PowerBI dashboard
Budget: $10 – $30 AUD
The Problem
One of our key business drivers is the overall value proposition of private health insurance (PHI): private insurance must be good value for money. In other words, the consumer will only take up insurance if the total cost can be justified by the quality of care, condition of private facilities, accessibility of services, etc… PHI has two primary types of coverage: Private Hospitals and “General Treatment” (ancillary coverage for dental/chiro/physio etc..). As a provider of acute private care, our focus is primarily on the former.
The key metric for the value proposition of PHI is % of population that has taken up insurance. As a general rule, the greater the uptake, the more buoyant the industry. Industry uptake peaked in July 2015 at 47.4% and has since experienced 16 consecutive quarters of decline to 44.2%.
While uptake is a key metric, it may not translate directly to hospital activity & revenue. For example, if healthy individuals are dropping coverage, the impact to total hospital episodes and benefits paid to hospitals may be less than the uptake percentage might suggest.
The two key questions I’d like you to answer are:
A) What could be causing a decrease in hospital coverage uptake since June 2015?
B) In that time period, what is the corresponding impact to industry-wide private hospitals benefits and activity (both episodes & patient days)?
The Deliverables
1. Build a model in Power BI using APRA data (linked below)
2. Evaluate the data in the context of the two questions above, preparing a single Power BI report
3. Provide brief commentary (in bullet point format) for each visualisation, including the inferences you’ve drawn from the data
The Data
· The data can be found here. https://www.apra.gov.au/quarterly-private-health-insurance-statistics
· Unhide the sheets in the Excel to gain access to the detail of some of the datasets. You are free to use whichever datasets you believe are the most relevant.
· Please ignore general treatment and prosthesis figures
· A number of charts & visuals exist already, but I encourage you to be creative
· Also of relevance is a glossary: https://www.apra.gov.au/sites/default/files/qphis_glossary_0.pdf
One of our key business drivers is the overall value proposition of private health insurance (PHI): private insurance must be good value for money. In other words, the consumer will only take up insurance if the total cost can be justified by the quality of care, condition of private facilities, accessibility of services, etc… PHI has two primary types of coverage: Private Hospitals and “General Treatment” (ancillary coverage for dental/chiro/physio etc..). As a provider of acute private care, our focus is primarily on the former.
The key metric for the value proposition of PHI is % of population that has taken up insurance. As a general rule, the greater the uptake, the more buoyant the industry. Industry uptake peaked in July 2015 at 47.4% and has since experienced 16 consecutive quarters of decline to 44.2%.
While uptake is a key metric, it may not translate directly to hospital activity & revenue. For example, if healthy individuals are dropping coverage, the impact to total hospital episodes and benefits paid to hospitals may be less than the uptake percentage might suggest.
The two key questions I’d like you to answer are:
A) What could be causing a decrease in hospital coverage uptake since June 2015?
B) In that time period, what is the corresponding impact to industry-wide private hospitals benefits and activity (both episodes & patient days)?
The Deliverables
1. Build a model in Power BI using APRA data (linked below)
2. Evaluate the data in the context of the two questions above, preparing a single Power BI report
3. Provide brief commentary (in bullet point format) for each visualisation, including the inferences you’ve drawn from the data
The Data
· The data can be found here. https://www.apra.gov.au/quarterly-private-health-insurance-statistics
· Unhide the sheets in the Excel to gain access to the detail of some of the datasets. You are free to use whichever datasets you believe are the most relevant.
· Please ignore general treatment and prosthesis figures
· A number of charts & visuals exist already, but I encourage you to be creative
· Also of relevance is a glossary: https://www.apra.gov.au/sites/default/files/qphis_glossary_0.pdf