Analytics Explained: Importance of Healthcare Analytics

Understanding healthcare analytics is important in order to fully take advantage of what it has to order. For this reason and more, healthcare organization management must always have Analytics Explained to all level of employees. This way, they can understand the importance of analytics and work together as an organization to achieve their goals. Below we discuss some importance of analytics.

Analytics Helps with Prevention

With the availability of analytics, specifically big data analytics, healthcare organizations can track the flow of data collected from patient health devices and from hospitals as well. This will help them see and study any trends in healthcare and then take precautionary actions to prevent issues. It also means they can see what they are doing right and do more of it thus helping the healthcare industry increase the health of the population. If this is not convincing enough, top healthcare companies have noticed an increased in the Return on Investment (ROI) after they applied analytics data and big data analytics to their decision making. Thanks to technology and increased number of health gadgets and health wearable devices, data can be gotten from so many places such as hospital databases and from the pedometer of it’s patients.


Return on Investment (ROI) is a performance measure used to evaluate the efficiency of an investment or compare the efficiency of a number of different investments. ROI tries to directly measure the amount of return on an particular investment, relative to the investment’s cost. To calculate ROI, the benefit or return of an investment is divided by the cost of the investment. The result is expressed as a percentage or a ratio.



Analytics Helps with Faster and More Accurate Diagnosis

Analytics and more specifically, big data analytics helps doctors and medical practitioners to make more accurate and faster diagnosis. They can leverage the large volume of healthcare data and derive useful knowledge and insight from past data and use that to make current diagnosis. When data is studied and analyzed, it can be easy to see patterns in the data and these patterns serves as useful tool for insight into new health conditions or new patients. In addition, doctors can from analytics, know the complete health history of a patient thus helping them provide better care. A national pool of all health data can help governmental public health organizations such as the Center for Disease Control (CDC) prevent  outbreaks, predict possible outbreaks, and treat any outbreaks that might occur to stop it from spreading.

Analytics Helps with Fraud Detection

Fraud has a way of taking advantage of existing systems and finding ways to cut corners without anyone noticing it. With analytics and big data, fraudulent acts that normally fly under the radar will be easily identified and stopped. With analytics, healthcare organizations can safeguard their health information, health data, prescriptions, and all other valuable resources. This can help prevent the loss of funds or valuable information in a healthcare organization.

Important Members of a Healthcare Analytics Team

A healthcare analytics team is in charge of handling all analytics issues in a healthcare organization and making sure the data is available for use and used in the right way or ways. The Healthcare analytics team comprises of the following

The Chief Analytics Officer

This is the leader of the analytics team and is usually a senior manager in the healthcare organization. They are usually has a vast knowledge of the workings of analytics and have solid background in applied mathematics, engineering, science, physics and any other related field. They usually have years of experience in data applications, data analysis, programing, and data simulation. They are also very versed in programing languages such as Python, R, and  Matlab and can use data visualization tools such as Excel perfectly. The chief analytics officer position is a very important position because besides their role of understanding the data and how to use it, they are also responsible for leading the data analytics team and helping the team members make sense of the data for the benefit of whole healthcare organization.

Analytics Explained

With big data comes big data problems and as such the chief analyst officer must guide the team during data crisis or issues so the problems can be solved in a  timely and proper fashion. The position of Chief analyst officer should not be mistaken for the Chief Information Officer (CIO). Chief analyst officer is tasks specifically with leading a team and making sure all the data needs are met. The Chief Information officer is in charge of a broader set of tasks that are relevant to all information and technology needs for the healthcare organization. The role if the chief analyst officer has gotten more and more important in recent times. As healthcare organizations begin to rely more on big data and on technology, healthcare analytics analytics will play a substantial role in clinical patient management, identification and support for best practices in guidelines, and supporting the end-user (doctors, nurses, clinicians etc) in making better decisions.

The Data ScientistAnalytics Explained

The data scientist is also an important member of the analytics team in any organization. They are responsible for collecting data and making the data useful for various purposes within the organization. A data scientist must have good problem solving skills because they will be the first point of contact with most if not all data problems. The data scientist has expertise in data visualization tools, programing languages and other types of analytics. They also need to be able to work in a  team and communicate effectively. Effective communication means being able to break down the data in a way that other members of the organization will understand and find useful. A Data scientist usually has several degrees (including advanced degrees) in computer science, computer programing, coding languages, mathematics, engineering, and statistics.