.png){kind=logo}
Hospitals can leverage predictive analytics to identify patients likely to be at high risk of infections or complications.
Hospital-acquired conditions (HACs) present a significant challenge to patient safety, care quality, and healthcare system costs.
These conditions, including surgical site infections, ventilator-associated pneumonia, and deep vein thrombosis, affect hospitalized patients due to preventable complications [1].
The lack of early detection and inefficient use of hospital data contribute to the high prevalence of HACs.
The developed predictive algorithm analyzes comprehensive data, including patient demographics, health status, and treatment regimens, to identify patients at risk of developing HACs. Integrated into the hospital's information system:
- Detect patterns associated with HACs with high accuracy.
- Alert clinical staff about at-risk patients.
- Propose personalized interventions, such as treatment adjustments or increased monitoring.
Training of this model was guided by a synthetic data set generated from research including the Agency for Healthcare Research and Quality's national HAC scorecard, estimates of cost savings by Sankaran et al [5]., and prevention strategies from the National HAI Action Plan.
Hospital-acquired conditions (HACs) present a significant challenge to patient safety, care quality, and healthcare system costs.
These conditions, including surgical site infections, ventilator-associated pneumonia, and deep vein thrombosis, affect hospitalized patients due to preventable complications [1].
The lack of early detection and inefficient use of hospital data contribute to the high prevalence of HACs.
The developed predictive algorithm analyzes comprehensive data, including patient demographics, health status, and treatment regimens, to identify patients at risk of developing HACs. Integrated into the hospital's information system:
- Detect patterns associated with HACs with high accuracy.
- Alert clinical staff about at-risk patients.
- Propose personalized interventions, such as treatment adjustments or increased monitoring.
An estimated additional cost of $2,500 to $5,000 per patient due to extended hospital stays and increased treatment requirements [3].
The prevention and management of hospital infections alone consume up to 20% of a hospital's total operational budget in some regions [1].
Training of this model was guided by a synthetic data set generated from research including the Agency for Healthcare Research and Quality's national HAC scorecard, estimates of cost savings by Sankaran et al [5]., and prevention strategies from the National HAI Action Plan.
