Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Hospital readmission: predicting the risk.

R J Lagoe1, C M Noetscher, M P Murphy

  • 1St. Joseph's Hospital Health Center, Syracuse, New York, USA.

Journal of Nursing Care Quality
|July 17, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mitochondrial complex I activity in microglia sustains neuroinflammation.

Nature·2024
Same author

Mitochondrial reverse electron transport in myeloid cells perpetuates neuroinflammation.

bioRxiv : the preprint server for biology·2024
Same author

Chronic Fragmentation of the Daily Sleep-Wake Rhythm Increases Amyloid-beta Levels and Neuroinflammation in the 3xTg-AD Mouse Model of Alzheimer's Disease.

Neuroscience·2021
Same author

Mitochondria-targeted antioxidant MitoQ ameliorates ischaemia-reperfusion injury in kidney transplantation models.

The British journal of surgery·2021
Same author

Ex vivo normothermic perfusion of isolated segmental porcine bowel: a novel functional model of the small intestine.

BJS open·2021
Same author

Translatable mitochondria-targeted protection against programmed cardiovascular dysfunction.

Science advances·2020
Same journal

Blood Culture Contamination Reduction Using a Diversion Device in the Emergency Department.

Journal of nursing care quality·2026
Same journal

C. Differently: A Nurse-Driven Multidisciplinary Initiative to Overcome C. difficile's Challenges for Better Patient Outcomes.

Journal of nursing care quality·2026
Same journal

Enhancing Heart Failure Care Through Community Paramedic Education.

Journal of nursing care quality·2026
Same journal

Fostering Nurse Preceptor Preparedness: A Quality Improvement Project.

Journal of nursing care quality·2026
Same journal

Charting and Predicting Risk: Artificial Intelligence/Machine Learning Pilot Model for Hospital-Acquired Pressure Injuries.

Journal of nursing care quality·2026
Same journal

Making It Stick: The Science of Implementation.

Journal of nursing care quality·2026
See all related articles

This study identifies key clinical, utilization, and demographic variables to predict patient readmission risk. Analyzing these factors helps focus clinical management on outcomes and optimize resource allocation.

Area of Science:

  • Health Services Research
  • Clinical Informatics
  • Healthcare Management

Background:

  • Hospital readmissions represent a significant challenge in healthcare delivery.
  • Predicting patient readmission is crucial for effective resource allocation and patient outcome improvement.
  • Existing methods often lack a systematic approach to identify and combine predictive variables.

Purpose of the Study:

  • To develop and validate an approach for identifying specific variables that predict hospital readmission.
  • To create a method for assembling patient risk populations based on combinations of predictive variables.
  • To demonstrate the utility of risk analysis in optimizing clinical management and resource utilization.

Main Methods:

  • Utilized clinical, utilization, and demographic variables available in hospital databases.

Related Experiment Videos

  • Developed a process for identifying and comparing individual variables with the highest predictive power for readmission.
  • Implemented a procedure for creating risk populations by combining multiple predictive variables.

    • Main Results:

    • Identified specific variables with significant predictive power for patient readmission.
    • Successfully assembled distinct patient risk populations based on variable combinations.
    • Demonstrated that the risk analysis approach can effectively target clinical management efforts.

    Conclusions:

    • The proposed approach effectively predicts patient readmission likelihood using readily available data.
    • Risk analysis enables focused clinical management on patient outcomes, enhancing efficiency.
    • This methodology offers a valuable tool for healthcare providers to reduce readmissions and optimize resource use.