Estimated Resource Use in Australian Patients below 65 years of Age

The study examines the prescribing patterns of patients aged below 65 in Australia, using data from the General Practice Research Database (GPRD). The analysis reveals that the median number of prescription medications per patient per year is 2, with a higher proportion of prescriptions given to older age groups and those with poorer health. Additionally, there is variation across practices in prescribing patterns, particularly for patients over 65 years old and those in the sickest morbidity groups. The study highlights the importance of considering patient morbidity when developing prescribing models and suggests that incorporating diagnosis-dependent patient morbidity measures may help to explain variability in prescribing patterns between practices.

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Using healthcare data for decision making
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1. Background
Per-diem refers to a hospital activity of charging rates on daily bases where the expenses
incurred are all averaged over the entire hospital population.
Casemix funding refers to a method of allocating funds considering number of patients
treated and also the types of patients treated (Milovic, 2012). For case mix funding to be used
there are requirements needed:-
Patients treated classified considering the disease treated and type of treatment administered.
The total cost of the patients treated.
For counting its required for proper administrative health data collections that are maintained
by health departments (Stiggelbout, Van der Weijden, De Wit, Frosch, Légaré, Montori,
Trevena and Elwyn, 2012). For classification all patients treated are classified into different
diagnosis related groups that shows patients those who have similar conditions and require
the same treatment and resources.
Costing includes all cash paid to be reported as a part of good hospital management for both
patients who are admitted and those who were not admitted.
One of the disadvantage of casemix funding is that one cost fund is used in order to fund each
of the Patient considering that not every individual needs the same amount for treatment each
patient has his/her own charges requirement.
Also casemix funding creates financial risks to the patients and also the providers of health
care unlike for the case of per-diem rate where finances are properly management ensuring no
misusing of funds and every cost is taken care of through proper planning (Ryan, Gerard, and
Amaya-Amaya, 2007). Since there is lenient record keeping there are no financial risks at all
for per-diem rate.

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Per-diem helps a lot in covering the staffing needs this is because the staffing needs varies
from time to time considering the climatic condition of a place where hospitals are located.
In Australian hospitals before they paid per diem only but later the national health insurance
scheme was introduced and after the introduction the hospitals were of the completely new
settlement settings to become much more utilized (Ryan, Gerard and Amaya-Amaya, 2007).
Hospitals that offered much more intricate services required extra benefits and there some
more categories of hospitals were added including surgical, medical and advanced surgical.
For patients classifications the government adopted private sector hospital classification that
was not friendly at all and thus ruined it. Some years later the government introduced patient
classification. In additional casemix funding in Australia is expected to put all hospital
funding above politics and payments of this funding varies from one hospital to another.
Public sector casemix has been introduced also and suggests that repayments would certainly
cover up the adjustable expenses of hospitals along with the fixed populace dependent area
financing would certainly cover up all the fixed expenses.
Description of difference between case mix funding and per diem funding model
The casemix funding method highlight the kind of the mix which the patient was treated
when it comes the resources that depends on the parameter of interest. UTS hospital has
classified people into various groups (Koh and Tan, 2011). On the per diem model there is a
fixed amount of payment which is offered to the patient per day while in hospital, regardless
of the charges which they incur in the hospital.
Statement of aim of analysis
The aims of this analysis was to highlights the difference between the casemix funding and
fixed per diem funding. The focus has been on the pros and cons of these methods.

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2. Methods
Data from the Common Practice Research Database (GPRD) was employed for this study.
Basic procedures working for the GPRD carry out consented recommendations for the
recording of medical and prescribing information, and submit anonymized patient-based
clinical records to the database with some regularity. The precision and comprehensiveness of
the data documented in the GPRD continues to be documented previously. The data includes
demographic items, clinical data, laboratory tests and other values, and prescribing
information. Data from the GPRD on patient diagnoses, prescriptions, age and gender were
acquired. Initially based on age, gender and a combination of documented diagnoses over a
one year period, patients were allocated using the
ACG System software5. These types of ACGs were after that grouped into six collectively
exclusive classes employing the ACG software program which ranks the ACGs based on the
patients’ estimated resource use , depending on that of a nationally representative database of
two million patients of below 65 years of age in the Australia ( Ryan and Farrar , 2000 ) .
These types of 6 groups were accustomed to characterize patient morbidity sets ranging from
the healthiest to the sickest in addition to were employed like a method of clinical
circumstance mix of the patients. Age was arranged as young people , teenagers , older
grownups as well as aged
The variety of medications documented in the GPRD was adequate to approximate the
models’
Coefficients with preferred degree of accuracy .
3. Results
After exemption, there have been 129 procedures in the GPRD with an overall of 1, 032, 072
patients, with 49 .3% men as well as 50 .7% women. The total prescribing rate was 4 .5
products per affected person per year as well as 64% of the affected individuals were given a

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prescribed at least one time in the course of
2001 . The median percent of victims given a medication by practice was 65%. The median
number of prescription medications issued was determined for every of the 129 practices and
median of those was two.
The percent of the patient in the several sickest morbidity groups were little and therefore
were joined in most analyses. The median variety of prescription medications given amplified
with age bracket together with morbidity sets and was larger for females (Koh and Tan,
2011). The gender distribution of the victims was equivalent across the procedures. The
proportion of victims in various age group and morbidity groupings diversified across
methods to certain scope with the largest variance observed for patients above sixty five years
of age and for morbidity. There was clearly furthermore certain variance across techniques in
patient syndication for the 2 healthful morbidity sets.
The median variety of prescription medications given diversified the majority of between the
methods for patients aged above sixty-five as well as for the sickest morbidity sets.
The estimated amount of prescription medications for men and women aged zero to fifteen
were projected to be 1 .6 and also 2 .2 respectively (Ryan and Farrar, 2000). The related
estimated figure is 9 .2 and also 12 .7 for men and women aged sixty-five in addition to over
respectively. For the healthiest males and females aged zero to fifteen, the projected range of
prescriptions is 0 .05.
Visual representation between Lengths of stay and age

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Discussion of findings
From the diagram below it highlights the average length of stage in hospitals by age. In the
figure shows that the higher the age of the patient the higher the number of stay in the
hospital. This is applicable also to the lower age groups. The young individuals stays fewer
days than the old.
Tabulated presentation

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Discussion of findings
Based on the data presented on DRG it is evident to highlights that the older individual who
are over 70 years suffers more from the common ailments and as highlighted there are
various AR-DRG components that are shown.
4. Discussion
The affected individual’s morbidity describes significantly more of the variability in
prescribing compared to affected person age as well as gender only (Edwards and Elwyn,
2009). Relating to 4% of the entire variance is at the practice degree in addition to the
majority of the variance is within methods.
5. Conclusions and recommendations

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This research reveals that addition of a diagnosis dependent affected person morbidity
measure into prescribing models can describe a lot of variability at both patient and practice
levels. The usage of patient-based scenario mix techniques needs to be researched additional
whenever investigating variance in prescribing designs between procedures in the Australia,
particularly for particular prescribing categories, together with may confirm beneficial in
fairer utilization of financial budgets.

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References
Edwards, A. and Elwyn, G. eds., 2009. Shared decision-making in health care: Achieving
evidence-based patient choice. Oxford University Press.
Koh, H.C. and Tan, G., 2011. Data mining applications in healthcare. Journal of healthcare
information management, 19(2), p.65.
Milovic, B., 2012. Prediction and decision making in health care using data mining. Kuwait
chapter of arabian journal of business and management review, 1(12), pp.126-136.
Ryan, M. and Farrar, S., 2000. Using conjoint analysis to elicit preferences for health care.
BMJ: British Medical Journal, 320(7248), p.1530.
Ryan, M., Gerard, K. and Amaya-Amaya, M. eds., 2007. Using discrete choice experiments
to value health and health care (Vol. 11). Springer Science & Business Media.
Stiggelbout, A.M., Van der Weijden, T., De Wit, M.P., Frosch, D., Légaré, F., Montori, V.M.,
Trevena, L. and Elwyn, G., 2012. Shared decision making: really putting patients at the centre
of healthcare. BMJ: British Medical Journal (Online), 344.