7. Method
7.6. Study Part 2 – Financial Modelling Procedures
As discussed in previous sections, it is imperative to understand what financial
impacts are there to a public hospital in providing benefits to patients who choose to
use their private health insurance. There has been no study previously in
understanding what such cost is, this chapter aims to answer that question.
Calculation used in this chapter is based on the following formula: Net Revenue =
Total revenue - (Total cost of goods and services plus or minus cost
adjustment). Where total revenue equates to PHI revenue the hospital earned
through patients using their PHI. Total cost of goods and services are the costs
involved in providing benefits for patients who choose to use their PHI. Cost
adjustment is the difference between what the hospital would have paid the visiting
Data collection for the second research question requires research based on
financial information, and this part of the study would therefore not necessarily be
based on an experiential study. The data required for this part of the study would
firstly include the cost of using PHI for the individual patients, the cost of encouraging
patients to use PHI through offering benefits such as free newspapers, free
TV/Telephone, free parking, free toiletry pack (in the instance of Canterbury Hospital,
these are the only benefits being provided to PHI patients) the time spent by private
patient officers (PPOs), administrative staff, finance staff etc. The guiding principle
for this part of the study is that not only should the revenue gained by the hospital be
accounted for, but the cost incurred by the hospital in encouraging patients to use
their PHI or the methods used to attract new patients should also be made clear. In
addition to this, there is a need to quantify other costs or benefits that are not directly
measured by monetary value, e.g. cost of time spent by administrative officers trying
to convert a patient, the potential extra cost for the hospital as potentially private
hospital work is shifted to the public hospital, etc. The archival data search should be
conducted on total PHI patient fees, patient volume, projected return etc. through the
hospital’s own information systems. By putting these two sets of data together, a
model could be constructed to assess the effectiveness of the hospital’s current
strategies in encouraging patients to use PHI, and the financial returns of doing so.
Data collection and analysis
This chapter describes how the data would be collected and analysed.
Part 1:
When the main research questionnaire has been completed, one of the
administrative officers working at Canterbury Hospital had entered the result for each
in such a way that it identifies the questions and their corresponding answers clearly.
For example, survey Question 5 asked the respondents to explain reasons for using
PHI, and provided 8 possible answers, and all the possible options appeared in the
spreadsheet with 1 representing “yes” and 0 representing “no”. All responses that
were completed or partially completed will be recorded in this spreadsheet together
with the corresponding fields. Missing data, or any answers left unanswered were
known as “Not answered” with the notation of “Nil” in the spreadsheet. This method
of data transcribing ensures that all corresponding questions and answers are
presented in such a way that it reflects the original questionnaire structure.
Upon completion of data collection from the study, the data was then interpreted
through a series of analyses. These analyses aimed to identify the various frequency
counts of answered options, and the likelihood of the variables.
For the data collected during the survey on PHI utilisation (Question 5 as an example)
a descriptive ranking had been made to outline which of the reasons constitute the
most prevalent to the least prevalent in affecting the patients’ decision about whether
or not to use their PHI. During the data analysis, the ranking of such results were
simply carried out using frequency counts in order to illustrate their relative
importance and the likely outcome these variables may have. With questions that
had a dichotomous answer, options the data analysis examined the percentage of
people answering “Did you use your PHI for your admission?” or “Did a staff ask you
if you hold PHI?”. The percentage to each of the dichotomous answers was then
compared with the responses provided for their decision about whether or not use
PHI. The data analysis aims to identify any patterns that could produce meanings
There were also some answers to the question that were presented in ordinal scales.
These questions include: “How do you rank your understanding of your PHI policy?”
and “Was the member of staff who informed you about choice of financial election
knowledgeable about private health insurance?”. For the data analysis concerning
respondents’ knowledge of their PHI policy, the results were compared with the
respondents’ propensity toward using the policy in the public hospital. The analysis
can identify whether or not there is a correlation between good understanding of
policy and actually using PHI, or vice versa. On the other hand, with the data
analysis concerning staff knowledge on PHI, the analysis compared the respondents’
rating and their propensity toward using the PHI, or vice versa. The aggregate of the
result from this question, if the respondent indicates an apparent lack of staff PHI
knowledge, then it would have produced a useful analysis result in developing a
remedy to increase staff knowledge in the area. In addition, the questions that aim to
identify the various PHI communication opportunities the respondents have had
during their hospital stays were analysed together with their final choice of whether
or not to use PHI. This small analysis was useful in identifying if any of these
communication strategies had contributed to their final decision to any degree.
SPSS version 18 and Microsoft Excel version 2003 have been used jointly in
conducting the data analysis. SPSS’s strength in data analysis and conducting
significance test had benefited the data analysis part greatly. Microsoft Excel has the
ability to import, export and reformat data in various ways and produce a graphical
representation of data analysis. These functions have been useful when conducting
the analysis and presenting the findings of the analysis.
Analysis of the data has been conducted in order to understand respondents’ views,
measurement of likelihood of PHI utilisation, by way of analysing correlation between
the answers provided in various questions, may not take data variation into
consideration. Some data obtained may be abnormal or considered as anomalous
due to the above reasons.
Part 2:
Archival data collected for the second part of the study help to facilitate a financial
modelling on both individual benefits versus cost and hospital benefits versus cost.
There had been attempts made to ensure that the data obtained in this stage were
defined accurately and analysed for their application in the study. Several likely
analyses required in this stage of data analysis are listed as below.
Firstly, an analysis looked at what the average cost of an individual using PHI would
be; this data had been constructed into an aggregate model in order to understand
the average cost for a typical patient admission using PHI versus admission as a
public Medicare patient. There are several variable factors involved, such as how the
patient entered the health service and what type of insurance policies they hold.
Private patients entering Canterbury Hospital via the ED do not have to pay any
OOP expenses, whereas they may have to pay if they come via the elective surgery
corridor. The cost modelling for the individual will be quite different based on their
mode of entry to the hospital. Furthermore, individuals’ personal circumstances and
the type of cover they have may vary, and these factors could all affect the way in
which this model would be constructed. Moreover, tangible benefits such as free
newspapers, free parking etc. can be seen as having a positive financial impact to
the patient, and these values could be added directly as benefits. It should be noted
that the model is a way of predicting the general cost and benefits for the individual,
cent. Therefore, it may not be exactly practical to utilise a cost model to measure an
individual’s perceived and actual utility of using PHI.
For the hospital in the context of PHI utilisation, benefit and cost data is collected
through the central financial information system controlled by the SLHD. A similar
model to the abovementioned can be constructed based on the financial data
obtained to determine the cost versus revenue raised as a result. The analysis
focused on the concept of ROI, a key financial analysis ratio that is being widely
used in the commercial world (Laitinen, 1991). In essence, this analysis applies the
ROI model to evaluate the effectiveness of the investment – financial costs involved
in encouraging and converting patients to use their PHI (tangible benefits, waiver of
fees, cost of resources, etc.) and financial return – various revenues, including
patient fees revenue (income received from the insurance companies), prosthetic
device revenue, diagnostic and pathology test revenue, etc.