Hospital Acquired Conditions

Machine Learning Model Estimates Optimal Treatment Timing for Sepsis

April 10, 2023 - Researchers from Ohio State University (OSU) have developed a machine learning (ML) model that can accurately estimate optimal treatment timing for sepsis cases and support clinical decision-making, according to a study published last week in Nature Machine Intelligence. Sepsis is a life-threatening condition that can rapidly lead to tissue damage,...

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Johns Hopkins Machine-Learning Tools Predict Risk of ICU Delirium

by Shania Kennedy

Johns Hopkins University researchers have developed machine-learning (ML) algorithms that can detect the early warning signs of delirium and predict which patients will be at high risk of delirium at...

Geisinger Chosen as Finalist in CMS Artificial Intelligence Challenge

by Jessica Kent

CMS has selected Geisinger as one of seven finalists in its Artificial Intelligence Health Outcomes Challenge. In collaboration with Medial EarlySign, the health system used artificial intelligence...

Risk Scores Predict Patients Likely to Develop Multiple Infections

by Jessica Kent

Certain risk scores already used to evaluate the severity of a trauma patient’s condition can also help providers determine which patients are highly likely to develop multiple infections,...

Geisinger Advances in CMS Artificial Intelligence Challenge

by Jessica Kent

CMS has selected Geisinger and EarlySign, a machine learning company, for their joint proposal in the agency’s Artificial Intelligence Health Outcomes Challenge. The partnering organizations are...

Predictive Analytics Model Detects Sepsis in Pediatric ED Patients

by Jessica Kent

A predictive analytics algorithm was able to accurately identify children at high risk of developing septic shock in the emergency department (ED), according to a study published in The Journal of...

Philips, DoD Build Machine Learning System to Detect Infection

by Jessica Kent

Royal Philips, in collaboration with the Defense Threat Reduction Agency (DTRA) and Defense Innovation Unit (DIU) of the US Department of Defense (DoD), are building a machine learning algorithm that...

Satisfaction High with Epic, Cerner Clinical Surveillance Tools

by Jennifer Bresnick

Clinical surveillance tools are generally doing a good job of alerting providers to sudden patient downturns and delivering meaningful decision support, yet optimizing these health IT tools to produce...

Predicting Pressure Injuries with Machine Learning, EHR Data

by Jessica Kent

Fueled by EHR data, machine learning tools have shown potential in improving several areas of care delivery, including sepsis prediction, chronic disease management, and cancer detection. As providers...

Predictive Analytics, EHR Big Data Reduce Sepsis Mortality by 18%

by Jessica Kent

At North Oaks Health System in Hammond, Louisiana, researchers have used big data from the Epic electronic health record (EHR) to develop a predictive analytics tool that has reduced sepsis mortality...

71% of Veterans Affairs Hospitals Improve on Care Quality

by Jennifer Bresnick

Nearly three-quarters of Veterans Affairs hospitals have improved on care quality and certain patient outcomes measures, according to the latest data from the VA’s Strategic Analytics for...

CMS Efforts to Reduce Hospital-Acquired Conditions Save $2.9B

by Jessica Kent

The Agency for Healthcare Research and Quality (AHRQ) has released a report showing that CMS-led patient safety initiatives have reduced hospital-acquired conditions, helping to prevent an estimated...

751 Orgs Get Medicare Penalties for Hospital Acquired Conditions

by Jennifer Bresnick

Medicare has penalized 751 of the nation’s hospitals and health systems for high rates of hospital acquired conditions (HACs) such as preventable falls, infections, and pressure...

FDA Antibiotic Stewardship Tool Offers Centralized Data Resource

by Jessica Kent

The FDA has launched a new antibiotic stewardship system that will serve as a centralized data resource for antimicrobial drugs while providing physicians with the most up-to-date information to...

EHR Analytics Track C. Diff Patients to Flag Infection Trends

by Jennifer Bresnick

Using electronic health record (EHR) data to track the movements of Clostridium difficile (C. diff) patients throughout the hospital can help to highlight the risks of infection for future...

HHS: Patient Safety Efforts Save 125K Lives, $28B in Spending

by Jennifer Bresnick

Hospital-acquired conditions (HACs) are down and patient safety is continuing its steady rise as healthcare organizations keep pushing forward with quality improvement programs, many of which originated...

$347M in CMS Grants to Improve Patient Safety, Care Quality

by Nathan Boroyan

The Centers for Medicare and Medicaid Services (CMS) is investing $347 million to improve patient safety by reducing the number of hospital-acquired conditions and preventable readmissions among...

Big Data Shows Gender-Based Medical Error, Patient Safety Patterns

by Jennifer Bresnick

Big data analytics tools are helping researchers at the Pennsylvania Patient Safety Authority (PSA) unearth patient safety patterns hidden within 2.5 million reports on medical errors and near-miss...

Antibiotic Stewardship, Patient Safety Plans Get Industry Support

by Jennifer Bresnick

A proposed CMS rule to require antibiotic stewardship programs and improved patient safety techniques in hospitals is receiving widespread industry support.  The Pew Charitable Trusts, as well as...

EHR Adoption May Not Reduce Patient Safety, But Does It Help?

by Jennifer Bresnick

The lengthy, convoluted process of industry-wide electronic health record (EHR) adoption has produced a large number of concerns about how this new technology will impact patient safety, especially in...

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