Analysis And Comparison Of Casemix And Per Diem Funding Models For Healthcare Services

Description of UTS Hospital Case Study

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 namely: counting the number of treated patients, classifying patients treated and the costing of patients treated.

Diagrammatic representation of the casemix system considerations 

                                                         

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

According to (Vian, et al., 2012), the three most common pros and cons of the per diem funding model are as follows;

Strengths	Weaknesses
· They have facilitated straightforward administration and contracting for over many years.	· Hospitals lack the incentive to avert needless days during hospitalization
· This payment method offers some limitations on cost-generating hospital conduct, since 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

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 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

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 diagnosis are translated to codes and then 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.

I have employed Microsoft excel software.  It is a software tool which permits users to analyze data in a nearly unlimited ways (Isik, et al., 2013).  It fulfills the broadest array of analytical needs.  I have used both the qualitative and quantitative data analysis techniques.

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 the 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.

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 number of patients treated. This is 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.