Effectiveness of a Decision Support System in Improving the Diagnosis and Screening Rate of Breast Cancer

NCT ID: NCT01336257

Last Updated: 2012-04-17

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 2200 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2009-11-30
• Study Completion Date: 2012-06-30

Brief 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.

Conditions

Breast Cancer

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Electronic Reminder

alert from SEBASTIAN decision support system

Group Type: EXPERIMENTAL

SEBASTIAN Clinical Decision Support System (CDSS)

Intervention Type: OTHER

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

control

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

SEBASTIAN Clinical Decision Support System (CDSS)

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

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Women between 50 and 69 years old

Exclusion Criteria

• Breast Neoplasms
• Bilateral mastectomy
• Disabled Persons

• Minimum Eligible Age: 50 Years
• Maximum Eligible Age: 69 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Duke University

OTHER

Sponsor Role: collaborator

Hospital Italiano de Buenos Aires

OTHER

Sponsor Role: lead

Responsible Party

Damian Borbolla

MD

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Damian A Borbolla, MD

Role: PRINCIPAL_INVESTIGATOR

Hospital Italiano de Buenos Aires

Locations

Hospital Italiano de Buenos Aires

Buenos Aires, Buenos Aires, Argentina

Site Status: RECRUITING

Countries

Argentina

Facility Contacts

Ana M Gomez, MD

Role: primary

anamaria.gomez@hospitalitaliano.org.ar

541149590200 ext. 5398

Other Identifiers

HIBA00019

Identifier Type: -

Identifier Source: org_study_id