Evidence Based Decision Making: Integrating Clinical Prediction Rules (iCPR and EHR)
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|ClinicalTrials.gov Identifier: NCT01386047|
Recruitment Status : Completed
First Posted : June 30, 2011
Last Update Posted : October 4, 2012
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|Condition or disease||Intervention/treatment||Phase|
|Strep Pharyngitis Pneumonia||Other: Integrated Clinical Prediction Rule (iCPR)||Not Applicable|
Clinical prediction rules (CPRs) are frontline decision aids that help physicians make evidence-based, cost-effective decisions that benefit their patients. CPRs are proven tools that translate evidence into practice, increase quality while reducing costs, and can be used by physicians in a wide variety of clinical settings, such as primary care offices, emergency rooms, and hospitals. While many CPRs have been developed and validated over the years, health care providers have yet to incorporate them into everyday care.
CPRs aid providers in assessing the impact of individual components of a patient's history, physical examination, and basic lab results to estimate probability of disease or potential response to a treatment. Prediction rules use data that is readily available at the time of a patient encounter and often reduce unnecessary treatments and diagnostic testing. CPRs differ from reminder systems or alerts in that CPRs pull in aspects of the history and physical exam and in an evidence based fashion estimate probabilities, prognosis, or make treatment recommendations.
The goal of this study is to utilize patient electronic health records to incorporate CPRs into the face-to-face patient encounter. We propose to select certain clinical situations where well-validated CPRs are available and likely to be needed on a frequent basis. We will randomly assign an integrated CPR versus usual care into the point of care and evaluate the impact of this integration on doctor behavior and evidence-based decision making. Mount Sinai's Division of General Internal Medicine (DGIM) has significant experience with all aspects of CPRs, including derivation, validation, implementation, and systematic review. Furthermore, the Division has developed an interactive web library of CPRs for clinical use that is one of the most widely sites of its kind. We propose to collaborate with Epic, one of the nation's largest and most respected electronic medical record (EMR) companies, to integrate validated CPRs into EMRs and assess the impact on provider behavior and patient care.
|Study Type :||Interventional (Clinical Trial)|
|Actual Enrollment :||168 participants|
|Intervention Model:||Parallel Assignment|
|Masking:||Single (Outcomes Assessor)|
|Primary Purpose:||Health Services Research|
|Official Title:||Evidence Based Decision Making: Integrating Clinical Prediction Rules Into Electronic Health Records|
|Study Start Date :||August 2010|
|Actual Primary Completion Date :||January 2012|
|Actual Study Completion Date :||July 2012|
Experimental: iCPR randomized providers
The physician population for the proposed study will comprise primary care providers (physicians, internal medicine residents, or licensed nurse practitioners; practicing in the outpatient primary care clinics at Mount Sinai Medical Center. The iCPR tool will automatically trigger for providers randomized into the iCPR intervention arm when they initiated an encounter for a patient that meets the criteria for possible evaluation of Strep Pharyngitis or Pneumonia.
Other: Integrated Clinical Prediction Rule (iCPR)
Integrated clinical prediction rule for Strep Pharyngitis based on Walsh clinical prediction rule (CPR) criteria and rule for Pneumonia based on Hecklering CPR criteria.
No Intervention: Control providers
The physician population for the proposed study will comprise primary care providers (physicians, internal medicine residents, or licensed nurse practitioners; practicing in the outpatient primary care clinics at Mount Sinai Medical Center. These providers will conduct visits for Strep Pharyngitis and Pneumonia in their manner (usual care).
- The primary outcome of this study will be focused on changes in doctor behavior and the comparison of the number of diagnostic tests ordered (chest x-rays) and antibiotics prescribed per patient encountered per diagnosis. [ Time Frame: Comparisons between case and control ordering will be measured after a year of using the EMR tool ]The data for the intervention and control groups will be compared for each of the two diagnostic areas. For example, for all patients presenting with URI symptoms or sore throat, data will be collected from Epic on the number of prescriptions for antibiotics written by providers randomized to the iCPR compared to usual-care arms, respectively. Among patients presenting with suspicion of pneumonia, the number of chest x-rays ordered and antibiotics prescribed at the clinical encounter will be determined.
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|Ages Eligible for Study:||Child, Adult, Older Adult|
|Sexes Eligible for Study:||All|
|Accepts Healthy Volunteers:||Yes|
- Providers who are part of Mount Sinai's Division of General Internal Medicine
- Not a provider at Mount Sinai's Division of General Internal Medicine
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01386047
|United States, New York|
|Mount Sinai School of Medicine|
|New York, New York, United States, 10029|
|Principal Investigator:||Thomas M McGinn, MD, MPH||Northwell Health|
|Responsible Party:||Thomas McGinn, Chair and Professor for the Hofstra North Shore-LIJ School of Medicine, Northwell Health|
|Other Study ID Numbers:||
5R18HS018491 ( U.S. AHRQ Grant/Contract )
|First Posted:||June 30, 2011 Key Record Dates|
|Last Update Posted:||October 4, 2012|
|Last Verified:||October 2012|
Clinical Prediction Rules
Electronic Health Records
Walsh Clinical Prediction Rule
Heckerling Clinical Prediction Rule
Respiratory Tract Infections
Respiratory Tract Diseases