Analysis of Patient Information: An Empirical Modeling Approach

Abstract

With rising costs and increasing complexities, many hospitals seek to better understand the intricate details of their operations. Increasingly, these organizations have a strong desire to accurately predict the resources required to effectively treat their patient load. This research investigates patient length-of-stay in a hospital neurological unit using an empirical modeling approach. Factors significantly affecting patient length of stay were identified and used to construct a regression model. The predictive model provides hospital decision makers with a compact tool to input what-if scenarios and predict future patient treatment lengths, thus, allowing the hospital to properly allocate resources.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2006
Accession Number
ADA446209

Entities

People

  • Tony A. Murphy

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Computer Programming
  • Data Analysis
  • Data Mining
  • Data Science
  • Health Care
  • Health Care Facilities
  • Health Services
  • Hospitals
  • Information Science
  • Medical Personnel
  • Nervous System
  • Peripheral Nervous System
  • Regression Analysis
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics

Fields of Study

  • Medicine

Readers

  • Computational Modeling and Simulation
  • Medical or Health Care Field.
  • Systems Analysis and Design