Healthcare Analytics

Data Aggregation and the Single Source of Healthcare Analytics Truth

Break Out of Silos

In the healthcare industry, data is everywhere. It is frequently fragmented among a variety of information systems, such as electronic health records, laboratory systems, radiology systems, adjudicated claims, spreadsheets, databases, and more that may or may not be compatible. Data ownership is often siloed at the team or departmental level, which hinders an organization's ability to standardize or validate it. In this scenario, it's easy to pick and choose data to provide support for a variety of viewpoints. But this method of "data shopping" provides no strategic insight into what's really happening within the organization and prevents leadership from making data-driven decisions.

Data aggregation is the first step toward solving this problem and creating a "single source of truth" that can be used to strategically drive decision making across the organization. Moving data to the enterprise level is necessary for predictive modeling or any other healthcare data analytics effort.

Move Data Aggregation to the Enterprise Level

  • Define standards for data sources and elements
  • Establish ownership of data quality management outside of IT
  • Streamline and automate data collection and reporting efforts
  • Optimize the use of existing IT, vendor, and technology resources
  • Choose a platform that can manage a variety of data sources
  • Build out analytics as an organizational resource, not a departmental resource
  • Use a data governance in healthcare approach that provides multi-disciplinary ownership over data, tools, and processes
An enterprise-level, cross-functional team approach to healthcare analytics

Use Data Aggregation to Break the Language Barrier Between Roles

A side effect of siloed or compartmentalized data is the lack of standardized definitions across an organization. If one department measures newborn weight in kilograms and another uses pounds, the data can't easily be compared or used outside of the department.

For a healthcare network with multiple hospitals, length of stay is a powerful example. A nurse manager in a given hospital may define length of stay as the actual number of days each patient is in the hospital. Finance, on the other hand, is more likely to determine length of stay by calculation. At first this may seem like a subtle distinction. However, length of stay for hospitals with a maternity ward is likely to be lower because most newborns are discharged quickly. Facilities with a trauma or psychiatric service line will skew higher with no consideration for the type of patient being treated. Data aggregation allows for standardization across the enterprise that leads to insight into the big picture.

Are you ready to take your data to the enterprise level? Learn how healthcare analytics software can help you aggregate health data.

Next: Data Governance in Healthcare

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