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.

Public Health Relevance

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 effectivenss of integrated CPRs impact on doctor's behaviors (e.g. test ordering and medication prescribing).

Agency
National Institute of Health (NIH)
Institute
Agency for Healthcare Research and Quality (AHRQ)
Type
Research Demonstration and Dissemination Projects (R18)
Project #
7R18HS018491-03
Application #
8262455
Study Section
Health Care Technology and Decision Science (HTDS)
Program Officer
Randhawa, Gurvaneet
Project Start
2009-09-30
Project End
2012-07-31
Budget Start
2011-01-18
Budget End
2011-07-31
Support Year
3
Fiscal Year
2010
Total Cost
Indirect Cost
Name
Feinstein Institute for Medical Research
Department
Type
DUNS #
110565913
City
Manhasset
State
NY
Country
United States
Zip Code
11030
Kannry, Joseph; McCullagh, Lauren; Kushniruk, Andre et al. (2015) A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial. EGEMS (Wash DC) 3:1150
McCullagh, L J; Sofianou, A; Kannry, J et al. (2014) User centered clinical decision support tools: adoption across clinician training level. Appl Clin Inform 5:1015-25
McCullagh, Lauren; Mann, Devin; Rosen, Lisa et al. (2014) Longitudinal adoption rates of complex decision support tools in primary care. Evid Based Med 19:204-9
McGinn, Thomas G; McCullagh, Lauren; Kannry, Joseph et al. (2013) Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial. JAMA Intern Med 173:1584-91
Li, Alice C; Kannry, Joseph L; Kushniruk, Andre et al. (2012) Integrating usability testing and think-aloud protocol analysis with ""near-live"" clinical simulations in evaluating clinical decision support. Int J Med Inform 81:761-72
Mann, Devin M; Kannry, Joseph L; Edonyabo, Daniel et al. (2011) Rationale, design, and implementation protocol of an electronic health record integrated clinical prediction rule (iCPR) randomized trial in primary care. Implement Sci 6:109