Predicting Hospital Admissions With Poisson Regression Analysis

Abstract

In this thesis, Poisson regression is used to predict and analyze inpatient hospital admissions for two inpatient units (Four East and Four West) at Naval Medical Center San Diego. Data that include age group, gender, beneficiary category, enrollment site and fiscal month are collected for the patient population. This information is used along with additional details about past admissions such as the location and source of admission. These data are next fit to four different models that correspond to Four East (enrolled and un-enrolled beneficiaries) and Four West (enrolled and un-enrolled beneficiaries). Stepwise selection techniques are used to arrive at final models. The final models are used to observe trends in predicted hospital admissions based on trends in current population sizes.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2009
Accession Number
ADA501543

Entities

People

  • Lisa A. White

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Age Groups
  • Data Science
  • Department Of Defense
  • Health Care
  • Health Services
  • Hospitals
  • Information Science
  • Linear Regression Analysis
  • Medical Personnel
  • Military Hospitals
  • Military Medicine
  • Operations Research
  • Regression Analysis
  • Statistical Analysis
  • Statistical Distributions
  • Therapy
  • United States

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