KPN Health – The Importance of Big Data in Healthcare


The DFW Hospital Council is posting blogs submitted by our Associate Members. The following was provided by KPN Health, Inc. For guidelines, please contact Chris Wilson at

Blog by Don Navarro, Chairman, KPN Health and David Hultsman, Chief Technology Strategist, KPN Health

Big Data is not a new concept. It is a better marketing “word” to describe the increasingly vast amount of data that is available for consumption by an authorized consumer. The challenge is making the data accessible, accurate, secure and available only to the authorized consumer while being presented in a manner that presents insight into the need or questions asked. Data stored in source systems, data marts, enterprise data warehouses or other storage formats is rarely useful on its own. Just like any raw material, data must be processed to be useful. This processing is how data starts to become the information and insight needed to understand and impact the operations of a healthcare organization.

When a quality improvement specialist, executive or clinician asks for data, the request is rarely for just data. Requests for data typically result from a need to better understand a problem, identify quality and performance issues or evaluate patient outcomes and the effects of quality improvement activities.

How can we turn untapped data into meaningful insight that enables better administrative and clinical decision making? The first step in understanding an organization and its processes is knowing the data itself, its context and how it relates to the business. To provide meaningful insights that can begin to help decision makers, the analytical results must use data that accurately reflects the status of patients and the performance quality associated with clinical and business process workflows. It’s often said in healthcare settings that “you can’t manage what you can’t measure.” Organizations need to ensure that the data being worked with is an accurate reflection of what they are measuring.

In what kind of system is the data kept, such as an enterprise data warehouse or other format? How is the data physically stored on the database? Is the data type stored by integer, character or date? How might that storage format constrict what can be done with the data? At the database level, the data type that’s assigned to a field controls what kind of information can be stored in that field, such as numbers, words, character strings or a selection of menu choices. This helps to ensure the integrity of stored data so that when the data is read back from the database, the software knows how to interpret.

Regardless of how data might be physically stored in a database, it’s important to know what value the
data represents. This knowledge allows for meaningful analysis of the data. If the type of analysis performed is not appropriate for the data type or what the data represents, the results will more than likely be nonsensical and a waste of valuable time. Given the type of data and storage, discretion must be carefully applied when deciding the kind of database manipulations and mathematical operations that are to be performed so the results are worthwhile.

Critical to any useful analytics is an understanding of what clinical or business problems decision makers need to solve. With the availability of large volumes of data, and relatively inexpensive computing power that can perform deep data analysis, there’s a temptation to take the “shotgun approach” and unleash all available tests and analysis on a data set. Not to discourage this; such data explorations can reveal insight, uncover unknown relationships in data and satisfy intellectual curiosity.

The result of analysis must be information that drives decision making and enables clinicians, administrators and quality improvement stakeholders to take appropriate action to achieve the goals and objectives of the organization. The Data Scientist of today is always remarking, “Make sure you answer the question.” Healthcare organizations are generating and using unprecedented volumes and varieties of data. Despite advances in data collection, management, analysis and insight-generation, basic principles about data analysis still (and will always) apply: know what data you have, know what it means, know what you can do with it and be sure to answer the original question.