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Related Experiment Video

Updated: May 5, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Technology diffusion in hospitals: a log odds random effects regression model.

Jos L T Blank^1,2, Vivian G Valdmanis^1,3

¹Institute for Public Sector Efficiency Studies, Delft University of Technology, Delft, The Netherlands.

The International Journal of Health Planning and Management

|December 11, 2013

Summary

This summary is machine-generated.

Hospital innovation diffusion is influenced by size, competition, and commitment to innovation. Establishing an innovation office can improve technology adoption and efficiency.

Keywords:

diffusion hospitals log odds regression model technology

Related Experiment Videos

Last Updated: May 5, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Health Services Research
Innovation Management
Health Economics

Background:

Understanding factors influencing the adoption of new technologies in hospitals is crucial for improving healthcare delivery.
Previous research has explored various determinants of innovation diffusion, but a comprehensive analysis using micro-level hospital data is needed.

Purpose of the Study:

To identify key factors affecting the diffusion of innovations within Dutch general hospitals.
To analyze the relationship between hospital characteristics and the adoption of new technologies.

Main Methods:

Application of a log odds random effects regression model on hospital micro data.
Analysis of data from 60 Dutch general hospitals between 1995 and 2002.
Distinguishing between service, physician, environmental, financial, and organizational determinants.

Main Results:

Hospital size, market competition, and a commitment to innovation positively influence the diffusion of innovations.
The study confirms existing research on determinants of innovation diffusion.
External effects of demand and market competition are critical for policy development.

Conclusions:

A direct relationship exists between identified determinants and the diffusion of hospital innovations.
Policies aimed at diffusing innovations should consider external market factors for efficient technology use.
Establishing an dedicated innovation office is recommended for individual hospitals to foster adoption.