HIT Think: Achieving optimal sepsis sensitivity and specificity with EMR surveillance

HIT Think: Achieving optimal sepsis sensitivity and specificity with EMR surveillance


Note: This is an excerpt from an article originally published in Health Data Management.

Sepsis accounts for half of hospital deaths, according to research published in the Journal of the American Medical Association. It’s the leading cause of readmissions, and more than $20 billion is spent on it annually.

According to the Centers for Disease Control and Prevention, sepsis has become a medical emergency. Every year there are at least 1.7 million cases of sepsis implicated in as many as 270,000 deaths, the CDC says.

Paramount to a successful sepsis detection solution is performance with regard to sensitivity and specificity. Sensitivity is the ability of a test to recognize true positives, while specificity measures the number of true negatives correctly identified. In a screening test for a potentially life-threatening disease such as severe sepsis, high sensitivity would be valued over high specificity.

The majority of healthcare delivery organizations look to leverage sepsis detection capabilities native to their EMR. Depending on the vendor, sepsis detection manifests itself within the EMR through specific modules, frameworks or tools, notification and alerting, surveillance dashboards, and in some cases, custom solutions through extensibility offered by the vendor. In some cases, users report the need to adopt in-house tools for their workflows. In addition, EMR users report alert fatigue with the system, potentially due to sensitivity issues.

Accurate triggering of clinical decision support will become increasingly important as clinical decision support is integrated into EMRs. Since decision support has the potential to interrupt the clinical workflow, every attempt should be made to ensure all eligible patients receive decision support (sensitivity), and that non-eligible patients are not mistakenly targeted (specificity) thus leading to alert fatigue.

Read the full article on Health Data Management.

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