Decrease Hospital Spending: There's an App for That! A Retrospective Analysis of Implementation of a Mobile Resident Handbook on Hospital Costs and Disposition

Telemed J E Health. 2017 Oct;23(10):828-832. doi: 10.1089/tmj.2016.0257. Epub 2017 May 10.

Abstract

Background: Patient care involves time sensitive decisions. Matching a patient's presenting condition with possible diagnoses requires proper assessment and diagnostic tests. Timely access to necessary information leads to improved patient care, better outcomes, and decreased costs.

Introduction: This study evaluated objective outcomes of the implementation of a novel Resident Handbook Application (RHAP) for smart phones.

Methods: The RHAP included tools necessary to make proper assessments and to order appropriate tests. The RHAPs effectiveness was accessed using the Military Health System Military Mart database. This database includes patient specific aggregate data, including diagnosis, patient demographics, itemized cost, hospital days, and disposition status. Multivariable analysis was used to compare before and after RHAP implementation, controlling for patient demographics and diagnosis. Internal medicine admission data were used as a control group.

Results: There was a statistically significant decrease in laboratory costs and a strong trend toward statistically significant decreases in the cost of radiology performed after implementation of RHAP (p value of <0.02 and <0.07, respectively). There was also a decrease in hospital days (3.66-3.30 days), in total cost per admission ($18,866-$16,305), and in cost per hospital day per patient ($5,140-$4,936). During the same time period a Control group had no change or increases in these areas.

Conclusions: The use of the RHAP resulted in decreases in costs in a variety of areas and a decrease in hospital bed days without any apparent negative effect upon patient outcomes or disposition status.

Keywords: business administration/economics; e-health; m-health; military medicine; telemedicine.

MeSH terms

  • Adult
  • Age Factors
  • Consumer Behavior
  • Female
  • Hospital Costs / statistics & numerical data*
  • Humans
  • Internship and Residency / methods*
  • Length of Stay / statistics & numerical data
  • Male
  • Middle Aged
  • Mobile Applications / economics*
  • Practice Guidelines as Topic
  • Retrospective Studies
  • Sex Factors
  • Socioeconomic Factors
  • Time Factors