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Evidence Based Decision Making: Integrating Clinical Prediction Rules (iCPR and EHR)

Primary Purpose

Strep Pharyngitis, Pneumonia

Status
Completed
Phase
Not Applicable
Locations
United States
Study Type
Interventional
Intervention
Integrated Clinical Prediction Rule (iCPR)
Sponsored by
Northwell Health
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional health services research trial for Strep Pharyngitis focused on measuring Clinical Prediction Rules, Electronic Health Records, Walsh Clinical Prediction Rule, Heckerling Clinical Prediction Rule

Eligibility Criteria

undefined - undefined (Child, Adult, Older Adult)All SexesAccepts Healthy Volunteers

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

Sites / Locations

  • Mount Sinai School of Medicine

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

No Intervention

Arm Label

iCPR randomized providers

Control providers

Arm Description

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.

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).

Outcomes

Primary Outcome Measures

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.

Secondary Outcome Measures

Full Information

First Posted
June 28, 2011
Last Updated
October 3, 2012
Sponsor
Northwell Health
Collaborators
Icahn School of Medicine at Mount Sinai, Agency for Healthcare Research and Quality (AHRQ)
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1. Study Identification

Unique Protocol Identification Number
NCT01386047
Brief Title
Evidence Based Decision Making: Integrating Clinical Prediction Rules
Acronym
iCPR and EHR
Official Title
Evidence Based Decision Making: Integrating Clinical Prediction Rules Into Electronic Health Records
Study Type
Interventional

2. Study Status

Record Verification Date
October 2012
Overall Recruitment Status
Completed
Study Start Date
August 2010 (undefined)
Primary Completion Date
January 2012 (Actual)
Study Completion Date
July 2012 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Northwell Health
Collaborators
Icahn School of Medicine at Mount Sinai, Agency for Healthcare Research and Quality (AHRQ)

4. Oversight

Data Monitoring Committee
No

5. Study Description

Brief Summary
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
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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Strep Pharyngitis, Pneumonia
Keywords
Clinical Prediction Rules, Electronic Health Records, Walsh Clinical Prediction Rule, Heckerling Clinical Prediction Rule

7. Study Design

Primary Purpose
Health Services Research
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
Outcomes Assessor
Allocation
Randomized
Enrollment
168 (Actual)

8. Arms, Groups, and Interventions

Arm Title
iCPR randomized providers
Arm Type
Experimental
Arm Description
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.
Arm Title
Control providers
Arm Type
No Intervention
Arm Description
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).
Intervention Type
Other
Intervention Name(s)
Integrated Clinical Prediction Rule (iCPR)
Intervention Description
Integrated clinical prediction rule for Strep Pharyngitis based on Walsh clinical prediction rule (CPR) criteria and rule for Pneumonia based on Hecklering CPR criteria.
Primary Outcome Measure Information:
Title
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.
Description
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.
Time Frame
Comparisons between case and control ordering will be measured after a year of using the EMR tool

10. Eligibility

Sex
All
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
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
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Thomas M McGinn, MD, MPH
Organizational Affiliation
Northwell Health
Official's Role
Principal Investigator
Facility Information:
Facility Name
Mount Sinai School of Medicine
City
New York
State/Province
New York
ZIP/Postal Code
10029
Country
United States

12. IPD Sharing Statement

Citations:
PubMed Identifier
23896675
Citation
McGinn TG, McCullagh L, Kannry J, Knaus M, Sofianou A, Wisnivesky JP, Mann DM. Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial. JAMA Intern Med. 2013 Sep 23;173(17):1584-91. doi: 10.1001/jamainternmed.2013.8980.
Results Reference
derived
PubMed Identifier
21929769
Citation
Mann DM, Kannry JL, Edonyabo D, Li AC, Arciniega J, Stulman J, Romero L, Wisnivesky J, Adler R, McGinn TG. Rationale, design, and implementation protocol of an electronic health record integrated clinical prediction rule (iCPR) randomized trial in primary care. Implement Sci. 2011 Sep 19;6:109. doi: 10.1186/1748-5908-6-109.
Results Reference
derived

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Evidence Based Decision Making: Integrating Clinical Prediction Rules

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