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

A method for analyzing inpatient care variability through physicians' orders.

Matthew C Lenert1, Randolph A Miller2, Yevgeniy Vorobeychik3

  • 1Dept. of Biomedical Informatics, Vanderbilt University, 2525 West End Ave. Suite 1475, Nashville, TN 37203, USA.

Journal of Biomedical Informatics
|February 3, 2019
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

Loneliness, Anxiety Symptoms, Depressive Symptoms, and Suicidal Ideation in the All of Us Dataset.

JAMA network open·2026
Same author

Clinical Note-Extracted Psychosocial Factors for Predicting Suicide Attempt Among ED Patients With Suicidal Ideation.

JAMA network open·2026
Same author

Trends in unintentional injury death among post-9/11 Army Veterans who do and do not use Veteran Health Administration services.

Injury epidemiology·2026
Same author

Prospective Validation of a Suicide Event Risk Model in Transgender Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

Identifying and supporting trafficked individuals: provider and community organization perspectives on existing sociotechnical approaches.

Journal of the American Medical Informatics Association : JAMIA·2025
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026
Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026
See all related articles

Physician orders offer a better measure of care variability than cost variability, aiding standardization efforts. This scalable metric is calculable during patient care.

Area of Science:

  • Healthcare Management
  • Clinical Informatics
  • Health Services Research

Background:

  • Healthcare administrators use chart review or cost variability to guide care standardization.
  • Current methods like chart review are resource-intensive, and cost variability lacks precision.

Purpose of the Study:

  • To explore physician orders as a potential alternative metric for assessing care variability.
  • To compare the predictive power of order variability versus cost variability for length of stay.

Main Methods:

  • Developed an order variability metric using adult patient data from Vanderbilt University Hospital (2013-2016).
  • Compared cost variability and order variability models in predicting length of stay, adjusting for clinical covariates.
Keywords:
Care variabilityCost variabilityPhysician orders

Related Experiment Videos

Main Results:

  • The order variability model demonstrated superior performance, significantly minimizing the Akaike information criterion compared to the cost variability model.
  • This finding remained consistent even when excluding patients from intensive care units.

Conclusions:

  • Physician order variability serves as a more effective indicator of actual care variability than cost variability.
  • Order variability is a scalable and real-time calculable metric, suitable for ongoing care management and standardization initiatives.