Provider-Line Ancillary Service Support: A Study of Performance and Cost Data

Abstract

Under the Department of Defense (DoD) Coordinated Care Program (CCP), reliable cost data become more important than ever. Decentralized cost accountability rationalizes the need for a single framework for average and total costs, and increases the need to know provider-line costs. It becomes appropriate to ask, how accurate are the data on which cost analyses rest? This study framed this question in light of wide disparity in reported ancillary service performance data and increasing Partnership provider productivity at Darnall Army Community Hospital (DACH). The subject of this study was the distribution of ancillary service data for MEPRS summary accounts with multiprovider lines; and the accuracy of cost analyses based on these data. Ancillary service requests from a clinical department were audited for provider- line and procedure data (raw count and weighted value) for one reporting period. Provider-line ancillary service performance distribution, based on this audit, was compared to the distribution reflected in the comparable MEPRS summary account. Average ancillary service cost were computed based on the results of the tabulation. The delta between average ancillary service cost based on ancillary service data summaries and average costs derived from the audited service requests was reported.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1992
Accession Number
ADA261661

Entities

People

  • Bonnie M. Murdock

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Blood
  • Blood Banks
  • Chemistry
  • Cost Analysis
  • Databases
  • Delivery Of Health Care
  • Department Of Defense
  • Frequency
  • Health Care
  • Health Services
  • Hospitals
  • Information Systems
  • Medical Personnel
  • Military Medicine
  • Personnel Management
  • Therapy
  • Women'S Health

Fields of Study

  • Medicine

Readers

  • Life Cycle Cost Analysis
  • Medical or Health Care Field.
  • Regression Analysis.