4 Strategies That All Healthcare Data Analytics Must Know

By Jessie L. Morgan Friday, September 13, 2019
Big data is becoming a rising trend in a huge number of industries, especially in the healthcare field. If you guys are healthcare data analytics who are struggling with raw data, this article will help you.


Optimizing data analytics’ effectiveness and value isn’t an easy task for healthcare leaders because most of them don’t access to the proper tools. As an experienced and advanced healthcare data analyst, I used to deal with various difficulties and obstacles when managing and analyzing tons of data to give out certain useful results.

Data Analytics

After a long time of working hard and researching continuously to look for a satisfactory answer, I assume that the root of my mistake was not using the suitable tools to analyze data and find out insights which could speed up care and process enhancement initiatives. Hence, in this article, I will show you four secrets that I sum up in many years to transform your raw data into meaningful results.

Key stages of transforming raw data

Before learning strategies to turn raw data into meaningful analytics, let’s go through some basic knowledge on 3 key stages that all data analytics have to understand thoroughly.

- Data capture

Data capture is the most important stage and decides greatly on whether the output result is trustworthy or not. The way devices, people, and processes produce and capture data are in charge of the data’s appropriateness (did data analysts capture the right data?), ease of data extraction (whether data is picked up in an accessible way or not) and also discreteness (did they capture data in the proper format?).

- Data provisioning

Analysts are in need of data from various source systems through the organization to generate meaningful insights. For instance, an analyst helping clinicians team on the quality improvement issue requires a load of data from numerous source systems: EMR data, cost data, patient satisfaction data as well as billing data.

Combining data manually and pulling them into one location, in a mutual format and making sure datasets are interacting with each other is impossible and highly time – consuming. Also, it makes data more liable to errors. There are more efficient and fast ways to gather data.

- Data analysis

After capturing proper data and pulling it into the appropriate place, the data analysis begins.
Data quality evaluation: Data analytics have to take a lot of time and effort to evaluate the data. Plus, they have to note their way of evaluation in case they share their results with the audience.

+ Data discovery: It is another pivotal component of professional data analysis. Before answering a particular question, analysts tend to explore the data and to search for meaningful trends and phenomenon. From my observation and experience, acted – upon analyses accounts for at least 50 percent in the discovery process.

+ Interpretation: When it comes to analyzing data, interpretation step comes up to most people’s head, but in fact, it is the smallest step in the whole process (about the total time that analysts spend on it)

+ Presentation: This is also a vital step as a data analysis couldn’t be recognized and appraise highly if the analyst couldn’t explain the result in an easy – to – understand and simple way.

These three stages of analyzing data will drive improvements. However, it isn’t adequate to generate meaningful and sustainable healthcare analytics. It is equally important to concentrate on analyzing data, not just picking up and provisioning data.

Optimize your data analytics’ value with 4 simple ways

Empowering data analysts to furnish insights necessary to make value-added improvements:

1. A data warehouse

The most efficient way to encourage analysts to drive improvements is by carrying out an enterprise data warehouse (EDW). The EDW becomes a stop shop for data aggregation. Analysts could access into all data through the health system by the login.

Several people assume that EDW is waste as it can put data together manually on a basis. This may sound acceptable in theory, but in fact, EDW offers various critical attributes such as security, common linkable identifiers, metadata, and auditing.

2. Full access to a testing environment

Keeping a tight rein on analysts’ access to EDW can vastly restrict their effectiveness. Give analysts plentiful opportunity to build and rebuild data sets. Analysts should be proficient in using the data warehouse in which they could store everything they consider useful.

3. Data discovery tools

Data discovery tools are as important as business intelligence tools, make it easy and simple for analysts to investigate the data and search for meaningful oddities or trends. However, BI tools are adequate for depth data analysis. BI tools feature graphs and charts that help analysts understand what the data is expressing. But they are still important to help analysts drill into the data, find trends and useful correlations. The proper data discovery tool should make it possible for analysts to generate insightful and intertwined reports that drive system improvements.

4. Direction

Healthcare data analysts are in need of direction, not detailed step – by – step guidelines about what the reports contain. Detailed instructions lead to one-off reports which are linked to very exact requests. On the other hand, direction results in deeper and more useful insights that could solve problems and drive improvements. A high – quality report requires providing sufficient direction to keep the analysts on the right track and enough flexibility to boost analysts to ask and exploit more questions.

Providing analysts with the right direction, enough time to shed light on the problem and a forum to ask more detailed questions is also needed. The final product will be much better as it includes both what the requester initially required and extra insights when going deeper into the data – which could be exactly what the requester needs.


In conclusion, analyzing data is not an easy task, and there is a lot of knowledge that analysts have to put their effort and time into. So keep calm and learning, I’m pretty sure that you guys will be excellent data analytics in the near future.

Source: www.xomo.net

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