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Trial Title:
Effectiveness of a Decision Support System in Improving the Diagnosis and Screening Rate of Breast Cancer
NCT ID:
NCT01336257
Condition:
Breast Cancer
Conditions: Official terms:
Breast Neoplasms
Conditions: Keywords:
Decision Support Techniques
Electronic Health Records
Reminder Systems
Early Detection of Cancer
Mammography
Women
Study type:
Interventional
Study phase:
N/A
Overall status:
Unknown status
Study design:
Allocation:
Randomized
Intervention model:
Single Group Assignment
Primary purpose:
Diagnostic
Masking:
None (Open Label)
Intervention:
Intervention type:
Other
Intervention name:
SEBASTIAN Clinical Decision Support System (CDSS)
Description:
SEBASTIAN is an example of a clinical decision support technology that supports the
latest, service-based architectural approach to CDSS implementation. Developed at Duke
University, SEBASTIAN is a clinical decision support Web service whose interface is now
the basis of the HL7 Decision Support Service draft standard SEBASTIAN places a
standardized interface in front of clinical decision support knowledge modules and makes
only limited demands on how relevant patient data are collected or on how decision
support inferences are communicated to end-users
Arm group label:
Electronic Reminder
Summary:
Clinical decision support has been shown to improve the performance of screening tests;
however, few studies have documented direct clinical benefit resulting from the increased
screening promoted by clinical decision support systems.
The purpose of this study was to determine if a standards-based, sophisticated decision
support system could not only promote additional breast cancer screening, but also detect
significantly more breast cancer
Detailed description:
Breast cancer is the most common female cancer. In the United States, the second most
common cause of cancer death in women, and the main cause of death in women ages 45 to 55
years old. The U.S. Preventive Services Task Force recommends screening mammography, with
or without clinical breast examination, every one to two years among women aged 50 to 69
years old.
Recent research has shown that health care delivered in industrialized nations often
falls short of optimal, evidence based care. US adults receive only about half of
recommended care. To address these deficiencies in care, health-care organizations are
increasingly turning to clinical decision support systems. A clinical decision-support
system is any computer program designed to help health-care professionals to make
clinical decisions. In a sense, any computer system that deals with clinical data or
knowledge is intended to provide decision support.
Examples include manual or computer based systems that attach care reminders to the
charts of patients needing specific preventive care services and computerized physician
order entry systems that provide patient-specific recommendations as part of the order
entry process. Such systems have been shown to improve prescribing practices, reduce
serious medication errors, enhance the delivery of preventive care services, and improve
adherence to recommended care standards.
The aim of this study is to show the efficacy of a decision-support system as a strategy
for improving the performance of the mammography care process and the detection of
significantly more breast cancer.
Criteria for eligibility:
Criteria:
Inclusion Criteria:
- Women between 50 and 69 years old
Exclusion Criteria:
- Breast Neoplasms
- Bilateral mastectomy
- Disabled Persons
Gender:
Female
Minimum age:
50 Years
Maximum age:
69 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Hospital Italiano de Buenos Aires
Address:
City:
Buenos Aires
Zip:
1209
Country:
Argentina
Status:
Recruiting
Contact:
Last name:
Ana M Gomez, MD
Phone:
541149590200
Phone ext:
5398
Email:
anamaria.gomez@hospitalitaliano.org.ar
Start date:
November 2009
Completion date:
June 2012
Lead sponsor:
Agency:
Hospital Italiano de Buenos Aires
Agency class:
Other
Collaborator:
Agency:
Duke University
Agency class:
Other
Source:
Hospital Italiano de Buenos Aires
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT01336257
