Emergency Department Performance Analysis

This assignment analyzes data collected from an emergency department to evaluate its performance. The analysis covers various aspects such as average age of patients, waiting times, clinic time spent, departure times, and the most prevalent illnesses treated. The findings highlight slow service delivery due to factors like limited workforce, inadequate equipment, and manual record-keeping. Recommendations are provided to improve the department's efficiency by implementing a computerized data system, increasing staffing levels, and modernizing equipment.

Analysis of emergency department dataset 1
ANALYSIS OF EMERGENCY DEPARTMENT DATASET
By (student name)
[Student class]
[lecture name]
[Univwersity Name]
[University Town Name]
[Date]

Analysis of emergency department dataset 2
Assignment 1
Group:
Mind map of the problem
The hospital is an institution that is set to offer various services to the patients who visits
to be given some medical services, the New South Wales hospital is a health medical institution
that handles the emergency issues from the patients referred there or who visits there by their
owns, however the emergency department is set up for handling such emergency issues that
could arise.
The New South Wales hospital senior management being the management team of this
hospital would like to get a report concerning the possible issues that could be causing the delay
of services to the patients and the solution for any possible means of improving the emergency
department performance ,this is after a noting that the patients queues had remained long which
in turn causes overcrowding which could lead to deaths of patients ,wastage of time, lack of on
time diagnosis and therapies.
After the process of brainstorming as group about the New South Wales hospital
emergency department where the patients required the medical attention we were able to get the
following as the findings:
i. The emergency department could handle various types of patient’s problems like the
blood pressure, diabetes and even other ICU critical conditions.
ii. There was a patient crowding due to the staying for long in the Emergency departments
who waits to be treated.
iii. There were various stages that patients were to go through while being attended to in the
emergency department.
iv. There were incidences of inferior clinical outcomes like rise in the mortality rates, delay
in the therapy administration to the patients.
v. The emergency department is in shortage of the staff members and the clinicians to serve
the patients.
vi. The department also has a very little number of the diagnosis machines and most of them
are very old and obsolete to offer quick services to the patients.

Analysis of emergency department dataset 3
Project justification and scope
In order to come up with the possible solution for the New South Wales hospital
challenges there will be the analysis of the various activities and the time spent for each process.
The patients who visits the Emergency department are required to go through the various
stages and processes which starts by the arrival at the reception section, triangulations and
waiting, patients clinical caring and the departure period ,all these stages consumes time for each
individual students and in the process they form a crowd and hence delays for the attendance.
As a result of those challenges the analysis will be conducted about the emergency
department which will have various goals as below:
i. To determine how many processes that the patients who attends the emergency
department possibly have to go through.
ii. To determine the duration for each and every stage of processes that the patients go
through the emergency departments.
iii. To detect the process that mostly consumes the highest period of time during the patients
treatment in the department.
iv. To determine the number of most probably occurring diseases treated in the emergency
department.
v. To check the number of the clinical officers who do serve the patients in the emergency
department.
vi. To determine the number of equipments used to diagnose the patients who visits the
emergency departments.
vii. To determine the possible number of the patients who seeks medication in the emergency
department in a given day.
Data dictionary
The analysis will require a set of data that is required to be used to do analysis and will
eventually be used to give statistical results and the graphical outputs, however the following
will be the set of data set that are used in the analysis process which entails the following:

Analysis of emergency department dataset 4
i. Patient name
ii. Patients arrival time
iii. Patients triage
iv. Patients registration and waiting period
v. Clinical care time
vi. Departure time
vii. Diagnosed disease.
However the group members are able to collect a sample data from the emergency
department that will be used to conduct the analysis process ,therefore the below data will be
used to perform the data analysis that will assist the group in getting the solution and giving the
possible recommendation for the ways to improve on the department’s services performance .
Data sample
Diag1_
Commonwealth
Person_
Identifier Departure_Time Arrival_Time
Mode_Of_
Separation
Ed_Visit
_Type
Triage_Ca
tegory Diagnosis_Codeset
Time
Spent
N390 3739670 7/1/2012 4:05:00 7/1/2012 0:01:00 4 1 3 ICD10AM 4.066
J050 3937880 7/1/2012 2:02:00 7/1/2012 0:17:00 4 1 2 ICD10AM 1.750
R55 4222 7/1/2012 2:32:00 7/1/2012 0:18:00 1 1 3 ICD10AM 2.233
2642712 7/1/2012 2:00:00 7/1/2012 0:21:00 6 1 4 1.650
2359474 7/1/2012 5:45:00 7/1/2012 0:30:00 6 1 4 5.250
A084 3877880 7/1/2012 7:05:00 7/1/2012 0:40:00 4 1 4 ICD10AM 6.417
2764768 7/1/2012 1:11:00 7/1/2012 0:42:00 6 1 4 0.483
3811904 7/1/2012 3:18:00 7/1/2012 0:49:00 6 1 4 2.483
S008 1207288 7/1/2012 1:07:00 7/1/2012 0:54:00 4 1 4 ICD10AM 0.217
2966358 7/1/2012 1:49:00 7/1/2012 1:08:00 6 1 4 0.683
R600 3500564 7/1/2012 7:02:00 7/1/2012 1:18:00 4 1 4 ICD10AM 5.733
2581558 7/1/2012 2:40:00 7/1/2012 1:21:00 6 1 4 1.317
2784768 7/1/2012 2:52:00 7/1/2012 1:25:00 6 1 4 1.450
2974366 7/1/2012 3:04:00 7/1/2012 1:34:00 6 1 4 1.500
J050 2579970 7/1/2012 4:06:00 7/1/2012 1:36:00 4 1 3 ICD10AM 2.500
1825292 7/1/2012 4:47:00 7/1/2012 2:02:00 6 1 4 2.750
J069 3957880 7/1/2012 7:33:00 7/1/2012 2:04:00 4 1 4 ICD10AM 5.483
Z001 2220324 7/1/2012 7:36:00 7/1/2012 2:35:00 4 1 4 ICD10AM 5.017
R681 2704768 7/1/2012 6:19:00 7/1/2012 3:05:00 4 1 3 ICD10AM 3.233
J050 2240324 7/1/2012 6:22:00 7/1/2012 3:16:00 4 1 4 ICD10AM 3.100
1263450 7/1/2012 4:46:00 7/1/2012 4:18:00 6 1 4 0.467
J219 3581364 7/1/2012 8:17:00 7/1/2012 4:20:00 1 1 3 ICD10AM 3.950
J050 366328 7/1/2012 7:39:00 7/1/2012 4:29:00 4 1 4 ICD10AM 3.167
3189346 7/1/2012 5:12:00 7/1/2012 4:50:00 6 1 4 0.367
Summary Of Dataset Exploration

Analysis of emergency department dataset 5
The above is the set of the data collected illustrating the data dictionary of the emergency
department that will be used to conduct the analysis to determine the possible causes of poor
performance and the results will give the incites on how to improve the services offered in the
emergency department.
Summaries of data exploration.
The data obtained from the emergency department is going to be used in the exploration
process this will enable us to get the facts and using the drawn facts one will be able to give the
conclusions and recommendations about the improving of the emergency department.
Collection of initial data.
The information to be used was collected through various means from the emergency
department , some of these methods are through observations , interviews and the from the
secondary sources from the organisation.
Data formats.
Below is the consolidated data set which is made of various variables which includes the
Patient name, Patients arrival time, Patients triage(years), Patients registration and waiting
period(hrs), Clinical care time (hours), Departure time (hours) and Diagnosed diseases as shown
in below sample of the data.
Diag1_
Commonwealth
Person_
Identifier Departure_Time Arrival_Time
Mode_Of_
Separation
Ed_Visit
_Type
Triage_Ca
tegory Diagnosis_Codeset
Time
Spent
N390 3739670 7/1/2012 4:05:00 7/1/2012 0:01:00 4 1 3 ICD10AM 4.066
J050 3937880 7/1/2012 2:02:00 7/1/2012 0:17:00 4 1 2 ICD10AM 1.750
R55 4222 7/1/2012 2:32:00 7/1/2012 0:18:00 1 1 3 ICD10AM 2.233
2642712 7/1/2012 2:00:00 7/1/2012 0:21:00 6 1 4 1.650
2359474 7/1/2012 5:45:00 7/1/2012 0:30:00 6 1 4 5.250
A084 3877880 7/1/2012 7:05:00 7/1/2012 0:40:00 4 1 4 ICD10AM 6.417
2764768 7/1/2012 1:11:00 7/1/2012 0:42:00 6 1 4 0.483
3811904 7/1/2012 3:18:00 7/1/2012 0:49:00 6 1 4 2.483
S008 1207288 7/1/2012 1:07:00 7/1/2012 0:54:00 4 1 4 ICD10AM 0.217
2966358 7/1/2012 1:49:00 7/1/2012 1:08:00 6 1 4 0.683
R600 3500564 7/1/2012 7:02:00 7/1/2012 1:18:00 4 1 4 ICD10AM 5.733
2581558 7/1/2012 2:40:00 7/1/2012 1:21:00 6 1 4 1.317
2784768 7/1/2012 2:52:00 7/1/2012 1:25:00 6 1 4 1.450
2974366 7/1/2012 3:04:00 7/1/2012 1:34:00 6 1 4 1.500
J050 2579970 7/1/2012 4:06:00 7/1/2012 1:36:00 4 1 3 ICD10AM 2.500
1825292 7/1/2012 4:47:00 7/1/2012 2:02:00 6 1 4 2.750
J069 3957880 7/1/2012 7:33:00 7/1/2012 2:04:00 4 1 4 ICD10AM 5.483

Analysis of emergency department dataset 6
Z001 2220324 7/1/2012 7:36:00 7/1/2012 2:35:00 4 1 4 ICD10AM 5.017
R681 2704768 7/1/2012 6:19:00 7/1/2012 3:05:00 4 1 3 ICD10AM 3.233
J050 2240324 7/1/2012 6:22:00 7/1/2012 3:16:00 4 1 4 ICD10AM 3.100
1263450 7/1/2012 4:46:00 7/1/2012 4:18:00 6 1 4 0.467
J219 3581364 7/1/2012 8:17:00 7/1/2012 4:20:00 1 1 3 ICD10AM 3.950
J050 366328 7/1/2012 7:39:00 7/1/2012 4:29:00 4 1 4 ICD10AM 3.167
3189346 7/1/2012 5:12:00 7/1/2012 4:50:00 6 1 4 0.367
Exploring data.
The data above is analysed to give the overview of the emergency department activities
the analysis is carried out sing the analysis tool called Microsoft office excel this will be able to
give various measures of central tendency and display the respective graphs for easy visual
display and interpretations.
The data analysed will give the following measures.
i. Average patient age-16.4 years.
ii. The oldest patient-25 years
iii. Patients registration and waiting period-1.4 hours.
iv. Patients average clinical care time- 3.6 hours.
v. Total time spent by patients-18 hours.
vi. Most diagnosed diseases- Malaria.
Exploring the data.
The corrected data is required to be analysed and below is the result of the analysis
carried out in the above data which give the central tendencies in summarised form and the
graphical representations as below:
Diag1_
Commonwealth
Person_
Identifier Departure_Time Arrival_Time
Mode_Of_
Separation
Ed_Visit
_Type
Triage_Ca
tegory Diagnosis_Codeset
Time
Spent
N390 3739670 7/1/2012 4:05:00 7/1/2012 0:01:00 4 1 3 ICD10AM 4.066
J050 3937880 7/1/2012 2:02:00 7/1/2012 0:17:00 4 1 2 ICD10AM 1.750
R55 4222 7/1/2012 2:32:00 7/1/2012 0:18:00 1 1 3 ICD10AM 2.233
2642712 7/1/2012 2:00:00 7/1/2012 0:21:00 6 1 4 1.650
2359474 7/1/2012 5:45:00 7/1/2012 0:30:00 6 1 4 5.250
A084 3877880 7/1/2012 7:05:00 7/1/2012 0:40:00 4 1 4 ICD10AM 6.417
2764768 7/1/2012 1:11:00 7/1/2012 0:42:00 6 1 4 0.483
3811904 7/1/2012 3:18:00 7/1/2012 0:49:00 6 1 4 2.483
S008 1207288 7/1/2012 1:07:00 7/1/2012 0:54:00 4 1 4 ICD10AM 0.217
2966358 7/1/2012 1:49:00 7/1/2012 1:08:00 6 1 4 0.683

Analysis of emergency department dataset 7
R600 3500564 7/1/2012 7:02:00 7/1/2012 1:18:00 4 1 4 ICD10AM 5.733
2581558 7/1/2012 2:40:00 7/1/2012 1:21:00 6 1 4 1.317
2784768 7/1/2012 2:52:00 7/1/2012 1:25:00 6 1 4 1.450
2974366 7/1/2012 3:04:00 7/1/2012 1:34:00 6 1 4 1.500
J050 2579970 7/1/2012 4:06:00 7/1/2012 1:36:00 4 1 3 ICD10AM 2.500
1825292 7/1/2012 4:47:00 7/1/2012 2:02:00 6 1 4 2.750
J069 3957880 7/1/2012 7:33:00 7/1/2012 2:04:00 4 1 4 ICD10AM 5.483
Z001 2220324 7/1/2012 7:36:00 7/1/2012 2:35:00 4 1 4 ICD10AM 5.017
R681 2704768 7/1/2012 6:19:00 7/1/2012 3:05:00 4 1 3 ICD10AM 3.233
J050 2240324 7/1/2012 6:22:00 7/1/2012 3:16:00 4 1 4 ICD10AM 3.100
1263450 7/1/2012 4:46:00 7/1/2012 4:18:00 6 1 4 0.467
J219 3581364 7/1/2012 8:17:00 7/1/2012 4:20:00 1 1 3 ICD10AM 3.950
J050 366328 7/1/2012 7:39:00 7/1/2012 4:29:00 4 1 4 ICD10AM 3.167
3189346 7/1/2012 5:12:00 7/1/2012 4:50:00 6 1 4 0.367
Verifying the data qualities.
The above data set is the accurate and no of the data that is beyond the data set or it is an outlier
however the data is having the appropriate data integrity constraints.
Analysis Plan
Data analysis plan.
The data collected from the Emergency department is going to be used in the various
decisions makings and suggestions to improve the performance.

Analysis of emergency department dataset 8
The analysis plan will have the type of statistical operations to be carried out and their
justifications together with the possible assumptions made.
However the following are the statistical operations done :
i. Mean –This is the average of the age of the patients,waiting time, clinic time
spent and the departure time.
ii. Maximum-This is the maximum of the age of the patients, patient’s waiting time,
patients’ clinic time spent and patients’ the departure time.
iii. Minimum- This is the minimum of the age of the patients, patient’s waiting time,
patients’ clinic time spent and patients’ the departure time.
iv. Totals- This is the total of the age of the patients, patient’s waiting time, patients’
clinic time spent and patients’ the departure time
v. Modes-The mode was used to show the most treated type of illness or condition in
the emergency department.
However the only few assumptions made during the data analysis was:
i. The patient’s age was not beyond 25 years.
ii. The patient’s record was captured once.
iii. There was only one doctor in the emergency department.
iv. The diagnosis equipment that was used was only one.
v. All data collected was pure and accurate.
Individual
Bibliography
Below are some of the bibliography used:
Grace,.B.,(2006). Sturctural Equation Modeling and Natural Systems. Cambridge University
Press,volume.1
Bollen, K., (2014). Interpreting the results from multiple regression and structural equation
models. Bulletin of the Ecological Society of America ,volume. 1.
Data construction

Analysis of emergency department dataset 9
The emergency department is serving the patients as their clients who are the
patients ,however the data dictionary below will be used for the purpose of data constructions
where all the attributes are clearly indicated as follows:
Diag1_
Commonwealth
Person_
Identifier Departure_Time Arrival_Time
Mode_Of_
Separation
Ed_Visit
_Type
Triage_Ca
tegory Diagnosis_Codeset
Time
Spent
N390 3739670 7/1/2012 4:05:00 7/1/2012 0:01:00 4 1 3 ICD10AM 4.066
J050 3937880 7/1/2012 2:02:00 7/1/2012 0:17:00 4 1 2 ICD10AM 1.750
R55 4222 7/1/2012 2:32:00 7/1/2012 0:18:00 1 1 3 ICD10AM 2.233
2642712 7/1/2012 2:00:00 7/1/2012 0:21:00 6 1 4 1.650
2359474 7/1/2012 5:45:00 7/1/2012 0:30:00 6 1 4 5.250
A084 3877880 7/1/2012 7:05:00 7/1/2012 0:40:00 4 1 4 ICD10AM 6.417
2764768 7/1/2012 1:11:00 7/1/2012 0:42:00 6 1 4 0.483
3811904 7/1/2012 3:18:00 7/1/2012 0:49:00 6 1 4 2.483
S008 1207288 7/1/2012 1:07:00 7/1/2012 0:54:00 4 1 4 ICD10AM 0.217
2966358 7/1/2012 1:49:00 7/1/2012 1:08:00 6 1 4 0.683
R600 3500564 7/1/2012 7:02:00 7/1/2012 1:18:00 4 1 4 ICD10AM 5.733
2581558 7/1/2012 2:40:00 7/1/2012 1:21:00 6 1 4 1.317
2784768 7/1/2012 2:52:00 7/1/2012 1:25:00 6 1 4 1.450
2974366 7/1/2012 3:04:00 7/1/2012 1:34:00 6 1 4 1.500
J050 2579970 7/1/2012 4:06:00 7/1/2012 1:36:00 4 1 3 ICD10AM 2.500
1825292 7/1/2012 4:47:00 7/1/2012 2:02:00 6 1 4 2.750
J069 3957880 7/1/2012 7:33:00 7/1/2012 2:04:00 4 1 4 ICD10AM 5.483
Z001 2220324 7/1/2012 7:36:00 7/1/2012 2:35:00 4 1 4 ICD10AM 5.017
R681 2704768 7/1/2012 6:19:00 7/1/2012 3:05:00 4 1 3 ICD10AM 3.233
J050 2240324 7/1/2012 6:22:00 7/1/2012 3:16:00 4 1 4 ICD10AM 3.100
1263450 7/1/2012 4:46:00 7/1/2012 4:18:00 6 1 4 0.467
J219 3581364 7/1/2012 8:17:00 7/1/2012 4:20:00 1 1 3 ICD10AM 3.950
J050 366328 7/1/2012 7:39:00 7/1/2012 4:29:00 4 1 4 ICD10AM 3.167
3189346 7/1/2012 5:12:00 7/1/2012 4:50:00 6 1 4 0.367
Result of exploring the data
The data exploration was done which entailed various steps and procedures each has its
specific purpose and use as below:
i. Cleaning of data-This was done in order to eliminate any data that is out of the range
and also to enter any missing record.
ii. Removal of noise-This is where wrongly entered values like letters are eliminated and
the correct ones are then entered accordingly.
iii. To calculate various central tendencies which includes:
a. Mean-This is the calculations of the averages in the age and the respective waiting
period for the various patients.

Analysis of emergency department dataset 10
b. Mode-This is used to identify the most occurrence emergency incidences that are
handled by the emergency department.
c. Maximum-This is used to identify the maximum values of all variables used in the
data dictionary.
d. Minimum- This is used to identify the maximum values of all variables used in the
data dictionary.
The below are the steps that are used to perform the exploration of the data for the emergency
department as follows :
Variable Identification
The data set has a set of variables which will be used to do analysis of the processes in the
emergency department and the respective time.
Below are the variables to be used:
Diag1_
Commonwealth
Person_
Identifier Departure_Time Arrival_Time
Mode_Of_
Separation
Ed_Visit
_Type
Triage_Ca
tegory Diagnosis_Codeset
Time
Spent
N390 3739670 7/1/2012 4:05:00 7/1/2012 0:01:00 4 1 3 ICD10AM 4.066
J050 3937880 7/1/2012 2:02:00 7/1/2012 0:17:00 4 1 2 ICD10AM 1.750
R55 4222 7/1/2012 2:32:00 7/1/2012 0:18:00 1 1 3 ICD10AM 2.233
2642712 7/1/2012 2:00:00 7/1/2012 0:21:00 6 1 4 1.650
2359474 7/1/2012 5:45:00 7/1/2012 0:30:00 6 1 4 5.250
A084 3877880 7/1/2012 7:05:00 7/1/2012 0:40:00 4 1 4 ICD10AM 6.417
2764768 7/1/2012 1:11:00 7/1/2012 0:42:00 6 1 4 0.483
3811904 7/1/2012 3:18:00 7/1/2012 0:49:00 6 1 4 2.483
S008 1207288 7/1/2012 1:07:00 7/1/2012 0:54:00 4 1 4 ICD10AM 0.217
2966358 7/1/2012 1:49:00 7/1/2012 1:08:00 6 1 4 0.683
R600 3500564 7/1/2012 7:02:00 7/1/2012 1:18:00 4 1 4 ICD10AM 5.733
2581558 7/1/2012 2:40:00 7/1/2012 1:21:00 6 1 4 1.317
2784768 7/1/2012 2:52:00 7/1/2012 1:25:00 6 1 4 1.450
2974366 7/1/2012 3:04:00 7/1/2012 1:34:00 6 1 4 1.500
J050 2579970 7/1/2012 4:06:00 7/1/2012 1:36:00 4 1 3 ICD10AM 2.500
1825292 7/1/2012 4:47:00 7/1/2012 2:02:00 6 1 4 2.750
J069 3957880 7/1/2012 7:33:00 7/1/2012 2:04:00 4 1 4 ICD10AM 5.483
Z001 2220324 7/1/2012 7:36:00 7/1/2012 2:35:00 4 1 4 ICD10AM 5.017
R681 2704768 7/1/2012 6:19:00 7/1/2012 3:05:00 4 1 3 ICD10AM 3.233
J050 2240324 7/1/2012 6:22:00 7/1/2012 3:16:00 4 1 4 ICD10AM 3.100
1263450 7/1/2012 4:46:00 7/1/2012 4:18:00 6 1 4 0.467
J219 3581364 7/1/2012 8:17:00 7/1/2012 4:20:00 1 1 3 ICD10AM 3.950
J050 366328 7/1/2012 7:39:00 7/1/2012 4:29:00 4 1 4 ICD10AM 3.167
3189346 7/1/2012 5:12:00 7/1/2012 4:50:00 6 1 4 0.367

Analysis of emergency department dataset 11
Type of variables Data types Variable categories
Prediactor variable
Diag1_Commonwealth
Person_Identifier
Departure_Time
Arrival_Time
Mode_Of_Separation
Ed_Visit_Type
Triage_Category
Diagnosis_Codeset
Target variable
Time Spent
Character
Diag1_Commonwealth
Diagnosis_Codeset
DateTime
Departure_Time
Arrival_Time
Numerical
Person_Identifier
Mode_Of_Separation
Ed_Visit_Type
Triage_Category
Time Spent
Categorical
Diag1_Commonwealth
Diagnosis_Codeset
Person_Identifier
Mode_Of_Separation
Ed_Visit_Type
Triage_Category
Continuous
Departure_Time
Arrival_Time
Time Spent
Univariate Analysis
In this stage the variables are catered for individually depending on the continuous or
categorical group for the used continuous variables we shall check on the mean, modes, max
and minimum for the categorical variable and histogram plot for the continuous variables
Continuous variables

Analysis of emergency department dataset 12
0.000
1.000
2.000
3.000
4.000
5.000
6.000
7.000
1 3 5 7 9 11 13 15 17 19 21 23
Time spent
Time spent
Categorical variables.
Bi-variate Analysis
To perform the bi-variant analysis the continuous and the categorical variable are
analyzed separate this by use of the scatter plots and the graphs respectively.
Continuous variables

Analysis of emergency department dataset 13
Categorical variables.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time spent 4 1 2 1 5 6 0 2 0 0 5 1 1 1 2 2 5 5 3 3 0 3 3 0
0.000
1.000
2.000
3.000
4.000
5.000
6.000
7.000
Axis Title
Time spent
Missing values treatment
In the dataset provided there are some variable which had some missing values in the
diag1_commonwealth and diagnosis_codeset however for efficient analysis the missing values in
diagnosis_codeset and diag1_commonwealth are all deleted in order to eliminate any deviations.
Therefore the following is the resulting datasets after elimination of the records with missing
values.
Diag1_
Commonwealth
Person_
Identifier Departure_Time Arrival_Time
Mode_Of_
Separation
Ed_Visit
_Type
Triage_Ca
tegory Diagnosis_Codeset
Time
Spent

Analysis of emergency department dataset 14
N390 3739670 7/1/2012 4:05:00 7/1/2012 0:01:00 4 1 3 ICD10AM 4.066
J050 3937880 7/1/2012 2:02:00 7/1/2012 0:17:00 4 1 2 ICD10AM 1.750
R55 4222 7/1/2012 2:32:00 7/1/2012 0:18:00 1 1 3 ICD10AM 2.233
A084 3877880 7/1/2012 7:05:00 7/1/2012 0:40:00 4 1 4 ICD10AM 6.417
S008 1207288 7/1/2012 1:07:00 7/1/2012 0:54:00 4 1 4 ICD10AM 0.217
R600 3500564 7/1/2012 7:02:00 7/1/2012 1:18:00 4 1 4 ICD10AM 5.733
J050 2579970 7/1/2012 4:06:00 7/1/2012 1:36:00 4 1 3 ICD10AM 2.500
J069 3957880 7/1/2012 7:33:00 7/1/2012 2:04:00 4 1 4 ICD10AM 5.483
Z001 2220324 7/1/2012 7:36:00 7/1/2012 2:35:00 4 1 4 ICD10AM 5.017
R681 2704768 7/1/2012 6:19:00 7/1/2012 3:05:00 4 1 3 ICD10AM 3.233
J050 2240324 7/1/2012 6:22:00 7/1/2012 3:16:00 4 1 4 ICD10AM 3.100
J219 3581364 7/1/2012 8:17:00 7/1/2012 4:20:00 1 1 3 ICD10AM 3.950
J050 366328 7/1/2012 7:39:00 7/1/2012 4:29:00 4 1 4 ICD10AM 3.167
Outlier treatment
The data set selected is fine and there is no any abnormal values which are beyond the
limit of the values required this is shown by the help of the below scatter diagram.
Variable transformation.
This is the process a variable is replaced with a formula or function ,however this
function is used to make new values from the original values ,in this case the only
transformation is in the Departure_Time and Arrival_Time to form the Time_Spent during
the patients treatment processes as shown below.

Analysis of emergency department dataset 15
Diag1_
Commonwealth
Person_
Identifier Departure_Time Arrival_Time
Mode_Of_
Separation
Ed_Visit
_Type
Triage_Ca
tegory Diagnosis_Codeset
Time
Spent
N390 3739670 7/1/2012 4:05:00 7/1/2012 0:01:00 4 1 3 ICD10AM 4.066
J050 3937880 7/1/2012 2:02:00 7/1/2012 0:17:00 4 1 2 ICD10AM 1.750
R55 4222 7/1/2012 2:32:00 7/1/2012 0:18:00 1 1 3 ICD10AM 2.233
A084 3877880 7/1/2012 7:05:00 7/1/2012 0:40:00 4 1 4 ICD10AM 6.417
S008 1207288 7/1/2012 1:07:00 7/1/2012 0:54:00 4 1 4 ICD10AM 0.217
R600 3500564 7/1/2012 7:02:00 7/1/2012 1:18:00 4 1 4 ICD10AM 5.733
J050 2579970 7/1/2012 4:06:00 7/1/2012 1:36:00 4 1 3 ICD10AM 2.500
J069 3957880 7/1/2012 7:33:00 7/1/2012 2:04:00 4 1 4 ICD10AM 5.483
Z001 2220324 7/1/2012 7:36:00 7/1/2012 2:35:00 4 1 4 ICD10AM 5.017
R681 2704768 7/1/2012 6:19:00 7/1/2012 3:05:00 4 1 3 ICD10AM 3.233
J050 2240324 7/1/2012 6:22:00 7/1/2012 3:16:00 4 1 4 ICD10AM 3.100
J219 3581364 7/1/2012 8:17:00 7/1/2012 4:20:00 1 1 3 ICD10AM 3.950
J050 366328 7/1/2012 7:39:00 7/1/2012 4:29:00 4 1 4 ICD10AM 3.167
Variable creation.
For this dataset there is new set of variable that will be created apart from the variable
time spent variable that results from the Departure_Time and Arrival_Time respectively as
shown below.
Diag1_
Commonwealth
Person_
Identifier Departure_Time Arrival_Time
Mode_Of_
Separation
Ed_Visit
_Type
Triage_Ca
tegory Diagnosis_Codeset
Time
Spent
N390 3739670 7/1/2012 4:05:00 7/1/2012 0:01:00 4 1 3 ICD10AM 4.066
J050 3937880 7/1/2012 2:02:00 7/1/2012 0:17:00 4 1 2 ICD10AM 1.750
R55 4222 7/1/2012 2:32:00 7/1/2012 0:18:00 1 1 3 ICD10AM 2.233
A084 3877880 7/1/2012 7:05:00 7/1/2012 0:40:00 4 1 4 ICD10AM 6.417
S008 1207288 7/1/2012 1:07:00 7/1/2012 0:54:00 4 1 4 ICD10AM 0.217
R600 3500564 7/1/2012 7:02:00 7/1/2012 1:18:00 4 1 4 ICD10AM 5.733
J050 2579970 7/1/2012 4:06:00 7/1/2012 1:36:00 4 1 3 ICD10AM 2.500
J069 3957880 7/1/2012 7:33:00 7/1/2012 2:04:00 4 1 4 ICD10AM 5.483
Z001 2220324 7/1/2012 7:36:00 7/1/2012 2:35:00 4 1 4 ICD10AM 5.017
R681 2704768 7/1/2012 6:19:00 7/1/2012 3:05:00 4 1 3 ICD10AM 3.233
J050 2240324 7/1/2012 6:22:00 7/1/2012 3:16:00 4 1 4 ICD10AM 3.100
J219 3581364 7/1/2012 8:17:00 7/1/2012 4:20:00 1 1 3 ICD10AM 3.950
J050 366328 7/1/2012 7:39:00 7/1/2012 4:29:00 4 1 4 ICD10AM 3.167
Data Quality Analysis
The data corrected however is fully accurate where there are no errors ,since the data
collected is fully complete without any missing value, the data integrity is also observed where
each variable is handling each of the respective data types, and the data is clean without noise or
the outliers at all ,thus the data is of the recommended type.
Assignment 2
Group:

Analysis of emergency department dataset 16
Final report
Results of analysis
The data analysis from the various individuals about the emergency department was
conducted and thus there were various findings that was obtained regarding the processes in the
emergency department, however the following were some of the findings obtained.
i. The emergency department mostly handles the incidents from the people of age between
25 years and below according to the sample data corrected.
ii. The most incident or disease taken care of here is the malaria which is the highest ,
accidents and diabetes .
iii. The average time spent for the patients to be treated by going through all the processes is
3.6 hours .
iv. According to the results obtained the number of male who are taken to emergency
department is very high compared to the number of female who visits the emergency
department.
v. The emergency department had an average of five patients in any given hour who are
seeking the treatment.
Interpretation of findings
According to the findings from the analysis of the emergency department, it means that
the departments have a huge number of patients who seek the service from the department in a
given time.
However the patients end up crowding for the service due to the lack of enough work
force, limited number of machines and lack of computerised data record system hence taking
more time to store and retrieve records (Otsuki,2014).
Recommendations.
According to the analysis of the data obtained from the analysis of the emergency department
there were various recommendations made in order to improve the level of performance which
includes the following.

Analysis of emergency department dataset 17
i. The department should put in place computerised data recording system to ensure there is
ease of data storage and retrieval.
ii. The department should increase the number of staffs who can offer services to the
patients who visits their department.
iii. The department should also increase the number of machines and equipments to be used
for diagnosis of their patients.
Individual
Analysis
The analysis of the collected data in regard to the emergency department has to be done
systematically, however the analysis plan entails the following operations.
The analysis plan will have the type of statistical operations to be carried out and their
justifications together with the possible assumptions made.
However the following are the statistical operations done:
i. Mean –This is the average of the age of the patients, waiting time, clinic time
spent and the departure time.
ii. Maximum-This is the maximum of the age of the patients, patient’s waiting time,
patients’ clinic time spent and patients’ the departure time.
iii. Minimum- This is the minimum of the age of the patients, patient’s waiting time,
patients’ clinic time spent and patients’ the departure time.
iv. Totals- This is the total of the age of the patients, patient’s waiting time, patients’
clinic time spent and patients’ the departure time
v. Modes-The mode was used to show the most treated type of illness or condition in
the emergency department.
Result of analysis
After performing the analysis of the collected data there was clear indication that the
emergency department performance was quite slow due to the delay of services (Daniel , 2013).
However the observed tread was that the most of the diseases that were being treated were
the malaria, also there were incidences like diabetes among the others.

Analysis of emergency department dataset 18
The below was just a simple graphical illustration of how the various patients were attended
to in the various processes.
Alice Paul peter
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Patients teratments processes
Axis Title
Interpretation of your findings
After the analysis the findings from the analysis of the emergency department, it means that
the departments has a huge number of patients who seek the service from the department in a
given time (Jare,2015).
However the patients end up crowding for the service due to the lack of enough work
force, limited number of machines and lack of computerised data record system hence taking
more time to store and retrieve records.
Therefore according to my opinion the performance of the emergency department can be
improved by ensuring that the labour force is raised, better record keeping method is adopted
and more modernised equipments are installed.

Analysis of emergency department dataset 19
Recommendations
According to the analysis of the data obtained from the analysis of the emergency department
there were various recommendations made in order to improve the level of performance which
includes the following.
i. The department should put in place computerised data recording system to ensure there is
ease of data storage and retrieval.
ii. The department should increase the number of staffs who can offer services to the
patients who visits their department.
iii. The department should also increase the number of machines and equipments to be used
for diagnosis of their patients (Alan, 2014).
.

Analysis of emergency department dataset 20
References
Alan,A.(2014). Statistics for Big Data for Dummies, U.S.A: For Dummies publishers.
Jare, D.(2015). Big Data, Data Mining and Machine Learning,INDIA: Wiley publishers
Daniel ,C.(2013).Analytics: Data Science, Data Analysis and Predictive Analytics for Business,
INDIA: CreateSpace Independent Publishing Platform
Otsuki,A.(2014).Big Data Analysis,USA: LAP LAMBERT Academic Publishing