Evidence Based Decision Making: Integrating Clinical Prediction Rules

Study Overview
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Brief Summary
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Clinical prediction rules (CPRs) are frontline decision aids that help physicians make evidence-based, cost-effective decisions that benefit their patients. The aims of this project are to incorporate two well validated CPRs (Streptococcal Pharyngitis Prediction Rule and the Pneumonia Clinical Prediction Rule) into an outpatient Electronic Medical Record System (EMR) and to perform a randomized controlled trial of the effectiveness of integrated CPRs impact on doctor's behaviors (e.g. test ordering and medication prescribing).

Detailed Description
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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.

Official Title
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Evidence Based Decision Making: Integrating Clinical Prediction Rules Into Electronic Health Records

Conditions
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Strep Pharyngitis, Pneumonia

Intervention / Treatment
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* Other: Integrated Clinical Prediction Rule (iCPR)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Providers who are part of Mount Sinai's Division of General Internal Medicine Exclusion Criteria: Not a provider at Mount Sinai's Division of General Internal Medicine
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: iCPR randomized providers<br>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)<br>* Integrated clinical prediction rule for Strep Pharyngitis based on Walsh clinical prediction rule (CPR) criteria and rule for Pneumonia based on Hecklering CPR criteria.<br>|
| No Intervention: Control providers<br>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). | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 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. | Comparisons between case and control ordering will be measured after a year of using the EMR tool | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Clinical Prediction Rules, Electronic Health Records, Walsh Clinical Prediction Rule, Heckerling Clinical Prediction Rule