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UTS Hospital Assignment

Summary of Rethinking Assumptions About Delivery of Healthcare

In UTS hospitals, the clinical coders might not always assign the correct diagnostic and procedure codes using the medical notes' information leading to incorrect AR-DRG classification. This has led to the hospital experiencing financial difficulties since the private health insurance funders reimburse the funds depending on the estimated rate for each AR-DRG. UTS hospital is a charitable hospital operated on a no-profit basis, and with a bed capacity of 250, this hospital receives a large number of patients that constrain the limited resources available in this facility (Scheurwegs et al. 2016). This hospital is currently facing financial difficulties due to insufficient fund income; the only source of income is from the private health insurers, which has also reduced their reimbursement rate due to the incorrect assignation of diagnostic and procedure codes by the clinical coders. The reduction of the incoming funds from the private health insurers and the fact that this hospital has no other income source implies that this facility is currently struggling to maintain its normal operational activities. For instance, provision efficient and effective medical services to the patients, pay the hospital bills and pay the professionals who have dedicated their time and heart to serve this facility. This hospital has always recruited skillful and knowledgeable personnel. The hospital aims to provide standard healthcare to the patients, through qualified nurses and clinical officers who even go overboard to take advantage of their work environment to provide the patients who they interact with on a routine basis health education concerning ways of preventing infectious and even non-communicable diseases from reaching their homesteads. The nurses even go ahead to educate patients with chronic illnesses on the importance of following their discharge guidelines, medication systematic plans, follow-ups with doctors’ appointments, consults and referrals, and the necessity for equipment. Despite all these efforts by the hospital and its employees to take this hospital to the next level, to satisfy the patients through the hospital’s efficiency, there are still some obstacles that are a setback to the hospital's progress. Some workers still are still not up to the task at this hospital; for instance, the clinical coders who cannot follow the provided medical notes to assign correct diagnostic and procedure codes for AR-DRG classification and this is one of the major setbacks that have cost this hospital a lot of funds. This study explicitly examines, edit and analyze UTS hospital data using appropriate and standard software and analysis techniques (Karimi et al. 2017). Quantitative analysis techniques was used to come up with a summary using graphical presentations and tabulated presentations to demonstrate an understanding of the application and management of health data. The findings in this summary will help the executive of the hospital in decision making and planning. Regarding this study, it will be shown whether graphical presentation and tabulated presentation of health data will demonstrate an understanding of the data's application and management.

Introduction to Rethinking Assumptions About Delivery of Healthcare

UTS hospital is based in Australia. The hospital decided to analyze data of patients and services offered by the hospital. The data analyzed ranges from ENT to surgeries and all other hospital related services (Khan & Yairi 2018). Also, there is an analysis of data collected in the same hospital field for various countries in the world. The data was collected and recorded respectively. The hospital needs to plan a budget that can ease effective and smooth dispersion of services.

The method used to analyze this hospital services offered data is the AR-DRGv6 version AR-DRG v6 is an Australian Refined Diagnosis Related Group is a patient classification system which provides a clinically meaningful way of relating the number and types of patients treated in a hospital to the resources required by the hospital. The hospital needs to plan a budget that can ease effective and smooth dispersion of services (Vaikuntam 2020).

Hospitals have many services in treatment and require huge investments to operate. A budget for these servicesneeds to plan basing from the patients being treated. The data was classified under patient number, ARDRG-v6 number, ARDRG-v6 description, gender, age, marital status, discharge, service category, ICU hours, separation mode, financial class, mechVent hours, country of birth, indigenous status, readmit code, emergency status, principal diagnosis and other diagnosis.

Background of Rethinking Assumptions About Delivery of Healthcare

Hospitals spend a lot in running a health facility. This expenditure needs to be budgeted for to avoid mishaps along the year. Most budgets are planned annual. This is the budgeted expenditure is to cover the running of the institution for a whole financial year without cause for alarm.UTS hospital serves as no expulsion they also need a budget for their planned services.UTS hospital attends to a lot of patients in a day that either require being admitted or being treated then sent home.as seen from the data UTS hospital hosts ICU wings as well as HDU wings. The direct and overhead costs of running each department of the hospital vary from quantity, productivity as well as staff. On clinical salaries also vary from each type of DRG description. Delicate DRGs require big salaries as they serve as motivation of the staff attending to the patients (Heslop 2019).

Aims and Objectives of Rethinking Assumptions About Delivery of Healthcare

The aim of this AR-DRG v6 analysis is to help the hospital assess the expenditure in running their services. Through data analysis of patients and the treatment services given the hospital is able to identify what illnesses are commonly treated in the hospital.in line to the expenditure planning the hospital also gets to identify the loopholes in their service dispersion and cover them up. All this is in aim of providing better, affordable and sustainable services to the people. Good service dispersion will earn the hospital profits that can be used in development as well as repair and maintenance of the hospital. The data collected was for patient’s visiting the UTS hospital in Australia and for what illnesses were they treated for (Delaney 2018).

Methodology of Rethinking Assumptions About Delivery of Healthcare

Data was collected from the hospital records and analyzed using the AR-RDG model of classification for the (2012-2013). Data was first collected and recorded based on the age, gender and illness treated as seen in the excel file. First the patient ID data was recorded as well as the total number of patients. Secondly the DRG data was recorded. This was the type of treatment each of the 34624 patients recorded in the hospital. With the data recorded then the AR-DRG v6 model was used to develop the cost weights. The cost weights were then used to develop the average costs per DRG. This was the cost incurred in treatment of each disease. From this analysis then we developed the national estimates for cost weights using the AR-DRG method. This was through development of average component cost of each DRG (Das et al .2018).

Results and Findings of Rethinking Assumptions About Delivery of Healthcare

Respondents

Variable

Percentage

Male

14424

42

Female

20200

58

Total

34624

100

The above figures show the graphical representation of this particular question. It was observed that 42 of the respondents were male with 14424 in number amongst 34624 respondents. The female comprise of 58 percent respondents with 20200 in number.

Financial Class

Variable

Percentage

Public Patient - general & Psych

28819

83

Public Patient - Overseas reciprocal

52

0.1

Private - Shared Ward Overnight

1402

4

Private - single room overnight

272

0.7

Motor Vehicle Accident

120

0.3

Veterans Affairs

724

2

Unqulaified Baby of Public Patient

2914

8

Workers Compensation

251

0.72

Medicare Ineligible

64

0.18

Other Compensable

6

0.01

Total

34624

100

From the data analyzed it’s evident to note that: Most of the patients were Australia then a few from the other countries. Most of the patient was from the public patient financial class and others from unqualified baby of public patient financial class.

The above figures show the graphical representation of this particular question. Amongst all the financial classes it was observed that 28819 respondents were Public Patient - general & Psych comprising of 83 percent. The one who choose Public Patient - Overseas reciprocal was 52 in number with comprising of 0.1 percent of the total financial class description. The one who choose Private - Shared Ward Overnight was 1402 in number with comprising of 4 percent of the total financial class description. The one who choose Private - single room overnight was 272 in numbers with comprising of 0.7 percent of the total financial class description. The one who chooses Motor Vehicle Accident was 120 in number with comprising of 0.3 percent of the total financial class description. The one who chooses Veterans Affairs was 724 in number with comprising of 2 percent of the total financial class description. The one who chooses the Unqualified Baby of Public Patient was 2914 in number with comprising 8 percent of the total financial class description. The one who chooses Workers Compensation was 251 in number with comprising of .72 percent of the total financial class description. The one who chooses Medicare Ineligible were 64 in number with comprising of 0.18 percent of the total financial class description. The ones who choose Other Compensable were 6 in number with comprising 0.01 percent of the total financial class description. The total number of respondents was comprising of both male and female and they totaled into 34624.

Drug

Description

A01Z

Liver Transplant

B01A

Ventricular Shunt Revision W Catastrophic or Severe CC

C01Z

Procedures for Penetrating Eye Injury

D01Z

Cochlear Implant

E01A

Major Chest Procedures W Catastrophic CC

F01A

Implantation or Replacement of AICD, Total System W Catastrophic CC

G01A

Rectal Resection W Catastrophic CC

H01A

Pancreas, Liver and Shunt Procedures W Catastrophic CC

I01A

Bilateral/Multiple Major Joint Proc of Lower Extremity W Revision or W Cat CC

J01A

Microvas Tiss Transf for Skin, Subcutaneous Tiss & Breast Disd W Cat/Sev CC

K01A

OR Procedures for Diabetic Complications W Catastrophic CC

L02A

Operative Insertion of Peritoneal Catheter for Dialysis W Cat or Sev CC

M01A

Major Male Pelvic Procedures W Catastrophic or Severe CC

N01Z

Pelvic Evisceration and Radical Vulvectomy

O01A

Caesarean Delivery W Catastrophic CC

P01Z

Neonate, Died or Transferred <5 Days of Admission W Significant OR Procedure

Q01Z

Splenectomy

R01A

Lymphoma and Leukaemia W Major OR Procedures W Catastrophic or Severe CC

S60Z

HIV, Sameday

T01A

OR Procedures for Infectious and Parasitic Diseases W Catastrophic CC

U40Z

Mental Health Treatment, Sameday, W ECT

V60A

Alcohol Intoxication and Withdrawal W CC

W01Z

Ventilation or Cranial Procedures for Multiple Significant Trauma

X02A

Microvascular Tiss Transfer or (Skin Graft W Cat/Sev CC) for Injuries to Hand

Y01Z

Ventilation for Burns and Severe Full Thickness Burns

Z01A

OR Procedures W Diagnoses of Other Contacts W Health Services W Cat/Sev CC

There were different paints in the ICU, others in the HDU while others were minor treatments as well as their emergency status. There were costs incurred from services offered. This was recorded averagely as per the type of DRG. There is an incurred cost in direct and overhead costs incurred. The total number of costs was recorded and the totals calculated at the end of each cost. This data was used to estimate the national data that was used in knowing the number of hospitals established (Groom 2018).

Discussion on Rethinking Assumptions About Delivery of Healthcare

From the data it can be noted that the total direct and overhead costs were 10,965,323 and 8675557.it can be seen that direct costs are more than the overhead costs. From the costs seen in dispersion of services there is need for more investments both to the staff and non-staff as well as the service dispersion models. The hospital needs to look after its doctors and nurses as they are the key integral to a better dispersion of health and medical services to the people (Kaur et al.2019). (Refer to excel for more details)

Conclusion on Rethinking Assumptions About Delivery of Healthcare

In a nutshell, the hospital needs to be careful on the overhead and direct costs as the consume a lot of revenues. The core business of a hospital is to provide quality health care by applying the available resources efficiently and effectively. A reduction in these costs will reduce costs incurred as well as save the hospital more money. However, they should not cut on service delivery quality as it’s only going to reduce the services as well as service quality. An implementation of these recommendations will improve the hospital tremendously. Medical personnel will be satisfied and have the motivation to serve the people. The hospital will be running efficiently thus improving the profits earned from the hospital. Equally, implementation of these recommendations will help the hospital be considered for upgrade by the national government as well as get more resources to them through acquisition of ICU beds as well as medicine for their pharmacies.

Recommendation on Rethinking Assumptions About Delivery of Healthcare

Competitive clinical coders with sufficient knowledge and skills should be tasked with signing the diagnostic and procedure codes for correct AR-DRG classifications. According to the findings to the study findings, infections, deliveries, and neonates are the leading health problems in UTS hospitals. Therefore, it is recommended for the executive to pay much attention to these critical health problems (Mullenbach et al. 2018).

References for Rethinking Assumptions About Delivery of Healthcare

Aisbett, C., 2011. A methodology for refining AR-DRG. BMC Health Services Research, 11(S1).

Das, J., Woskie, L., Rajbhandari, R., Abbasi, K. & Jha, A., 2018. Rethinking assumptions about delivery of healthcare: implications for universal health coverage. Bmj361.

Delaney, L.J., 2018. Patient-centred care as an approach to improving health care in Australia. Collegian25(1), pp.119-123.

Groom, A., 2018. Describing the ‘clinical truth’in clinical coding. HIM-Interchange8(1), pp.8-13.

Heslop, L., 2019. Activity‐based funding for safety and quality: A policy discussion of issues and directions for nursing‐focused health services outcomes research. International journal of nursing practice25(5), p.e12775.

IHPA, H. Australian Refined Diagnosis Related Groups (AR-DRG) Version 10.0. Retrieved September 14, 2020, from https://www.ihpa.gov.au/admitted-acute-care/australian-refined-diagnosis-related-groups-ar-drg-version-100

Karimi, S., Dai, X., Hassanzadeh, H. &Nguyen, A., 2017, August. Automatic diagnosis coding of radiology reports: a comparison of deep learning and conventional classification methods. In BioNLP 2017 (pp. 328-332).

Kaur, N., Vedel, I., El Sherif, R. & Pluye, P., 2019. Practical mixed methods strategies used to integrate qualitative and quantitative methods in community-based primary health care research. Family practice36(5), pp.666-671.

Khan, S. & Yairi, T., 2018. A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing107, pp.241-265.

Mullenbach, J., Wiegreffe, S., Duke, J., Sun, J. & Eisenstein, J., 2018. Explainable prediction of medical codes from clinical text. arXiv preprint arXiv:1802.05695.

Scheurwegs, E., Luyckx, K., Luyten, L., Daelemans, W. &Van den Bulcke, T., 2016. Data integration of structured and unstructured sources for assigning clinical codes to patient stays. Journal of the American Medical Informatics Association23(e1), pp.e11-e19.

Vaikuntam, B.P., Middleton, J.W., McElduff, P., Walsh, J., Pearse, J., Connelly, L. & Sharwood, L.N., 2020. Gap in funding for specialist hospitals treating patients with traumatic spinal cord injury under an activity-based funding model in New South Wales, Australia. Australian health review44(3), pp.365-376.

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