Related Experiment Video

Updated: Sep 24, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Using diagnosis-related groups for studying variations in hospital admissions

N P Roos, J E Wennberg, K McPherson

    Health Care Financing Review
    |February 3, 1989
    PubMed

    Insights

    Diagnosis-Related Groups (DRGs) can classify hospital admissions by analyzing admission rate variations. This method consistently categorizes DRGs across different regions, revealing discretionary admission patterns.

    Area of Science:

    • Health Services Research
    • Hospital Administration
    • Healthcare Policy

    Background:

    • Diagnosis-Related Groups (DRGs) traditionally focus on hospital resource consumption.
    • DRGs also serve as a classification system for hospital admissions.
    • Understanding variations in admission rates is crucial for healthcare management.

    Purpose of the Study:

    • To demonstrate how consistent variations in admission rates can classify DRG categories.
    • To introduce and apply the Index of Discretionary Admissions.
    • To analyze the consistency of admission rate variations across different US states.

    Main Methods:

    • Utilized modified DRG categories for analysis.
    • Examined admission rate variations across hospital service areas.
    • Calculated the proportion of discretionary admissions using the Index of Discretionary Admissions.

    Main Results:

    • High consistency in admission rate variations was observed for modified DRG categories.
    • Analysis spanned multiple states: Iowa, California, Massachusetts, and Maine.
    • The proportion of highly discretionary hospital admissions varied by state, from 14% in California to 22% in Iowa.

    Conclusions:

    • Consistent patterns in admission rate variations can effectively classify DRG categories.
    • The Index of Discretionary Admissions provides a useful metric for understanding healthcare utilization.
    • State-level variations in discretionary admissions highlight potential areas for healthcare policy intervention.

    More Related Videos

    Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index06:55

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

    Published on: January 8, 2020

    Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools11:29

    Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

    Published on: June 20, 2020

    Related Concept Videos

    Genome-wide Association Studies-GWAS01:11

    Genome-wide Association Studies-GWAS

    Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
    GWAS does not require the identification of the target gene involved in...
    Formulating and Validating Nursing Diagnosis I01:26

    Formulating and Validating Nursing Diagnosis I

    A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
    There are thirteen domains for...
    Formulating and Validating Nursing Diagnosis II01:25

    Formulating and Validating Nursing Diagnosis II

    Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
    Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
    Documentation of Nursing Diagnosis01:10

    Documentation of Nursing Diagnosis

    The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
    In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
    Methods of Documentation VI: Case Management Model01:15

    Methods of Documentation VI: Case Management Model

    The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
    For example, a patient with a chronic illness...
    Comparing the Survival Analysis of Two or More Groups01:20

    Comparing the Survival Analysis of Two or More Groups

    Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...