Negative Binomials Regression Model in Analysis of Wait Time at Hospital Emergency Department
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields



Publication Date Range:


Document Data


Document Type:






Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page


Negative Binomials Regression Model in Analysis of Wait Time at Hospital Emergency Department

Filetype[PDF-392.76 KB]

  • English

  • Details:

    • Alternative Title:
      Proc Am Stat Assoc
    • Description:
      Wait time is the differences between the time a patient arrives in the emergency department (ED) and the time an ED provider examines that patient. This study focuses on the development of a negative binomial model to examine factors associated with ED wait time using the National Hospital Ambulatory Medical Care Survey (NHAMCS). Conducted by National Center for Health Statistics (NCHS), NHAMCS has been gathering, analyzing, and disseminating information annually about visits made for medical care to hospital outpatient department and EDs since 1992. To analyze ED wait times, a negative binomial model was fit to the ED visit data using publically released micro data from the 2009 NHAMCS. In this model, the wait time is the dependent variable while hospital, patient, and visit characteristics are the independent variables. Wait time was collapsed into discrete values representing 15 minutes intervals. The findings are presented.
    • Pubmed ID:
    • Pubmed Central ID:
    • Document Type:
    • Collection(s):
    • Main Document Checksum:
    • File Type:

    You May Also Like

    Checkout today's featured content at