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

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

Study Overview

• Status: Unknown
• Conditions: Breast Cancer
• Intervention / Treatment: Other: SEBASTIAN Clinical Decision Support System (CDSS)

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.

• Study Type: Interventional
• Enrollment (Anticipated): 2200
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Argentina
  • Buenos Aires, Argentina, 1209
    • Recruiting
    • Hospital Italiano de Buenos Aires
    • Contact: Ana M Gomez, MD; Phone Number: 5398 541149590200; Email: anamaria.gomez@hospitalitaliano.org.ar

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 50 years to 69 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: Female

Description

Inclusion Criteria:

• Women between 50 and 69 years old

Exclusion Criteria:

• Breast Neoplasms
• Bilateral mastectomy
• Disabled Persons

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: control
Experimental: Electronic Reminder; alert from SEBASTIAN decision support system	Other: 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of participants with new Breast Neoplasms Diagnosis (Incident cases)	New Breast Neoplasms Diagnosis (Incident cases from biopsy reports). Breast Neoplasms are stored in the institutional Clinical Data Repository. The diagnosis are automatically codified using a terminology server that use SNOMED-CT as reference terminology	18 month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of patient with breast cancer screening due that received the order to perform the study (mammography)	Number of patient with breast cancer screening due that received the order to perform the study (mammography). This information will be record through the clinical data repository every time that a provider order a screening test to each of the patients involve in the study	18 month

Collaborators and Investigators

• Sponsor: Hospital Italiano de Buenos Aires
• Collaborators: Duke University
• Investigators: Principal Investigator: Damian A Borbolla, MD, Hospital Italiano de Buenos Aires

Study record dates

Study Major Dates

• Study Start: November 1, 2009
• Primary Completion (Actual): June 1, 2011
• Study Completion (Anticipated): June 1, 2012

Study Registration Dates

• First Submitted: April 14, 2011
• First Submitted That Met QC Criteria: April 14, 2011
• First Posted (Estimate): April 15, 2011

Study Record Updates

• Last Update Posted (Estimate): April 17, 2012
• Last Update Submitted That Met QC Criteria: April 13, 2012
• Last Verified: April 1, 2012

More Information

Terms related to this study

• Keywords: Women; Mammography; Early Detection of Cancer; Decision Support Techniques; Electronic Health Records; Reminder Systems
• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms; Neoplasms by Site; Breast Diseases; Breast Neoplasms
• Other Study ID Numbers: HIBA00019