Healthcare Analytics using
Microsoft PowerBI

Monitor health, happiness and resources
to help your business make effective data-driven decisions

Healthcare Analytics using
Microsoft PowerBI

Monitor health, happiness and resources
to help your business make effective data-driven decisions

Introduction, Planning, and Establishing a Reason for the Project:

During a medical crisis like the global pandemic of COVID-19, healthcare is at the forefront of the news. Healthcare is crucial and looks different in each country. Looking at the global healthcare spending system while also being able to look into individual countries’ details is vital to many stakeholders. Accessibility to healthcare is linked to happiness, and economic determinants of whether an individual receives healthcare are, therefore, of great importance.

Some questions that could be asked on this topic are:

  • Does happiness increase in a country where healthcare is affordable?
  • What impact does government subsidies have on the happiness of its citizens?
  • Are countries with development assistance for healthcare systems happier?
  • What are the economics of healthcare?
  • How do the economics of healthcare relate to happiness within a country?

Data Collection

Data sources for this sample analytics report are from the United Nation’s World Happiness Report and the Institute for Health Metrics and Evaluation (IHME). Both sources are online, from the years 2017-2019.

Data from the United Nations are centered around ladder score (a happiness metric) by country and look at GDP per capita, social support, healthy life expectancies, freedom to make life choices, and other relevant contributors to happiness.

Data from the IHME looks at healthcare spending by country, looking into categories like government spending, out of pocket spending (spending by citizens), development assistance, and total health spending as a percentage of GDP.

The data from the United Nations and IHME give an overview of both happiness and healthcare expenses by country for the years 2017-2019. With these two data sets, we can begin exploring and creating dashboards within Power BI to aid stakeholders such as a non-profit looking to utilize donated money in a country with a low ladder score and little developmental assistance towards its healthcare system.

Data Analysis

Exploring and analyzing the data on a global scale, we find that there are some common-sense correlations. Citizens in nations with a higher GDP per capita, those with more freedom to make life choices, and those whose governments spend more towards subsidizing healthcare are happier.

  • If a country has a higher GDP per capita, it’s citizens will have more income to spend on healthcare, better diets, and other determinants of a healthy life like social relationships (going out with friends). An increase in GDP per capita will increase a healthy life expectancy.
  • If a country gives its citizens more freedom to make life choices, it will have a ladder score. This makes sense, as those within a country with more freedom to make life choices are freer to pursue their passions, interests, and more that lead to a happier life.
  • If a country’s government spends more on subsidizing healthcare, its citizens have more money to spend on experiences, services, and goods that increase their happiness rather than healthcare. This frees up money, compounding the freedom to make life choices and utilization of GDP per capita towards direct happiness and not costs of healthcare.


Dashboard Building and Evaluation

On the first page of this dashboard, we will focus on the global scale, and look at relevant metrics to it. We have the scatter plots with a trend line talked about earlier, as well as line and clustered column charts. The line and column chart on the left displays how total health spending is a high percentage of GDP (globally), and that out of pocket (personal) spending on health care is increasing.

The line and column chart on the right shows that there is a slight downtrend on developmental assistance for health care spending (per person), and an increase in government spending on healthcare (per person).

These visuals together start to build a narrative and give insight into the problem of increased healthcare spending by citizens globally. There is a decrease in development assistance, while healthcare costs rise; this increase in cost is eaten up by individuals’ out-of-pocket spending. An increase in healthcare costs is unavoidable, as some healthcare costs are necessary to continue living a healthy, long life. Therefore, the cost for individuals is important, as it directly affects disposable income, and income is correlated to happiness. Additionally, with a higher disposable income, individuals are freer to spend their money, and as we see on the freedom and ladder score scatter plot, the higher the freedom, the more happy a country is.

Healthcare, in conclusion, is rising in cost, and out of pocket costs are rising with that, while income is a significant contributor to happiness. Disposable income can be associated with freedom, and freedom to make life choices is another significant contributor to happiness. Government spending and development assistance are two ways to lower healthcare costs for an individual and increase a country’s ladder score.

Now, we begin to look at our data by country in this second dashboard. We are looking at metrics specific to a country, but relevant to the bigger picture of happiness and healthcare economics. From this interactive visual, we can look at a country’s relative ladder score standing, as well as the economics of its healthcare system. From this dashboard, we can get an overview of how a country is doing, and compare it to similar countries, getting insight into what changes can be made to increase ladder score.

For example, you may see that Australia has no developmental assistance, but a relatively large amount of government spending on healthcare per person. This results in a low difference in total health spending and out of pocket spending by GDP, meaning that those living in Australia spend less out of pocket (just over 1% of GDP) for healthcare. All of this contributes to their excellent standing of 10th in the world for happiness, according to the United Nations.

This third dashboard shows the key influencers to happiness and out of pocket healthcare spending. These two dependent variables are connected, as established earlier, the less out of pocket spending on health, the happier an individual will be. We can see how many social factors contribute to happiness, like perceptions of corruption or social support, as well as the variables established earlier. We also see that development assistance is a massive influencer to out of pocket healthcare spending, further supporting our narrative that third party money towards healthcare is a good thing, freeing up money for individuals to pursue happiness.


In this last dashboard, we can forecast some concluding variables. We see that development assistance is declining for Nicaragua, while healthcare spending is increasing along with out of pocket spending on healthcare. This will likely, based on this project’s data, result in a lower ladder score for Nicaragua.

This forecasting, along with the other dashboards, will give decision-makers with the ability to create change, aid in the restructuring of healthcare costs for countries that need it!


In conclusion, this project starts off with a narrative and outlook of the world by happiness in relation to healthcare costs with our first dashboard. This introduction to the data aids a decision-maker such as a non-profit with resources looking to invest in development assistance. That first group of visuals will help them understand the impacts of the economics in a healthcare system has on the happiness of a country.

The second dashboard and its visuals allow a decision-maker to understand deeper into the economics of healthcare within a country, with the potential for identifying those in need of more developmental aid. For those with little or no development assistance and little or no government spending on healthcare, additional developmental assistance would contribute most to an increase in ladder score.

The third dashboard, allows decision-makers to understand how much of an impact each variable has on happiness and out of pocket spending on healthcare. Both of these are connected.

And finally, the fourth dashboard gives a forecast of what a country’s healthcare system will look like economically, allowing for those who can intervene to do so and change the country’s path.

Want to know more?

If you think this solution would be useful for your organization or you have a relevant use case or pain point you would like to tackle, get in touch with us today and we can help you and work together towards a solution!