Using Healthcare Data For Making Decision Essay


Discuss About The Using Healthcare Data For Making Decision?



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, L?gar?, 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.

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.


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 .


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 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

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

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.

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.


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.


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., L?gar?, 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

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