The cost of inappropriate admissions: a study of health benefits and resource utilization in a department of internal medicine

J Intern Med. 1999 Oct;246(4):379-87. doi: 10.1046/j.1365-2796.1999.00526.x.


Objectives: High rates of inappropriate hospital admissions have been found in numerous studies, suggesting that a high percentage of hospital resources are, in effect, wasted. The degree to which this is true depends on how costly inappropriate admissions are compared to other admissions. This study aimed to estimate both the percentage and cost of inappropriate admissions.

Setting: Department of internal medicine at a teaching hospital.

Subjects: Consecutively admitted patients during a six-week study period.

Main outcome measures: Assessments of inappropriateness were based on estimates of health benefit and necessary care level. These estimates were made by expert panels using a structured consensus method. Health benefit was estimated as gain in quality-adjusted life years, or degree of short-term improvement in quality of life during or shortly after the hospital stay. The direct costs to the hospital of each stay were estimated by allocating the costs of labour, 'hotel' and overhead according to length of stay and adding to this the cost of ancillary resources used by each individual patient.

Results: A total of 422 admissions were included. The 102 (24%) judged to be inappropriate had a lower mean cost (US$ 2532) than the other 320 (US$ 5800) (difference 3268; 95% confidence interval 1025-5511). The inappropriate admissions accounted for 12% of the total costs.

Conclusions: Denying care for inappropriate admissions does not generate cost reductions of the same magnitude. Policy makers should be cautious in projecting the cost savings potential of excluding inappropriate admissions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cost Savings
  • Denmark
  • Health Services Misuse / economics*
  • Hospital Costs
  • Hospital Departments / economics*
  • Hospital Departments / statistics & numerical data
  • Hospitals, University / economics
  • Humans
  • Internal Medicine / economics*
  • Linear Models
  • Patient Admission / economics*