Data Analytics Assignment · Tableau

Hospital ER
Operations Analysis

Analyse emergency room operations using patient-level data. Explore admission patterns, waiting times, patient demographics, department referrals, and satisfaction scores to uncover opportunities for better healthcare service delivery.

Intermediate Estimated time: 2–3 hours Software: Tableau Dataset: 9,216 records

Explore the assignment →

01 · Assignment overview

Turn hospital data into operational insights

Emergency departments handle a continuous flow of patients. Understanding patient volumes, admissions, waiting times, and satisfaction can help healthcare administrators evaluate service performance and identify areas for improvement.

Understand Patient Flow

Analyse patient volumes, demographics, admission patterns, and department referrals.

Evaluate ER Performance

Measure waiting times, admission rates, and patient satisfaction to understand operational performance.

Build an Interactive Dashboard

Combine KPIs and visualizations into a dashboard that communicates important findings clearly.

Business scenario: You are a data analyst working with a hospital's emergency department. Hospital management wants to understand patient flow, admission patterns, department workload, waiting times, and patient experience. Your task is to analyse the available data and present your findings through an interactive Tableau dashboard.

02 · Dataset explorer

Get familiar with your dataset

Download the Hospital ER dataset, review its structure, and understand the available fields before starting your analysis.

9,216

Patient records

Dataset columns

50.04%

Admission rate

35.26

Average waiting time (minutes)

↓ Download Hospital ER Dataset

Data Dictionary

Use this reference to understand the available fields.

Column	Description	Data Type
Patient Id	Unique patient record identifier	String
Patient Admission Date	Date and time of the patient's ER visit	DateTime
Patient First Inital	First initial of the patient's name (as supplied)	String
Patient Last Name	Patient's last name	String
Patient Gender	Gender category: M, F, or NC	String
Patient Age	Patient's age in years	Integer
Patient Race	Patient's recorded race category	String
Department Referral	Department to which the patient was referred	String
Patient Admission Flag	Whether the patient was admitted: True / False	Boolean
Patient Satisfaction Score	Recorded patient satisfaction score (0–10)	Decimal
Patient Waittime	Patient waiting time in minutes	Integer
Patients CM	Additional field with values 0 and 1; definition not supplied	Integer

Sample Records

Search the sample records to explore the dataset.

Patient ID	Admission Date	Gender	Age	Department	Admitted	Satisfaction	Wait Time
145-39-5406	20-03-2024 08:47	M	69	Not Specified	False	10	39
316-34-3057	15-06-2024 11:29	M	4	Not Specified	True	Missing	27
897-46-3852	20-06-2024 09:13	F	56	General Practice	True	9	55
358-31-9711	04-02-2024 22:34	F	24	General Practice	True	8	31
289-26-0537	04-09-2024 17:48	M	5	Orthopedics	False	Missing	10
255-51-2877	20-04-2023 00:13	M	58	Not Specified	False	Missing	59
465-97-0990	23-08-2023 08:26	F	68	Not Specified	True	Missing	43
157-31-7520	29-07-2023 16:57	F	47	Not Specified	True	Missing	23

Important data-quality observations
The dataset contains 6,699 missing Patient Satisfaction Score values and 5,400 missing Department Referral values. Do not automatically remove these records. Treat missing department referrals as "Not Specified" when analysing department distribution. Calculate average satisfaction using only records with a valid score.

Important date note: Patient Admission Date is provided in DD-MM-YYYY HH:MM format. Convert it to a proper DateTime field in Tableau before building time-based visualizations. The supplied records span April 2023 to October 2024.

03 · Guided challenges

KPI Requirements

Create the following calculated measures to evaluate emergency department activity, admission performance, and patient experience.

01 Basic

Total Patients

Determine the total number of patient records in the dataset to understand the overall volume of ER visits.

COUNT(Patient Id)

Count Patient Id. Each record represents one patient visit in this dataset.

02 Basic

Total Admitted Patients

Calculate how many patients were admitted after their emergency room visit.

Admission Flag = True COUNT(Patient Id)

Filter Patient Admission Flag to True and count Patient Id. Alternatively, create a conditional calculated field.

03 Intermediate

Admission Rate

Calculate the percentage of patients admitted compared with the total number of patient records.

Admitted Patients / Total Patients

Divide the number of records where Patient Admission Flag is True by the total patient count. Format the result as a percentage.

04 Basic

Average Patient Wait Time

Calculate the average time patients wait before receiving emergency department service.

AVG(Patient Waittime)

Use AVG on Patient Waittime. Display the result in minutes and give the KPI an appropriate label.

05 Basic

Average Patient Age

Determine the average age of patients visiting the emergency department.

AVG(Patient Age)

Use AVG on Patient Age. Consider displaying the result rounded to one decimal place.

06 Intermediate

Average Satisfaction Score

Calculate the average patient satisfaction score to evaluate the reported patient experience.

AVG(Patient Satisfaction Score)

Calculate the average using only non-null satisfaction scores. Also show the number of valid satisfaction responses so viewers understand the sample size.

07 Basic

Total Patients by Gender

Identify the number of patients across the gender categories to understand the demographic distribution.

Patient Gender COUNT(Patient Id)

Group the patient count by Patient Gender. Retain M, F, and NC as separate categories rather than excluding NC.

08 Intermediate

Patients by Department Referral

Determine how many patients were referred to each department and identify the departments receiving the highest workload.

Department Referral COUNT(Patient Id)

Group the patient count by Department Referral. Create a separate "Not Specified" category for missing referrals so all patient records are represented.

Tableau tip: Use calculated fields for admission status and admission rate. For the admission rate, a simple calculation is SUM(IF [Patient Admission Flag] THEN 1 ELSE 0 END) / COUNT([Patient Id]). Format the result as a percentage.

04 · Visualization challenges

Chart Requirements

Create the following visualizations in Tableau. Focus on selecting appropriate chart types, dimensions, measures, sorting, labels, and tooltips.

01 Donut Chart

Patient Distribution by Gender

Show the distribution of patients across the gender categories. Highlight the proportion of each category in the total patient population.

Patient Gender COUNT(Patient Id)

Use Patient Gender as the category and patient count as the measure. Display category labels, patient counts, and percentages of total. Include NC as a separate category.

02 Line Chart

Patient Visits Over Time

Track patient visits over time using admission dates to identify trends, fluctuations, and changes in ER activity.

Patient Admission Date COUNT(Patient Id)

Convert Patient Admission Date to DateTime. Use MONTH or DAY of Patient Admission Date on the horizontal axis and patient count on the vertical axis. Monthly aggregation is recommended for the main trend.

03 Horizontal Bar Chart

Patients by Department Referral

Compare patient volumes across departments and identify which departments receive the highest number of referrals.

Department Referral COUNT(Patient Id)

Sort departments by patient count in descending order. Include a separate Not Specified category for missing referrals. Add data labels to make comparisons easier.

04 Stacked Bar Chart

Admission Status by Gender

Compare admitted and non-admitted patients across gender categories to understand differences in admission patterns.

Patient Gender Patient Admission Flag

Place Patient Gender on the category axis and patient count as the measure. Use Patient Admission Flag to create the stacked segments for True and False.

05 Bar Chart

Average Waiting Time by Department

Compare average patient waiting times across departments to identify potential operational bottlenecks.

Department Referral AVG(Patient Waittime)

Group average Patient Waittime by Department Referral. Sort the bars by average waiting time. Include Not Specified as a separate category and label waiting times in minutes.

06 Column Chart

Patient Satisfaction Score Distribution

Show the frequency of satisfaction scores from 0 to 10 to understand the distribution of patient feedback.

Patient Satisfaction Score COUNT(Patient Id)

Place satisfaction score on the horizontal axis and count the valid score records on the vertical axis. Keep scores in numerical order from 0 to 10. Do not treat missing scores as zero.

07 Stacked Bar Chart

Patient Distribution by Age Group

Group patients into age categories and compare admitted versus non-admitted patients across those groups.

Patient Age Patient Admission Flag

Create an Age Group calculated field using the suggested age bands below. Use patient count as the measure and admission status as the stacked category.

Calculated field reference

Suggested Patient Age Groups

Use these age categories for the age-group visualization. Keep the categories consistent throughout your dashboard.

Age Range	Category
0–17	Children
18–35	Young Adults
36–50	Adults
51–65	Older Adults
Above 65	Seniors

Suggested Tableau calculation: IF [Patient Age] <= 17 THEN "Children" ELSEIF [Patient Age] <= 35 THEN "Young Adults" ELSEIF [Patient Age] <= 50 THEN "Adults" ELSEIF [Patient Age] <= 65 THEN "Older Adults" ELSE "Seniors" END

05 · Go beyond the basics

Advanced Challenges

Once you complete the required KPIs and visualizations, attempt these additional challenges to develop deeper analytical insights.

Bonus 01

Admission Rate by Age Group

Identify which age groups have the highest admission rates. Compare the percentage of admitted patients across the different age categories.

Bonus 02

Peak ER Hours

Analyse patient arrivals by hour of day to identify peak periods of emergency department activity and potential staffing requirements.

Bonus 03

Waiting Time Distribution

Group waiting times into ranges such as 10–20, 21–30, 31–40, 41–50, and 51–60 minutes. Identify the most common waiting-time range.

Bonus 04

Satisfaction by Admission Status

Compare average satisfaction scores for admitted and non-admitted patients. Use only records with valid satisfaction scores.

Turn hospital data into operational insights

Understand Patient Flow

Evaluate ER Performance

Build an Interactive Dashboard

Get familiar with your dataset

Data Dictionary

Sample Records

KPI Requirements

Total Patients

Total Admitted Patients

Admission Rate

Average Patient Wait Time

Average Patient Age

Average Satisfaction Score

Total Patients by Gender

Patients by Department Referral

Chart Requirements

Patient Distribution by Gender

Patient Visits Over Time

Patients by Department Referral

Admission Status by Gender

Average Waiting Time by Department

Patient Satisfaction Score Distribution

Patient Distribution by Age Group

Suggested Patient Age Groups

Advanced Challenges

Admission Rate by Age Group

Peak ER Hours

Waiting Time Distribution

Satisfaction by Admission Status

Build Your Hospital ER Dashboard

Suggested Dashboard Layout

Recommended Dashboard Filters

Your Submission Checklist

Evaluation Criteria

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