Data analysis using the data warehouse prototype

The SAS enterprise guide 4.2 was used to analyse the data to answer questions 1, 2, 4 and 5. The first question addressed was question 1 from Table 6:”Comparison of risk scores – group by PREDMORT” (In the ICU risk score is named as Score and Cardiac surgery unit risk is named as PREDMORT). The

69 following figure (Figure 29) shows an information product for the analysis results; for the comparison of risk scores from the cardiac surgery unit and the ICU, grouped by cardiac surgery risk score (named as PREDMORT).

Figure 29: Comparison of risk scores –group by PREDMORT

This will provide clinicians from the cardiac surgery unit and ICU with a better understanding of the relationship between the preoperative cardiac surgery risk score for death and significant morbidities and risk of death from ICU comparison, and relates to the questions such as how the average risk for different clinical groups varies after surgery and what factors are involved and how to use the risk scores to improve performance outcomes in the cardiac surgical unit and ICU for cardiac surgical patients. Figure 30 shows the graphical display of interaction of risk scores.

Figure 31 shows an example of an information product from the prototype data warehouse to support decision-making processes based on Q2 from Table 6: “ The actual expenditure (AU$) per episode of care according to certain clinical groups: by procedural groups”. This shows the SAS analysis report for the actual expenditure per episode of care according to the major cardiac surgical clinical procedural groups and the results are grouped by patient age. This result gives clinicians the ability to understand how the total cost of an episode of care in the finance database relates to patient groups according to the surgeons’ frame of reference that is the clinical procedure groups used by surgeons in their clinical audit and monitoring processes. This can then be further combined with Transition II data to compare actual costs for these clinical groups with the State funds provided to the hospital according to the DRG groups. The information can be further broken down according to other clinical criteria such as age groups (as shown), or hospital post-operative morbidities captured on the CARPIA database such as deep sternal infections, or physiological parameters captured in the ICU database such as core body temperature variations at admission to ICU following surgery.

Figure 31: The actual expenditure per episode of care according to the certain clinical group

71 Analysis results for question Q4: “Cost of various post operative complications (AU$) – by bleeding morbidity group” shows (Figure 32) the summary statistics for the cost of various post-operative complications for example grouped by post- operative bleeding morbidity group. This will help clinicians to identify the cost implications of clinical issues and prioritise the quality improvement process as well as potentially evaluate cost savings from quality improvement processes resulting in reduced high cost morbidities, thereby valuing and appropriately resourcing such activities.

Figure 32: Cost of reoperation for bleeding as an example of post operational complications (AU$)

Analysis results for question 5: “Audit data sources to verify costings data includes high cost procedures appropriately” is shown in Figure 33. This output shows the costs associated with the DRG’s allocated according to the cardiac surgery unit admission status. Further analysis of this can contribute to evaluation of appropriate funding structures for institutions according to surgery status performed.

Figure 33: Costs associated with the DRG’s- according to cardiac surgery unit admission status (AU$)

Figure 33: (continued) Costs associated with the DRG’s- according to cardiac surgery unit admission status (AU$)

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Figure 33: (continued) Costs associated with the DRG’s- according to cardiac surgery unit admission status (AU$)

Limitations and constraints in the data extraction and data analysis process must be considered in the interpretation of these information products and include:

1. Extracted data from the CARPIA, ICU, Transition II are limited to a sample of year 2009.

2. Did not address the question 3 (“What is the rate of e-discharge summaries send to GP’s according to clinical guidelines for the cardiac surgical patients according to operative data, surgical consultant?”) because of the technical difficulties experienced in directly connecting to the e-DS database and time limitation of the research project.

3. All the extracted data are restricted to the Cardiac surgery unit patients.

4. When comparing the risk scores from CARPIA and ICU, a small number of ICU patient’s data from patients who returned back to ICU on the same day were excluded.

5. Data analysis is limited to 1000 patient records due to the study’s time constraints.

In general, gain from the benefits of data warehousing may take some time, for example, changes resulting from mismatch of State funding compared to actual costs for certain procedure groups may require further analysis and reporting to further stakeholders to facilitate change to the costing structure. On the other hand, some benefits can have a more rapid local effect, such as recognition by clinicians of the differential costs for various morbidities and implications for selection of quality improvement activities. In this research project, the data warehouse prototype was evaluated by collecting the feedback from the end users after reporting and explaining the analysis results.