This scientific publication reveals the value of examining patients’ wound risk over time. Using real-world data, a study was conducted to improve the predictive accuracy of the Braden assessment for pressure injury risk in skilled nursing facilities. The time series analysis showed the most significant predictors of increased pressure injury risk were identified as a recent (within 21 days) decrease in Braden score, low subscores in nutrition, friction and activity, and a history of pressure injuries. The analysis included 62,253 in-house pressure injuries. 

Using demographic data with disaggregated Braden scores, Swift improved the accuracy of pressure injury risk assessment by 10.4%. This is one example of the massive potential to deliver more personalized wound care by leveraging AI to analyze data on a large scale to help clinicians prevent wounds. 

Read more: https://onlinelibrary.wiley.com/doi/10.1111/iwj.70000