Overcoming Silos in Clinical Data

The conclusions healthcare companies are able to draw from quality data can influence decisions about patient care and have the potential to help improve the health of community populations.

But there are some obstacles in the way of harnessing the wealth of data found in electronic health records (EHRs).

  • Quality of data—Whether the data collected is not the kind that can yield actionable insights or if the information collected is out of date or from an unknown source, quality can be an issue. Analytics can only be as good as the information being mined.
  • Documentation problems—Gaps in patient data and human error can result in poor documentation that cannot yield helpful insights.
  • Strict regulations—Patient privacy and security are paramount, which means the healthcare industry is faced with attempting to gather and share data while also respecting strict regulations, like HIPAA.
  • Data silos—One of the major obstacles to quality clinical analytics is trapped data. When information is coming from a variety of sources, including clinical, financial and operational, those systems often cannot communicate with one another, or files are stored in incompatible formats that make reading reports difficult.


Data Governance Tools to Beat Silos

Data silos can be overcome with the help of a well-defined data governance system. The ability to collect, store and share healthcare data in a secure way paves the way for clinical analytics programs to provide the kind of insights that improve health, drive down cost and reduce errors.

Learn more about how to start a data governance initiative to promote insights in our white paper.


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