Identifying high-risk patients with predictive analytics

NorthShore University HealthSystem's emergency departments leverage data and predictive analytics to help it determine which chest pain patients should be admitted for observation and which patients can be sent home.

One of the more challenging aspects of emergency department (ED) healthcare is determining which patients to admit for observation and which patients to send home. One healthcare system is tackling that challenge with predictive analytics.

Unnecessary hospitalizations cause numerous problems: longer wait times, a lack of beds for the patients that really need them, wasted time of emergency medical staff, not to mention costs borne by patients, hospitals and insurers. On the other hand, failing to admit patients that really do need care can have deadly consequences.

NorthShore University HealthSystem, which operates four hospitals in Illinois, is leveraging data and predictive analytics to address that challenge. The project, dubbed 'Technology-driven Chest Pain Management in the ED,' won NorthShore a CIO 100 Award in IT Excellence.

An evidence-based approach

Chest pain is the most common reason that ED staff elects to keep patients for observation in NorthShore's emergency departments. But prior to 2017, NorthShore did not have an evidence-based practice for determining whether a chest pain patient should be admitted. Chest pain could be an indicator of a heart attack, but it could also be a symptom of something far less serious, like heartburn.

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