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Hospitals can leverage predictive analytics to identify patients likely to be at high risk for undesirable complications.
Hospital-acquired conditions (HACs), which are complications or medical conditions not present at the time of hospital admission, are a significant issue in the healthcare system. Annually, approximately 2.5 million HACs occur among inpatients over the age of 18 in the U.S., leading to substantial financial penalties for hospitals under the Hospital-Acquired Conditions Reduction Program, amounting to around $360 million each year (1)(2). Hospital-acquired infections (HAIs) constitute a major portion of these HACs and are among the leading causes of death in the country, with one in every 31 hospitalized patients having an HAI at any given time. This results in approximately 680,000 HAIs annually in U.S. acute care hospitals, nearly 70,000 of which are fatal (3)(4). HAIs contribute to a staggering $28 to $33 billion in potentially preventable healthcare expenditures each year (5). Additionally, patients with HAIs are at increased risk for sepsis, the leading cause of inpatient death and readmissions, affecting at least 1.7 million adults and resulting in nearly 270,000 deaths annually. Sepsis is the most expensive condition treated in the U.S., with healthcare costs exceeding $60 billion annually (6)(7)(8).
"HACPredict AI" is a predictive algorithm developed to mitigate the occurrence of HACs in hospitalized patients worldwide. By evaluating comprehensive data, including patient demographics, health status, and treatment regimens, HACPredict AI assesses the potential for a patient to develop a HAC during their hospital stay, thus supporting healthcare providers in improving patient outcomes and reducing the financial burden on health systems across the globe.
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 (1), estimates of cost savings by Sankaran et al. (2), and prevention strategies from the National HAI Action Plan (3). In-depth study of infections and sepsis, with findings from Magill et al. (5) and the CDC (7), shaped the development of accurate models for HAC risk prediction.
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Hospital-acquired conditions (HACs), which are complications or medical conditions not present at the time of hospital admission, are a significant issue in the healthcare system. Annually, approximately 2.5 million HACs occur among inpatients over the age of 18 in the U.S., leading to substantial financial penalties for hospitals under the Hospital-Acquired Conditions Reduction Program, amounting to around $360 million each year (1)(2). Hospital-acquired infections (HAIs) constitute a major portion of these HACs and are among the leading causes of death in the country, with one in every 31 hospitalized patients having an HAI at any given time. This results in approximately 680,000 HAIs annually in U.S. acute care hospitals, nearly 70,000 of which are fatal (3)(4). HAIs contribute to a staggering $28 to $33 billion in potentially preventable healthcare expenditures each year (5). Additionally, patients with HAIs are at increased risk for sepsis, the leading cause of inpatient death and readmissions, affecting at least 1.7 million adults and resulting in nearly 270,000 deaths annually. Sepsis is the most expensive condition treated in the U.S., with healthcare costs exceeding $60 billion annually (6)(7)(8).
"HACPredict AI" is a predictive algorithm developed to mitigate the occurrence of HACs in hospitalized patients worldwide. By evaluating comprehensive data, including patient demographics, health status, and treatment regimens, HACPredict AI assesses the potential for a patient to develop a HAC during their hospital stay, thus supporting healthcare providers in improving patient outcomes and reducing the financial burden on health systems across the globe.
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 (1), estimates of cost savings by Sankaran et al. (2), and prevention strategies from the National HAI Action Plan (3). In-depth study of infections and sepsis, with findings from Magill et al. (5) and the CDC (7), shaped the development of accurate models for HAC risk prediction.
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