Healthcare Data Analysis: Antenatal Admissions at UTS Hospital (92917)

This report analyzes healthcare data from UTS Hospital, focusing on AR-DRGs O66A/B 'Other Antenatal Admissions (with or without complicating diagnoses)'. The analysis utilizes pivot tables in MS Excel to extract significant insights from the raw data, which was collected through observation and interviews. The report identifies key DRG descriptions, including neonate DRG, vaginal delivery, and chest pain, along with common AMO specialties like obstetrics and gynecology. The data reveals a higher prevalence of health complications among female patients and variations in the length of stay (LOS) for different conditions. The report aims to provide data-driven recommendations implementable by UTS Hospital, offering valuable insights into patient demographics, prevalent health issues, and resource utilization, contributing to improved healthcare decision-making. Desklib provides a platform for students to access similar solved assignments and past papers.

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Using Health Care Data
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Executive summary
Health data is of great importance in healthcare organizations as it helps the organizations
to know the health status of their patients which makes it easy for them to offer treatments to the
appropriate people when needed. Health organizations need to have the health data of not only
the patients but also for the healthy people since they may need to offer some medications such
as vaccinations to the healthy people and having the data of the healthy people prior to giving the
medications will help them to know the best medications to give. With the health data, the
healthcare organizations need to store the data well to avoid leakage to the unauthorized people
since health data is very crucial and sensitive and should not be disclosed to the unauthorized by
any means. The healthcare organizations analyze their data regularly to understand the data well
and these help the organizations to understand the health status of the people especially their
patients’ well and offer the most appropriate care and treatments to the patients according to their
health demands. In this paper, we shall analyze the health data of UTS hospital to understand the
health status of the patients well and make some meaningful inferences from the analysis.

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Using Health Care Data
Introduction
Hospitals and the other healthcare organizations handle huge volumes of healthcare data.
These healthcare organizations must always be very keen when handling and storing healthcare
data since healthcare data is very crucial and sensitive and need to be handled well as required
and by the right personnel to avoid loss, leakage to the unauthorized people, among many other
problems which may arise if the data is not handled and stored properly (Yue et al., 2016, p.218)
The healthcare organizations greatly need the healthcare data for their patients as they use the
data to keep track of the health conditions of their patients and know the right treatments and
medications to offer to help the patients to recover from the illnesses (Wager, Lee, and Glaser,
2017). The healthcare data of the healthy people is also required by the healthcare organizations
as the healthcare organizations might need to offer some public medical services such as public
vaccination or immunization and with the healthcare data of all the people (both the patients and
the healthy people), the healthcare organizations will be in a better position to know which
treatments and medications to offer to which people which will not overreact with their bodies
(Hernandez, 2017, pp.186-194). We have some bodies which are very sensitive to some
medications and these medications may cause allergy or other health problems to the people and
therefore, the health practitioners must always be very careful when offering treatments to
different people (O'Brien, 2016). Healthcare organizations do a detailed analysis of the
healthcare data they collect from their patients and the other healthy people to have a deep
understanding of the data and for them to know the best ways to use the data which will be of

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great benefits to the patients. The main aims/objectives of this paper are to discuss healthcare
data in details and study its use in healthcare organizations and see some of the ways followed by
the healthcare organizations to analyze the healthcare data for them to understand the data in a
better approach. This discussion will help us to understand how effective and efficient the
current methods of health data collection and data analysis in hospitals are in the modern field of
healthcare. The paper will also discuss the main antenatal health problems and some
interventions which can be implemented to address the problems. This whole discussion will be
very helpful in answering our main research questions which are:
Are the current methods of data collection, analysis, and usage employed by the modern
healthcare organizations effective and have they helped to improve the overall efficiency or
performance of the medical field?
Are antenatal or neonate health problems common problems in hospitals?
What types of interventions should be taken to address these health problems?
Background/Literature review
As already mentioned, healthcare data of the patients and the healthy people is of great
importance in healthcare organizations. The healthcare organizations are ever collecting the
healthcare data and storing the data as required for their own benefits and for the benefits of the
patients and the healthy people whose data is collected. Having the health data of the patients

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and the healthy people helps the healthcare organizations to understand these people and thus
offer the best and the required medical services to each of the people. There are many methods
which have been invented to help in collecting and storing or recording of the healthcare data of
the patients and all these methods help to improve the quality of the medical services offered by
the healthcare organizations. One of the most common modern methods employed in the storage
of healthcare data is the electronic health record (EHR) or electronic medical records (EMR)
method. Electronic health record (EHR) is an electronic method which used in storage of the
patients’ medical history which is maintained by the providers over some time. The health data
of the patients stored using EHR may include demographics, the patients’ progress, the medical
problems experienced by the patients over time, the medications offered to the patients, the vital
signs shown by the patients, the radiology reports of the patients, among other data (Rumball-
Smith, Shekelle, and Damberg, 2018, pp.26-31).
In Australia and some other overseas countries, we have some well-established AR-
DRGs (Australian related diagnosis groups) and these AR-ARGs are normally associated with
some diagnoses and some medications. The AR-DRG is an Australian based classification
system which ensures there is a provision of a clinically meaningful way which relates the
number and the type of patients treated in hospitals to the medical resources which are required
by the hospitals (Jackson et al., 2015, pp.1433-1441). Each AR-ARG represents a particular
class of patients who have similar clinical conditions and hence require similar medical services.
Over the last few years, the prevalence and the incidences of antenatal admissions to
acute hospitals have been rising at an alarming rate. This rise results from an increase in the

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number of health problems and complications which affect the pregnant mothers. Some of the
major health complications which have become very common to pregnant mothers include non-
malignant breast conditions, vaginal complications especially during delivery, miscellaneous
metabolic disorders, complications of the digestive systems, among many other health
complications which have become very common in the modern world.
Different hospitals including UTS hospital have come up with some well-defined medical
approaches and procedures to help these pregnant mothers who experience the antenatal health
complications in order to improve their health and reduce the many cases of deaths resulting
from antenatal complications (Mbuagbaw et al., 2015). Some of these approaches include
establishing special sections (special medical rooms) for the pregnant mothers where they can
get special attentions and the required medical services from the available medical experts,
giving the first priority and the best medical services to the pregnant mothers who are always
very prone to more serious health complications which can even lead to deaths if not attended to
as fast as required, advising the pregnant mothers about their health status and some of the
healthy practices such as the diets they should take and some activities which they should avoid
for the sake of their health which is normally at great risk during pregnancies, among other
relevant approaches (Downe et al., 2016, pp.529-539).
Methods

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From the given raw data of the UTS hospital, we shall use pivot tables in Ms. Excel to do
our analysis. Pivot tables are strong data analysis tools used in Ms. Excel which make it easy to
extract the significance of large or bulky data, and this is very important in understanding the
data as required (Visser et al., 2018). The methods which were used to collect the given data
were the observation method and the interview methods (Hanvey, 2018, pp.19-46). The
researchers closely observed the patients and the symptoms they portrayed and asked them some
questions (interviewed) about their health conditions to understand them well. The hospital also
provided some health data of the patients which it had saved for its medical use. Having obtained
all the required data and using the pivot tables to get the significance of the data, we shall draw
tables in Ms. Word and some bar graphs to help in interpreting and understanding the data well.
Results and data analysis
From the data, we can see that at the time of data collection, UTS hospital had 34,624
patients who had different health problems. From the pivot tables, we can see that we have
35,086 different DRG (diagnostic related groups) descriptions where we can see that among the
major DRG descriptions with the patients are neonate DRG description with 2615 patients,
vaginal delivery W/O complicating diagnosis with 1806 patients, colonoscopy with 650 patients,
chest pain descriptions with 730 patients, other antenatal admission moderate or no complicating
diagnosis with 632 patients, oesophagitis, gastroent & misc digestive system disorders
descriptions with 560 patients, among others. A table showing the main DRG descriptions and
the number of patients is shown below:
DRG description Number of patients Percentages compared

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to total
Neonate DRG description 2615 7.45%
Vaginal delivery W/O complicating
diagnosis
1806 5.15%
Chest pain descriptions 730 2.08%
Colonoscopy 650 1.85%
Antenatal admission moderate or no
complicating diagnosis
632 1.80%
Oesophagitis, gastroent & misc
digestive system disorders
descriptions
560 1.60%
Total 6,993
From the data of the main DRG descriptions, we can draw a bar graph shown below to
compare the DRG descriptions and the number of patients with the descriptions.

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Neonate DRG
description Vaginal delivery
W/O
complicating
diagnosis
Chest pain
descriptions Colonoscopy Antenatal
admission
moderate or no
complicating
diagnosis
Oesophagitis,
gastroent &
misc digestive
system
disorders
descriptions
0
500
1000
1500
2000
2500
3000
2615
1806
730 650 632 560
A bar graph showing the number of patients given different DRG
descriptions in UTS hospital
DRG description
Number of patients
From the collected data and the pie chart shown above, we can see that the three main
DRG descriptions which had the highest number of patients were neonate DRG description
which had 2615 patients, vaginal delivery W/O complicating diagnosis with1806 patients, and
chest pain description with 730 patients.
From the pivot tables, we can also deduce that among the most common AMO specialty
cases are obstetrics which is most commonly used to address most of the female health
complications especially those related to vaginal and uterine problems (reproductive system
health problems), gynaecology also commonly used in addressing most of the health
complications which affect the reproductive systems of females, general medicine used to
address most of the general health problems experienced by both males and females, plastic
surgery used to address the health complications experienced by both males and females, among
other AMO specialties. From the data of the UTS hospitals, we can also see the number of

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females suffering from different health complications (the female patients) is by far higher than
the number of the male patients which clearly shows that females are always at higher health
risks as compared to their male counterparts (Torre et al., 2016, pp.182-2002).
From the data given by the pivot tables, we can also tell that the patients had different LOS
(length of stay) in the hospital. We can see that among the patients who spent the longest time in
hospitals were the patients with vaginal delivery W/O complicating diagnosis who spent 5008
hours in the hospital, the patients with neonate, AdmWt > 2499 g W/O significant O.R.
procedure W/O problems who spent 7446 hours in the hospital, the patients with rehabilitation
W catastrophic or severe CC who spent 6770 hours in the hospital, the patients with
Schizophrenia disorders W mental health legal status who spent 4843 hours in the hospital, the
patients with tracheostomy any age, any condition who spent 3582 hours in hospital, among
others.
The data of the DRG description and LOS can be tabulated in the table below:
DRG description LOS (hours)
The patients with vaginal delivery W/O
complicating diagnosis
5008
The patients with neonate, AdmWt > 2499 g
W/O significant O.R. procedure W/O
problems
7446
The patients with rehabilitation W
catastrophic or severe CC
6770
The patients with Schizophrenia disorders W
mental health legal status
4843
The patients with tracheostomy any age, any 3582

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condition
Total 27,649
From the above data, a bar graph can be drawn as shown below to show the DRG
descriptions and the LOS of the patients.
The patients with
vaginal delivery
W/O complicating
diagnosis
The patients with
neonate, AdmWt >
2499 g W/O
significant O.R.
procedure W/O
problems
The patients with
rehabilitation W
catastrophic or
severe CC
The patients with
Schizophrenia
disorders W mental
health legal status
The patients with
tracheostomy any
age, any condition
0
1000
2000
3000
4000
5000
6000
7000
8000
5008
7446
6770
4843
3582
A bar graph showing some DRG descriptions of the patients and the
LOS in hours
DRG description
LOS in hours
From the excel data obtained using filtering and the pivot tables, we can see that the six
main diagnosis of the aboriginal people were the caesarean delivery W/O complicating diagnosis
with 8 patients, vaginal Delivery W multiple complicating diagnosis, at least one severe with 11
patients, other antenatal admission W severe complicating diagnosis with 14 patients, neonate,
AdmWt > 2499 g W/O significant O.R. procedure W/O problem with 16 patients, vaginal
delivery W severe complicating diagnosis with 8 patients, and vaginal delivery W/O

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complicating diagnosis with 8 patients. This information can be represented by in the table and
the bar graph shown below:
A table showing the main DRG descriptions of the aboriginal people:
DRG description Number of aboriginal people
(patients) with the description
Caesarean delivery W/O complicating diagnosis 8
Vaginal Delivery W multiple complicating diagnosis, at
least one severe
11
Other antenatal admission W severe complicating
diagnosis
14
Neonate, AdmWt > 2499 g W/O significant O.R.
procedure W/O problem
16
Vaginal delivery W severe complicating diagnosis 8
Vaginal delivery W/O complicating diagnosis 8