Challenges Providers Face Obtaining and Sharing Data in Healthcare Today


Challenges Providers Face Obtaining and
Sharing Data in Healthcare Today
November 2018

Don Navarro, Executive Chairman
David Hultsman, Chief Technology & Information Officer

Sharing information relating to:  Encounter optimization, patient retention, and patient engagement are major areas of contention that impact healthcare organizations today.  Sharing data, connecting multiple disparate EHRs and Practice Management Systems, curating the aggregated data to high levels of accuracy, turning this data into actionable knowledge with accurate evidence for use or sharing by Providers is a major challenge for Value-Based Care.

Healthcare organizations need data (information) that is transformed into sharable, actionable knowledge to accurately and efficiently analyze, measure, and manage performance, that help providers and administrators deliver higher quality care at a lower cost.

The market is ripe with concepts and products to share data.  One way to share data is through this concept and products known collectively as Big Data.  Big Data is not a new concept.  It is a better marketing “word” to describe the increasing vast amount of data that is available for consumption by an authorized consumer.  The challenge is making the data accessible, sharable, 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, while sharable, is rarely useful without context.  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.

  1. What Should be Done with the Data?

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.   

  1. How is the Data Stored?

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 share ability and 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 it and it can be shared throughout the organization.

  1. What is the Data Type?

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 and shared.

  1. What is the Problem?

Critical to any useful analytics is an understanding of what clinical or business problems decision makers are coding with and 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 in a shared way.  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, data sharing, 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.

KPN, is a highly-respected healthcare advisory firm, delivering on these needs and providing deep knowledge and expertise in creating and supporting Value-Based Care networks and shared data that improve financial performance through increasing revenue, measuring and managing cost / risk for higher performance-with accurate evidence (example-increased CMS reimbursements).  We evaluate your capabilities in relation to your initiatives and deliver tailored solutions to achieve Value-Based Care objectives.  KPN is also an expert at supporting full risk commercial, Medicare and Medicare Advantage models.

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