Predicting Total Sick Days Experienced by an Active Duty Military Population

Abstract

This research analyzed the effects of Major Diagnostic Category Groups upon Total Sick Days associated with specific diagnosis for inpatients at Ireland Army Community Hospital. Data were collected from the Individual Patient Data System. Regression analysis was used to test the effects of MDCGs upon TSDs while controlling for the effects of time and gender. Results strongly suggest that MDCGs significantly affect TSDs (R squared = .57) while in the presence of time and gender controls, F(15, 255) = 22.17,p .001. These results indicate that MDCGs can be used to predict TSDs for an active duty military population with true validity and reliability. Military Treatment Facility Commanders can use this information to assess the current inpatient portion of health status for the military population the MTF serves. Additionally, Commanders can use the predictive power of this model in developing essential information for improving resource allocation decisions and organizational workload management.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 07, 1990
Accession Number
ADA238148

Entities

People

  • Thomas C. Clines

Organizations

  • Academy of Health Sciences

Tags

DTIC Thesaurus Topics

  • Active Duty
  • Chronic Diseases
  • Data Science
  • Databases
  • Delivery Of Health Care
  • Diseases And Disorders
  • Health Care
  • Health Services
  • Hospitals
  • Information Science
  • Information Systems
  • Knowledge Management
  • Medical Personnel
  • Regression Analysis
  • Statistical Analysis
  • Therapy
  • United States

Readers

  • Medical or Health Care Field.
  • Mental Health of Military Veterans with Posttraumatic Stress Disorder (PTSD): Risk Factors, Prevalence, Symptoms, and Treatment.
  • Oncology (Cancer Research).