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 Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

289
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
289
Relative Risk01:12

Relative Risk

151
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
151
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

138
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
138
Hazard Ratio01:12

Hazard Ratio

114
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
114

You might also read

Related Articles

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

Sort by
Same author

Real-Time Versus Video-Recorded Step Count Validation Among Young Adults.

Journal for the measurement of physical behaviour·2026
Same author

Risk-Adjusted Excess Length of Stay for Patients With Heart Failure Across Facilities: A Large US Cohort Study.

Journal of the American Heart Association·2026
Same author

Applying the PRECEDE-PROCEED model to develop MommaConnect: a digital healthcare platform for addressing postpartum depression and improving infant well-being.

Exploration of neuroscience·2024
Same author

A Novel Method for Assessing Risk-Adjusted Diagnostic Coding Specificity for Depression Using a U.S. Cohort of over One Million Patients.

Diagnostics (Basel, Switzerland)·2024
Same author

The Association of eHealth Literacy Skills and mHealth Application Use Among US Adults With Obesity: Analysis of Health Information National Trends Survey Data.

JMIR mHealth and uHealth·2024
Same author

Factors Associated with Suicide Risk Behavior Outcomes Among Black High School Adolescents.

Journal of community health·2023

Related Experiment Video

Updated: Jun 25, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

A Data-Driven Approach to Defining Risk-Adjusted Coding Specificity Metrics for a Large U.S. Dementia Patient Cohort.

Kaylla Richardson1,2, Sankari Penumaka2, Jaleesa Smoot1,2

  • 1Department of Public Health Sciences, University of North Carolina at Charlotte (UNC Charlotte), Charlotte, NC 28223, USA.

Healthcare (Basel, Switzerland)
|May 24, 2024
PubMed
Summary
This summary is machine-generated.

Accurate medical coding is crucial for patient care and reimbursement. This study developed a data-driven model to assess dementia diagnosis coding specificity in claims data, identifying facilities needing improvement.

Keywords:
ICD-10coding specificitydementia

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K

Related Experiment Videos

Last Updated: Jun 25, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K

Area of Science:

  • Health Informatics
  • Medical Economics
  • Public Health

Background:

  • Medical coding precision directly influences patient care quality, healthcare reimbursement, and system reliability.
  • Inconsistent or inadequate coding specificity presents significant administrative and patient-level challenges.
  • Existing clinical metrics for assessing medical practices are often logistically infeasible at a population level, especially with claims-only data.

Purpose of the Study:

  • To develop and validate a data-driven approach for assessing medical coding specificity, particularly for dementia diagnoses.
  • To identify factors influencing coding specificity using administrative claims data.
  • To create a benchmark metric for healthcare facilities to evaluate and enhance their coding practices.

Main Methods:

  • Utilized a large all-payor administrative claims dataset of 487,775 dementia hospitalization records from 2022.
  • Employed logistic regression models incorporating patient and facility characteristics to analyze dementia diagnosis coding specificity.
  • Developed a two-step approach with a Poisson binomial model to identify facilities with over- or under-specified dementia diagnoses relative to industry standards.

Main Results:

  • Identified multiple significant factors associated with dementia coding specificity, particularly for principal diagnoses (AUC = 0.727).
  • Developed a novel risk-adjusted metric to benchmark coding specificity across healthcare facilities.
  • Demonstrated the practical application of the metric through facility-level analysis and geospatial mapping across the U.S.

Conclusions:

  • The developed data-driven metric provides a valuable tool for healthcare facilities to assess and improve dementia coding specificity.
  • Enhancing coding specificity aligns with healthcare industry standards, potentially improving patient care quality and healthcare system reliability.
  • This approach offers a feasible method for evaluating coding practices in large, non-centralized healthcare systems using administrative claims data.