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Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

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Radiology order entry with decision support: initial clinical experience.

Daniel I Rosenthal¹, Jeffrey B Weilburg, Thomas Schultz

¹Department of Radiology, Massachusetts General Hospital, Harvard University School of Medicine, Boston, MA 02114, USA. dirosenthal@partners.org

Journal of the American College of Radiology : JACR

|April 7, 2007

Summary

A computerized order entry system for radiology was clinically accepted and improved ordering practices. This system, incorporating decision support, reduced low-utility imaging examinations by 66%.

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Area of Science:

Radiology Informatics
Clinical Decision Support Systems
Health Information Technology

Background:

Computerized order entry systems are crucial for modern healthcare delivery.
Optimizing diagnostic imaging utilization is essential for cost-effectiveness and patient care.
Clinical acceptance and impact on physician behavior are key metrics for health IT system success.

Purpose of the Study:

To evaluate the clinical acceptance of a designed computerized order entry system for radiology.
To determine if the system could effectively influence physician ordering practices.
To assess the impact of decision support tools on imaging utilization.

Main Methods:

An intranet-based outpatient ordering and scheduling system was implemented in 2001.
Standardized indications and minimized keystrokes were used, with decision support added in 2004.
Utility scores were assigned to examination requests, with physician performance tracked and reviewed.

Main Results:

The system achieved 75% adoption for outpatient studies.
The percentage of low-utility examinations decreased from 6% to 2% after decision support implementation.
The greatest improvements were observed in primary care physicians and those using the system directly.

Conclusions:

Computerized order entry systems with integrated decision support are clinically accepted by physicians.
Such systems can significantly impact and improve radiology ordering practices.
The study demonstrates a successful model for enhancing diagnostic imaging efficiency.