Harness the power of data analytics in healthcare research

Peilun (Ann) Liu
3 min readMar 10, 2021
Photo by Mark König on Unsplash

People say that data analysis is a universal skill, you can gather insight with data analysis in almost every industry. This is true in business sectors that have data on the users and clients, and want to improve their quality of service or maximize their profit. But what about not-for-profit organizations? During my MSBA program, I had the opportunity to work with a healthcare research institution and think about ways to incorporate data analytics to make their job easier. In this blog, I will talk about a few implementations of data analysis that we have identified to be or can potentially be important and even strategic for scientists and researchers in the healthcare industry.

1. Improved efficiency and scalability

Hospitals and healthcare institutions usually don’t have a large data team. Physicians and scientists are also not familiar with the tools used for data analysis. On the other hand, they have a lot of data from different test results and patient profiles. This creates a gap between the amount of data and the actual capability of data processing, and the full potential of this data is yet to be discovered. Before our business partner started working with our program, they used excel to manage their data, which requires a lot of manual work and made it time-consuming to add or update any information. During our project, the student team helped build an automated data pipeline that stores, prepares, and visualizes the data for the scientists. The scientists only need to upload raw data and will get an easy-to-navigate dashboard with all the information they need. This has helped them shorten their data process time “from hours to minutes”, as our business partner said, so they can focus on creating value out of their data. More importantly, scientists can now store a large amount of data and incorporate different types of data to scale up and expand their research scope in the future.

Photo by Andreas Klassen on Unsplash

2. Added analytics power

The introduction of tools such as database, python, and tableau leads scientists to a world with endless possibilities of ways to analyze the data they have. Before this project, the kinds of analysis that scientists can do were limited. Even the comparison of two different sample profiles was difficult with scattered files. Now the scientists can connect different drug responses to their genetic profiles and uncover much deeper insights into their research. The consolidation of different data sources itself is already beneficial. On top of that, programming languages and algorithms enable scientists to conduct complex computations and build statistical models easily. Moreover, the added visualization layer helps create useful content for case studies or even research papers that can help draw more funding for future research.

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3. Additional industry impact

Data analysis is gradually being incorporated into the healthcare industry with a focus on customer service. However, most healthcare institutions are working with outside consultants to create data solutions and only have a small amount of dedicated data analysts. It is even rarer to see data analysts in research institutions. Since the length of our project is limited to the duration of the program, we hope that they can gradually build a data team that can help maintain and expand the platform we built, so the tools that the scientists can use will get better and smarter. This can give them a leading position on the application of data analysis in healthcare research and help create a bigger industry influence. Eventually, help them become more recognized and get more resources to support their future research.

There is no denying that data analysis has a unique strategic position at most companies these days and can lead to competitive advantages. Although it may seem to be less relevant to a not-for-profit research institution, the value it adds is never less.