- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT02645357
Implementing an Evidence-based Computerized Decision Support System Linked to Electronic Health Records to Improve Care for Cancer Patients (ONCO-CODES)
Effect on Professional Practice and Health Care Outcomes of Computerized Evidence-based Information System and Clinical Decision Support: A Randomized Controlled Trial in Orthopaedics (GR-2009-1606736)
Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients.
The ONCO-CODES (Computerized DEcision Support in ONCOlogy) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio Study Duration 12 month Study Center(s) Single-center
Objectives:
The primary outcome of this trial is a process outcome. i.e. the rate at which the issues reported by the reminders are resolved (resolution rates).
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients.
Short Title/Acronym ONCO-CODES Protocol Code IRST100.23
Rationale:
Computerized decision support systems (CDSSs) are computer programs that provide clinicians, staff, patients, or other individuals with person-specific, actionable recommendations or management options that are intelligently filtered or presented at appropriate times to enhance health and health care. CDSSs might be integrated with patient electronic health records (EHRs) and evidence-based knowledge. The Investigators designed a pragmatic randomized controlled trial to evaluate the effectiveness of patient-specific, point-of-care reminders generated by the Medilogy Decision Support System (MediDSS) on clinical practice and the quality of care in the Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS. The Investigators hypothesize that MediDSS reminders can increase clinician adherence to guidelines and, eventually, improve the quality of care offered to hospitalized patient. The adoption of CDSSs is likely to increase across healthcare systems due to growing concerns about the quality of medical care and discrepancy between real and ideal practice, continuous calls for a meaningful use of health information technology, and the increasing use of and familiarity with advanced technology among new generations of physicians.
Study Design:
The ONCO-CODES (Computerized DEcision Support in ONCOlogy) trial is a pragmatic, parallel group, randomized controlled study with 1:1 allocation ratio Study Duration 12 month Study Center(s) Single-center
Objectives:
The primary outcome of this trial is a process outcome. i.e. the rate at which the issues reported by the reminders are resolved (resolution rates).
Number of Subjects:
The investigators calculated the sample size on the basis of the primary outcome. A sample of 1,704 reminders will be necessary to detect the difference between the two groups (power = 0.90; α =0.05, two-sided; 1:1 allocation). Since estimates for intracluster correlation are not available, Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients.
Diagnosis and Main Inclusion Criteria:
The investigators will include all the patients admitted to the facilities of the IRST IRCCS. There are no exclusion criteria
Statistical Methodology:
All analyses will follow the intention-to-treat principle: patients will be analyzed in the group to which they have been randomized. Descriptive statistics will be presented.
All statistical tests will be two-sided. The investigators will use the Stata software to perform all statistical analyses (Stata Corp., College Station, TX, USA).The investigators increased the required sample size (by 20%) to 2,046 reminders to account for clustering by patient.
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
-
-
-
Meldola, Italy, 47014
- Oncologia Medica, IRST IRCCS, Meldola
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- The Investigators will include all the EHRs of patients admitted to the facilities of the IRST IRCCS.
Exclusion Criteria:
- There are no exclusion criteria.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Health Services Research
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
---|---|
Experimental: computer reminders on clinical practice
on-screen, point-of-care computer reminders on clinical practice.
|
on-screen, point-of-care computer reminders on clinical practice.
|
Other: Control group
No on-screen, point-of-care computer reminders on clinical practice.
|
control group
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
resolution rates
Time Frame: 12 months
|
the rate at which the issues reported by the reminders are resolved
|
12 months
|
Collaborators and Investigators
Collaborators
Investigators
- Principal Investigator: Alessandro Passardi, MD, Irst Irccs
Publications and helpful links
Study record dates
Study Major Dates
Study Start
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimate)
Study Record Updates
Last Update Posted (Estimate)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- IRST100.23
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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