Analysis of the Relationship Between Predictors of No-Show Appointment Behavior and the Benefit of Automated Patient Reminders

Abstract

Appointments that are not kept can potentially impact both the personal health status of the patient and the financial health of the Military Health care System. This report studied nine predictors of appointment non-adherence using both univariate and multivariate analyses to show which predictors have the greatest effect on patient appointment keeping behavior and the resultant benefit of automated telephone reminder technology. Univariate analysis revealed the following eight significant relationships with appointment keeping behavior: age, marital status, beneficiary category, Tricare Prime enrollment, proximity to the facility, branch of service, appointment day of the week and call to appointment interval. However, multivariate analysis revealed that age, beneficiary category, sponsors branch of service and marital status were the only variables that contributed to the statistical power of the predictive model, which produced an R2 value of 0.011 (p < .001). The study went on to find that implementation of an automated appointment reminder system yielded a statistically significant reduction in the overall clinic no-show rate. The reduction from 8.65% in FY00 to 7.60% in FY01 resulted in a X2 (1) = 7.24, p< .05. This finding demonstrates the usefulness of this technology as a means for improving overall clinic efficiency.

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Document Details

Document Type
Technical Report
Publication Date
May 13, 2002
Accession Number
ADA420913

Entities

People

  • Timothy G. O'haver

Organizations

  • Academy of Health Sciences

Tags

Communities of Interest

  • Biomedical
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Air Force
  • Data Science
  • Databases
  • Delivery Of Health Care
  • Department Of Defense
  • Family Medicine
  • Health Care
  • Health Services
  • Information Science
  • Medical Personnel
  • Military Medicine
  • Multivariate Analysis
  • Patient Care Management
  • Physicians
  • Predictive Modeling
  • Statistics
  • Therapy

Readers

  • Medical or Health Care Field.
  • Organizational Psychology.