessibility initiatives.

• The data can be reused in many ways. Researchers have the ability to examine comprehensive collections of clinical data without personally identifiable information at an individual patient level. They can then create statistical and machine learning models that could potentially be applied in different healthcare environments. For instance, a hypothetical study could analyze patterns of admission related to specific demographic variables, which might lead to targeted interventions for high-risk populations. Another example could involve using the dataset to develop predictive models for patient flow, helping hospitals optimize staffing and resource allocation during peak hours.

• Moreover, studies could explore the relationship between ED admission trends and public health incidents, such as outbreaks of infectious diseases, thereby enhancing surveillance efforts and guiding timely public health responses. The versatility of this dataset emphasizes its potential not only in advancing academic research but also in contributing to impactful changes in healthcare systems and policies at a systemic level.

2 Background

In a healthcare organization, there are multiple technology systems that healthcare professionals utilize to provide efficient healthcare services. Emergency departments (EDs) have been slowly incorporating electronic health record (EHR) systems into their operations since the 1970s [1]. Ever since EHRs were implemented in hospitals, healthcare professionals and scholars have been exploring ways to utilize them for easier and enhanced patient care [2]. Specifically, this is achieved by providing immediate access to patient data and enhancing the sharing of information among healthcare professionals [3], enabling them to make informed decisions in high-pressure situations.

Emergency departments are under significant strain due to the high number of patients and increased demand for resources [4]. This situation has led to overcrowding and delays in providing care [5], which has unfortunately resulted in higher rates of illness and death [6]. ED has limited resources, and the most valuable resource, human attention, is carefully allocated to ensure the best outcomes for patients. With the latest advances in algorithms, there is a promising opportunity to enhance the level of care provided in the emergency department. However, to conduct data-driven analyses, it is necessary to have open-access datasets [7].

3 Data Description

Data were gathered from individuals who received medical care at the emergency department of the main university hospital in Isfahan, Iran. The collected information includes a total of 143,582 instances of emergency department stays. The demographic attributes of the patients are summarized in Table 1. The age distribution of patients was 47.58 ± 22.38 (mean ± standard deviation) years, while the median age (IQR) was 42 (28–62) years. Gender distribution was 62.3 % male and 37.7 % female. Fig. 1 presents age and sex distribution of the patients. The most common chief complaints among patients included trauma, COVID-19, abdominal pain, dyspnea, and chest pain (Fig. 2). The frequency of the most common chief complaints by gender, age and date of referral were provided in Fig. 3, Fig. 4, Fig. 5, respectively. Fig. 6 illustrates length of stay (LOS) distribution of the data set.Table 1 Patients’ demographic characteristics.

Table 1:Variables	Levels	Frequency (%)	
Triage levels	Urgent	91,419 (63.7)	
	Non-urgent	52,163 (36.3)	
Gender	Male	89,479 (62.3)	
	Female	54,103 (37.7)	
Type of admission	Accompaniment	80,101 (55.8)	
	Pre-hospital emergency	51,967 (36.2)	
	Others	11,514 (8)	
Insurance organization	Voluntary	53,092 (37)	
	Health services	26,032 (18.1)	
	Armed Forces	13,257 (9.2)	
	Social Security	13,080 (9.1)	
	Other insurance	38,121 (26.6)	
Age (year)	0–1	565 (0.4)	
	2–5	3035 (2.1)	
	6–10	2278 (1.6)	
	11–20	11,748 (8.2)	
	21–40	42,500 (29.6)	
	41–60	36,589 (25.5)	
	>60	46,564 (32.6)	
Date of Referral	March - December 2017	25,633 (17.9)	
	January–December 2018	29,134 (20.3)	
	January–December 2019	32,604 (22.7)	
	January–December 2020	24,381 (17)	
	January–December 2021	25,911 (18)	
	January–March 2022	5919 (4.1)	
Discharge status	Discharge with physician's order	138,485 (96.5)	
	Discharge against physician's order	852 (0.6)	
	Death	2074 (1.4)	
	Follow-up	129 (0.1)	
	Transfer to another treatment center	1071 (0.7)	
	Others	998 (0.7)	
Length of stay (LOS)	0–6 h	44,679 (31.1)	
	6–12 h	15,428 (10.7)	
	12–24 h	10,425 (7.3)	
	24–48 h	11,159 (7.8)	
	2–7 days	38,267 (26.6)	
	>7 days	23,624 (16.5)	

Fig. 1 Age and sex distribution of the patients.

Fig. 1

Fig. 2 The frequency of chief complaints.

Fig. 2

Fig. 3 Frequency of the most common chief complaints by gender.

Fig. 3

Fig. 4 Frequency of the most common chief complaints by age.

Fig. 4

Fig. 5 Frequency of the most common chief complaints by date of referral.

Fig. 5

Fig. 6 LOS distribution of the data set.

Fig. 6

A schema was developed with three tables: ED_triage, ED_admission, and services. The purpose of the “ED_triage” table is to monitor the admission and discharge of patients from the ED triage during a single stay, identifiable by the triage_code. Two other tables, “ED_admission” and “services”, store information recorded throughout the patient's visit. The table names are chosen to reflect the data contained or its origin. To avoid duplicate entries, observations were checked for redundancy upon insertion using a primary key specific to each table. The primary key consisted of a combination of triage_code, PatientCode, enter_time, and ResidentDate. To protect patient privacy, all personal information was removed from the records.

The information obtained from patients during triage is stored in the ED_triage table. Triage is the initial assessment process where patients' health status is evaluated and the reason for their visit is determined. The ED_triage table consists of 28 columns, with some of the most important ones being: triage_code, ChiefComplaint, BloodpressureSystol, BloodpressureDiastol, PulseRate, RespiratoryRate, Temperature, O2Saturation, TriageGrade, and admission_(year, month, day, weekday). Vital signs collected during triage include the patient's temperature, heart rate, respiratory rate, oxygen saturation, systolic blood pressure, and diastolic blood pressure. Only numeric vital signs were retained in the de-identification process, even though they could have been recorded as free-text. The patient's reported pain level is available in the PainGrade column. The ChiefComplaint field contains the patient's stated reason for seeking care at the ED, following the ICD-10 standard.

The information concerning patients admitted to the ED is stored in the "ED_admission" table. These admissions are categorized into two groups. The first group consists of patients who are discharged directly from the ED (DischargeFromED = 1), while the second group consists of patients who are transferred to a ward and later discharged from there (DischargeFromED = 0). The ED_admission table is comprised of 26 columns, with some of the significant columns including triage_code, PaitientCode, age, gender, marital_status, ResidentDate_(year, month, day), DischargeDate_(year, month, day), status_on_discharge, service_count_lab, and service_count_graphy.

The services table contains various types of services recorded for patients during their hospital stay, including measurements, instruments, laboratory tests, and medical imaging. It consists of three columns: PatientCodes, wardCodes_id, and distanceOrderInHour. PatientCodes serves as the relational key with the ED_admission table. The time difference in hours between the ResidentDate and a service order is recorded in the distanceOrderInHour column. The wardCodes_id column represents the type of service ordered, with a value of 1 indicating hospital measurements, 2 indicating laboratory tests, 3 indicating medical imaging, and 4 indicating the use of instruments and drugs. Due to its large size of 8025,037 records, the services table is divided into 10 separate tables, with two tables created for each year.

4 Experimental Design, Materials and Methods

The developments of these datasets are the result of a 6-step construction process: (i) data extraction, (ii) data cleansing, (iii) data mapping, (iv) data integration, (v) data transformation and (vi) data enrichment.i. Data spanning five years (between March 2017 and March 2022) were extracted from the Hospital Information System (HIS) using Microsoft Excel Spreadsheet. The data were then converted from Excel into an open-source relational database (MySQL) to facilitate analysis. These datasets consist of 143,582 ED stays.

ii. The aim of this step is to find the easiest way to rectify quality issues, such as eliminating bad data. Outliers should be deleted because they provide only noisy information. The ranges of certain clinical features were extracted from previous research and subsequently verified by an emergency medicine specialist. For instance, regarding the age feature, any values exceeding 135 are deemed erroneous and subsequently eliminated from the dataset. In the case of the blood pressure feature, the value 12,080 is identified as invalid; however, it is corrected into two valid values: 120 for systolic blood pressure and 80 for diastolic blood pressure.

iii. Data mapping is the process of connecting a data field from one source to a data field in another source. Certain features, such as the triage_code, PatientCode and Archivecode were chosen as linkage keys to connect various sources.

iv. Data integration brings together data from one or more sources into a single destination in real time. Data mapping is necessary to understand the data integration path and process. By using proper mapping, several data sources (ED_triage, ED_admission, services, etc.) were integrated to create a comprehensive data source.

v. This step involves converting the data into a suitable format for analysis. Common transformation techniques, such as normalization and discretization (which involves dividing continuous data into discrete categories or intervals), were applied to several features. Categorization of certain clinical features was based on prior research and then confirmed by an expert in emergency medicine. This step was executed using a program written in Python by one of the authors of the article.

vi. In this step, various feature engineering techniques are applied to the data to effect the desired transformations. Based on the comments of 3 medical coding experts, the chief complaint field was changed to the ICD-10 standard. The dataset was enriched with this encoding.

Limitations

Data are gathered as part of regular clinical procedures, and their primary purpose is for patient care. However, these data can also be utilized for research purposes. It is important to note that the data might have unintentional biases due to how they are collected locally, there may be inaccurate values for measurements, and some documentation may be missing for the treatments provided. Additionally, certain interventions and significant events may not be well documented.

The frequency statistics for patients with various diseases may be biased because, during the COVID-19 pandemic, the hospital in question served as a primary center for treating this disease.

Given the importance of emergency data in developing machine learning models, the presence of missing or inaccurate data can significantly undermine the validity of these predictive models. Therefore, it is recommended to employ missing data imputation algorithms, such as K-Nearest Neighbors (KNN), in the subsequent steps to address this issue effectively.

Ethics Statement

Regarding ethical issues, this study has been assessed by the research council of Mashhad University of Medical Sciences (Reference Number: IR.MUMS.REC.1401.002). The study was approved because no identifying data have been reported.

CRediT Author Statement

Majid Jangi: Resources, Zohreh BaniHassan and MohammadReza Kazemi: Methodology, Software, Validation, Formal analysis, Data Curation, Writing - Original Draft, Visualization. Hamed Tabesh: Writing - Review & Editing, Supervision, Project administration.

Data Availability

An open‑access dataset of emergency department admissions at a large teaching hospital in Iran (Original data) (Mendeley Data)

Acknowledgments

The study received funding from 10.13039/501100004748 Mashhad University of Medical Sciences (Fund Number: 4001755 ).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References

1 Bloom B. Pott J. Thomas S. Gaunt D. Hughes T. Usability of electronic health record systems in UK EDs Emerg. Med. J. 38 6 2021 410 415 10.1136/emermed-2020-210401 33658268
2 Kariotis T. Harris K. Clinician perceptions of My Health Record in mental health care: medication management and sharing mental health information Aust. J. Prim. Health 25 1 2019 66 71 10.1071/PY17181 30636668
3 Mullins A. O'Donnell R. Morris H. Ben-Meir M. Hatzikiriakidis K. Brichko L. Skouteris H. The effect of My Health Record use in the emergency department on clinician-assessed patient care: results from a survey BMC Med. Inform. Decis. Mak. 22 1 2022 1 9 10.1186/s12911-022-01920-8 34983500
4 Xie F. Zhou J. Lee J.W. Tan M. Li S. Rajnthern L.S.O. Chee M.L. Chakraborty B. Wong A.K.I. Dagan A. Ong M.E.H. Benchmarking emergency department prediction models with machine learning and public electronic health records Sci. Data 9 1 2022 658 10.1038/s41597-022-01782-9 36302776
5 Savioli G. Ceresa I. Gri N. Bavestrello Piccini G. Longhitano Y. Zanza C. Piccioni A. Esposito C. Ricevuti G. Bressan M. Emergency department overcrowding: understanding the factors to find corresponding solutions J. Pers. Med. 12 2 2022 279 10.3390/jpm12020279 35207769
6 Pearce S. Marchand T. Shannon T. Ganshorn H. Lang E. Emergency department crowding: an overview of reviews describing measures causes, and harms Intern. Emerg. Med. 18 2023 1137 1158 10.1007/s11739-023-03239-2 36854999
7 Johnson A. Bulgarelli L. Pollard T. Celi L.A. Mark R. Horng S. MIMIC-IV-ED (version 2.2) PhysioNet 2023 10.13026/5ntk-km72
