HOSPITAL EMERGENCY ROOM

Project overview

The “ER Dashboard for Patient Traffic Monitoring” initiative seeks to give hospital personnel and management a thorough tool to track and examine important metrics pertaining to the Emergency Room (ER) operations. This dashboard will make it possible to make data-driven decisions, allocate resources more effectively, and improve patient care in the ER.

Objects of the Project:

Total Visitors: Give hospital workers current data on the total number of persons who have visited the ER, assisting them in understanding the workload.

Patient characteristics:

Displaying the number of patients by gender enables analysis and resource planning based on gender.

To better understand the age distribution in the ER, group patients by age.

To find any potential discrepancies in healthcare access, analyze the distribution of patients by race.

Typical Waiting Period:

Determine and show the monthly average for patient wait times in the emergency room.

Allowing previous comparisons will help you spot waiting time trends.

satisfaction of the patient

Utilize surveys to gauge patient satisfaction and present average ratings by month.

Utilize feedback data to enhance the ER’s level of care and service.

Obtaining Referrals:

Sort individuals into groups according to the source of their referral to the department (such as a primary care doctor, a walk-in, or an ambulance).

Decide which sources are responsible for the greatest ER traffic.

Analyzing time:

Identify patterns and peak days by breaking out ER visits by day of the week.

To properly distribute resources, consider the distribution of patients by time of day.technical application

The project will entail the following in order to meet these goals:

Patient surveys and the hospital’s electronic health records (EHR) system are used to collect data.

Data processing is the process of preparing raw data for analysis by cleaning and converting it.

Database integration is the process of storing data in a private, searchable database.

Development of a user-friendly and interactive dashboard using software like Power BI, Tableau, or specialized web design.

Visualization: Creating graphs, tables, and charts to depict the important metrics.

Implementing user authentication and access control to ensure the security and privacy of data.

Automated data updates are set up to keep the dashboard up to date.Project Benefits

Improved Patient Care: By keeping an eye on ER wait times and traffic, the hospital can improve patient care and shorten wait times.

Resource Optimization: Data-driven insights will make it possible to allocate resources more effectively, including personnel and equipment.

Patient Satisfaction: Tracking patient satisfaction levels and acting on their input helps boost satisfaction.

On the basis of current data, hospital management are able to make well-informed judgments.

Enhanced Efficiency: Investigating referral sources and temporal trends can make ER operations more effective.

HOSPITAL ER QUERIES

CLEANING AND ANALYSIS

SELECT * FROM [Hospital ER]

ALTER TABLE [Hospital ER]

ADD agegroup Varchar (10);

UPDATE [Hospital ER]

SET agegroup =

Case

when patient_age Between 1 and 9 then ‘1–9’

when patient_age Between 10 and 19 then ‘10–19’

when patient_age Between 20 and 29 then ‘20–29’

when patient_age Between 30 and 39 then ‘30–39’

when patient_age Between 40 and 49 then ‘40–49’

when patient_age Between 50 and 59 then ‘50–59’

when patient_age Between 60 and 69 then ‘60–69’

when patient_age Between 70 and 79 then ‘70–79’

else ‘80+’

end;

SELECT agegroup,patient_age FROM [Hospital ER];

QUESTIONS AND ANSWERS

You task is to build dashboard for Hospitals in order to monitor their patient

traffic in Emergency Room Here are some questions to answer

SELECT * FROM [Hospital ER];

— -1. Total no of people visiting the emergency room —

SELECT COUNT(*) AS Totalvisits FROM [Hospital ER];

— -2. No of patients by gender — -

SELECT patient_gender AS Gender,

count(*) AS No_of_patient_by_gender FROM [Hospital ER]

GROUP BY patient_gender;

— -3. Average waiting time by month — -

SELECT DATEPART(Year,date) AS year,

DATEPART(Month,date) AS Month,

AVG(patient_waittime) AS Average_waittime FROM [Hospital ER]

GROUP BY DATEPART(Year,date),DATEPART(Month,date)

ORDER BY Year,Month;

— -4. Average patient satisfaction by month — -

SELECT DATEPART(Year,date) AS year,

DATEPART(Month,date) AS Month,

AVG(patient_sat_score) AS Average_satisfaction FROM [Hospital ER]

GROUP BY DATEPART(Year,date),DATEPART(Month,date)

ORDER BY Year,Month;

— -5. No of Patients by Age in ER — -

SELECT agegroup,

COUNT(distinct patient_id) AS no_of_patient_by_Age

FROM [Hospital ER]

GROUP BY agegroup

ORDER BY agegroup;

— -6. No of patients by department referral — -

SELECT department_referral,

COUNT(distinct patient_id) AS no_of_patient_by_department_referral

FROM [Hospital ER]

GROUP BY department_referral

ORDER BY department_referral;

— -7. No of patients by race — -

SELECT patient_race,

COUNT(*) AS no_of_patient

FROM [Hospital ER]

GROUP BY patient_race

order by no_of_patient desc;

— -8. No of patients by week day — -

SELECT

DATENAME(WEEKDAY,[date]) AS weekday,

COUNT(*) AS no_of_patients

FROM [Hospital ER]

GROUP BY DATENAME(WEEKDAY,[date])

ORDER BY MIN([date]);

— -9.No of patients by time — -

SELECT

DATEPART(HOUR,date) AS hour,

DATEPART(MINUTE,date) AS Minute,

COUNT(*) AS No_of_patients

FROM [Hospital ER]

GROUP BY DATEPART(HOUR,date),DATEPART(MINUTE,date)

ORDER BY hour,Minute;