Application of Health Data
Assessment task 2: Case Study
Intent: This assessment item focuses on the ability to concisely respond to specific questions and to
demonstrate an understanding of the management and application of health data.
Objective(s): This assessment task addresses subject learning objective(s):
A, B, C, D and E
This assessment task contributes to the development of graduate attribute(s):
1.2, 2.1, 2.2, 3.2 and 4.0
Type: Case study
Weight: 30%
Task: 1. Read the following case scenario.
Provide a response that demonstrates an understanding of the application and management of
health data and refers to literature related to the identified issues and associated tasks.
2.
In the response, apply your findings to the hospital so as to assist the executive group in decision
making and planning.
3.
- The response should include appropriate and properly formatted tables and figures.
Case Scenario – UTS Hospital
UTS hospital is a well-established charitable hospital operated on a not for profit basis. It has 250
beds in an inner-city location. The population of the local community, from which it draws the majority
of its patients, is ageing: 40% are over the age of 65 years. UTS hospital has an excellent reputation
for innovative care, rapid uptake of new technologies, teaching and research. It gets very little
support from the government for running costs, although previous governments have been generous
in meeting the cost of constructing new buildings and refurbishing old ones.
The hospital is in financial difficulty. Over 90% of the funding to the hospital for acute inpatients
comes from private health insurers. The remainder is from the Department of Veterans Affairs,
patients who pay for their own admissions, compensable patients from motor vehicle and workplace
insurance, and patients whose stay is paid from a research grant. The rate of reimbursement from
private insurers is based on a negotiated rate for each AR-DRG. Every year, insurance companies
negotiate with the hospital the rate it pays for each AR-DRG (i.e. a type of casemix- or activity-based
funding). The fees are based on the average length of stay for each AR-DRG using the Australian
cost weights.
The Chief Executive Office (CEO) has called a special meeting of the executive to discuss the issues
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The Chief Executive Office (CEO) has called a special meeting of the executive to discuss the issues
facing the hospital and to plan the action they need to take. Present at the meeting are the Health
Information Manager (HIM), the Chief Financial Officer (CFO) and the Chief Information Officer (CIO).
The HIM is of the opinion that casemix-based funding using AR-DRGs are not the best method to
record performance because they do not suit the type of patients treated by UTS Hospital. She
states the majority of patients is older and more complex, and need to stay longer than the average
length of stay for each AR-DRG. She suggests that AR-DRGs are useless for measuring the
hospital’s performance when the length of stay of the patients is different to that of the average
hospital. She is of the view that the hospital should go back to insurance funds and negotiate a return
to the funding of patients on a fixed per diem basis.
As the HIM’s assistant, you are tasked with examining UTS Hospital data, and preparing a summary
for the HIM to use in the upcoming meeting.
You are asked to include the following information in the summary:
Background
Definitions for per-diem and casemix funding.
Description of the differences of these two funding models.
Identification of the pros and cons of the casemix-based funding approach compared to a fixed
per diem rate.
Description of how casemix funding is achieved in Australian hospitals.
Conclude this section with a statement of the aim of the analysis.
1.
Method
A brief description of software and techniques used to examine the data
2.
Results
Visual presentation to describe the relationship between Length of Stay (LoS) and age for the
entire dataset, accompanied by a description of the findings.
Tabulated presentation of the top most frequent AR-DRG for patients aged 70 years and older,
accompanied by a description of the findings.
3.
Discussions
A brief discussion of the findings in relation to the two funding models.
4.
Conclusion and recommendation
Provide a short statement of conclusion and recommendation that is linked to the UTS Hospital.
5.
Length: Maximum 900 words
Due: 11.59pm Wednesday 13 September 2017
Criteria: 10% Clear definitions of casemix and per-diem funding models.
10% Clear description of the differences of the two funding models.
10% Clear discussion of the pros and cons of the two funding approaches.
10% Clear description of how casemix funding is achieved in Australian hospitals.
5% Clear description of aim of the report.
5% Clear description of method of analysis.
15% Appropriate data presentation and description of findings.
15% Discussion of the advantages and disadvantages of casemix and per-diem funding and their
potential impact on a hospital.
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10% Statement of recommendation that are linked to the UTS Hospital.
10% Produces correct grammar, spelling, formatting, style and referencing.
Further
information:
Students will receive individual feedback via UTSOnline and face-to-face feedback at 3rd workshop.
Casemix and per diem funding definitions
In the hospital context, per diem refers to funding for inpatient services that provides a fixed cash amount for a patient`s day in the hospital (Turner et al., 2012). They add that this is regardless of the hospital`s charges or expenses incurred for caring for that given patient.
Casemix funding depicts a method of assigning funds based on the activities that hospitals execute and on the number and types of patients treated (Turner et al., 2012). According to them, this model has the following fundamental requirements: counting the number of treated patients, classifying patients treated, and the cost of patients treated.
Diagrammatic representation of the casemix system considerations
Differences between the two funding models
According to (O`Reilly et al., 2012), there exist monumental differences between the two health funding models, as illustrated in the table below.
| Per diem model | Casemix model |
| § It offers a fixed amount of cash for a patient`s day in the hospital | § Provides funds for the overall number of patients treated in the hospital |
| § It does not consider the type of patients treated at the hospital | § Considers the type of patients attended to at the hospital |
| § It does not consider the expenses incurred for those particular patients | § Considers the incurred care expenses for the patients |
Pros and Cons of the two funding models
According to (Vian et al., 2012), the three most common pros and cons of the per diem funding model are as follows;
Per diem pros and cons table
| Strengths | Weaknesses |
| · They have facilitated straightforward administration and contracting for many years. | · Hospitals lack the incentive to avert needless days during hospitalization |
| · This payment method offers some limitations on cost-generating hospital conduct since the daily amount payment is perpetually set (while the actual sum payment is retrospective). | · They do not offer adequate transparency about hospitals` real clinical activities. They do not allow comparisons among hospitals on outputs or activities produced |
| · They can offer increased transparency for consumers to enable them to compare lengths and prices of stay among hospitals as a measure of the overall hospital costs | · Efforts to curb costs may need third parties which monitor per diems to identify medical necessity via aggressive, persistent stay medical review. This causes administrative intricacy and at times, unsuitable clinical care intrusion |
Casemix pros and cons
The strengths and weaknesses of casemix funding are evidently straight forward as illustrated below (Heslop, 2012).
| Strengths | Weaknesses |
| · Increased efficacy and lowered cost per patient | · Rise in general hospital spending attributed to increases in the number of patients |
| · Higher healthcare access and minimized wait times | · Establishment of incentives for unnecessary care provision |
| · Heightened patient satisfaction and quality of care | · Need for comprehensive data and reporting information that may not be effortlessly available and is expensive to actualize |
| · Enhanced employment of best clinical practices | · Hardships in establishing the suitable funding amount for hospital care |
| · A movement in elective care to target clinical areas which are highly profitable |
Casemix funding in Australian hospitals
It is meant to monitor and manage healthcare funding offered in hospitals, particularly public hospitals (Curtis et al., 2011). The Diagnosis Related Groups (DRGs) system in the country categorizes 10,000 diseases into 700 groups (Uzkuraitis et al., 2010). According to them, the patients and their treatment costs (on average) are identical since the illnesses have identical resource use and cost. They add that procedures and diagnoses are translated into codes and entered into a computer software package that attaches a DRG to the inpatient episode. Each inpatient episode is awarded one DRD. The attached DRG and codes are then relayed to the Department of Health (DoH), after which the hospitals are funded accordingly (Nocera, 2010).
The aim of this analysis is to enable the HIM to provide the executive with holistic information about the two funding models to make informed decisions on the best way forward.
Method
I have employed Microsoft Excel software. It is a software tool that allows users to analyze data nearly unlimitedly (Isik et al., 2013). It fulfills the broadest array of analytical needs. I have used both the qualitative and quantitative data analysis techniques.
Results
Length of stay (LoS) and age visual representation
The figure above shows that the length of stay increases with age. The aged an individual is, the greater the possibility of staying longer at the facility for treatment and care. As such, the old, above 65 years, stay longer.
Tabulation of the top most frequent AR-DRG for patients of 70 years and above
| AR-DRG | MDC | Description |
| U63A | 19 Mental diseases and disorders | Major Affective Disorders |
The above table shows that the U63A is more prevalent among people who are aged 70 years and above. This is because the aged normally face mental regression attributed to the death of some brain cells, inactivity, and fatigue.
Discussions
The two hospital funding models are suitable based on the needs and structure of the given hospital. However, casemix model seems to be more appropriate universally. This is because the payment is made based on the total activities of the hospital the type, and the number of patients treated. This is the model`s alluring attribute.
Conclusion and recommendation
The casemix funding model remains the most appropriate system for the UTS hospital. However, the hospital needs to polish some of its operational areas and convincingly engage the government for sustainable financial support.
References
Curtis, K. et al., 2011. Do AR-DRGs adequately describe the trauma patient episode in New South Wales, Australia? Health Information Management Journal, 1(40), pp. 7-13.
Heslop, L., 2012. `Status of costing hospital nursing work within Australian casemix activity-based funding policy.` International Journal of Nursing Practice, 1(18), pp. 2-6.
`Isik, O., Jones, `C. & `Sidorova, A., 2013. `Business intelligence success: The roles of BI capabilities` and` decision-making environments`. Information & Management, 1(50), pp. 13-23.
Nocera, A., 2010. Performance-based hospital funding: a reform tool or an incentive for fraud? Medical Journal of Australia, 4(192), p. 222.
O`Reilly, J. et al., 2012. `Paying for hospital care: the experience with implementing activity- based funding in five European countries. Health, economics, policy and law, 1(7), pp. 73-101.
Turner, L., Sutch, S., Dredge, R. & Eagar, K., 2012. International casemix and funding models: lessons for rehabilitation. Clinical Rehabilitation, 3(26), pp. 195-208.
Uzkuraitis, C., Hastings, K. & Torney, B., 2010. `Casemix funding optimization: working together to make the most of every episode. Health information management journal`, 3(39), pp. 47-49.
`Vian, T., Miller, C., Themba, Z. & Bukuluki, P`., 2012. Perceptions of per diems in the health sector: evidence and implications. Health policy and planning, 3(28), pp. 237-246.