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Evidence Based Decision Making: Integrating Clinical Prediction Rules

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

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT01386047
Acronym
iCPR and EHR
Enrollment
168
Registered
2011-06-30
Start date
2010-08-31
Completion date
2012-07-31
Last updated
2012-10-04

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pneumonia, Strep Pharyngitis

Keywords

Clinical Prediction Rules, Electronic Health Records, Walsh Clinical Prediction Rule, Heckerling Clinical Prediction Rule

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.

Interventions

OTHERIntegrated 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.

Sponsors

Icahn School of Medicine at Mount Sinai
CollaboratorOTHER
Agency for Healthcare Research and Quality (AHRQ)
CollaboratorFED
Northwell Health
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE (Outcomes Assessor)

Eligibility

Sex/Gender
ALL
Healthy volunteers
Yes

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

Design outcomes

Primary

MeasureTime frameDescription
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.Comparisons between case and control ordering will be measured after a year of using the EMR toolThe 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.

Countries

United States

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Mar 28, 2026