Predict Hospital-Acquired Infections and Conditions

Description: Hospitals can leverage predictive analytics to identify patients likely to be at high risk of infections or complications.

Problem

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.

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Problem Size

• Recent studies show that between 5% and 10% of hospitalized patients in Latin America develop at least one HAC during their stay [2].
• These conditions generate significant additional costs and extend hospital stays by an average of 7 to 10 days [3].
• In Mexico, HACs contribute to 15% of avoidable hospital deaths, highlighting the urgency of implementing effective preventive strategies [4].

Solution

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.

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Opportunity Cost

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].

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Impact

• A significant reduction in HAC incidence, estimated at 40% in the first year of use [2].
• Decreased hospital costs by optimizing the use of financial resources by an average of 20%.
• Improved patient safety, fostering greater trust in healthcare services.
• Increased clinical staff satisfaction due to the availability of technological tools that assist in risk prevention.

Data Sources

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.

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Citations

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1. Pan American Health Organization. (2020). Hospital Infection Control: A Guide for Infection Prevention. Retrieved from https://iris.paho.org/bitstream/handle/10665.2/51545/ControlInfecHospitalarias_spa.pdf
2. Redalyc. (2023). The Impact of Hospital Infections in Latin America. Retrieved from https://www.redalyc.org/journal/843/84359527011/84359527011.pdf
3. Revista Virtual CIR. (2023). HACs in Mexican Hospitals: Analysis and Strategies. Retrieved from https://webcir.org/revistavirtual/12_2023/pdf/mexicoAnales/1_anales_es.pdf
4. ‍UniCórdoba. (2020). Study on Hospital-Acquired Conditions. Retrieved from https://repositorio.unicordoba.edu.co/server/api/core/bitstreams/475d81d4-d642-4fe5-9506-8dad2712f03d/content
5. ‍Sankaran, Roshun, et al. “A Comparison of Estimated Cost Savings from Potential Reductions in Hospital-Acquired Conditions to Levied Penalties under the CMS Hospital-Acquired Condition Reduction Program.” The Joint Commission Journal on Quality and Patient Safety, vol. 46, no. 8, Aug. 2020, pp. 438-447, doi:https://doi.org/10.1016/ijcjq.2020.05.002. Accessed 2 Mar. 2021.

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Video

https://www.tella.tv/video/clx0f4m3m01gm09jxeb94e2j1