Healthc Inform Res.  2013 Sep;19(3):215-221. 10.4258/hir.2013.19.3.215.

Effect of Health Information Technology Expenditure on Patient Level Cost

Affiliations
  • 1Health Policy and Management, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA. leejinh@gmail.com
  • 2Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN, USA.

Abstract


OBJECTIVES
This study investigate the effect of health information technology (IT) expenditure on individual patient-level cost using California Office of Statewide Health Planning and Development (OSHPD) data obtained from 2000 to 2007.
METHODS
We used a traditional cost function and applied hospital fixed effect and clustered error within hospitals.
RESULTS
We found that a quadratic function of IT expenditure best fit the data. The quadratic function in IT expenditure predicts a decrease in cost of up to US$1,550 of IT labor per bed, US$27,909 of IT capital per bed, and US$28,695 of all IT expenditure per bed. Moreover, we found that IT expenditure reduced costs more quickly in medical conditions than surgical diseases.
CONCLUSIONS
Interest in health IT is increasing more than ever before. Many studies examined the effect of health IT on hospital level cost. However, there have been few studies to examine the relationship between health IT expenditure and individual patient-level cost. We found that IT expenditure was associated with patient cost. In particular, we found a quadratic relationship between IT expenditure and patient-level cost. In other word, patient-level cost is non-linearly (or a polynomial of second-order degree) related to IT expenditure.

Keyword

Health Information Technology; Cost-to-Charge Ratio; Clustered Effect; Fixed Effect; Cost Function

MeSH Terms

California
Health Expenditures
Health Planning
Humans
Medical Informatics

Figure

  • Figure 1 Relationship between cost per inpatient stay and IT capital/IT labor/all IT expenditure.

  • Figure 2 Relationship between cost per inpatient stay and all IT expenditure by surgical and medical diagnosis related groups (DRGs).


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