Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support

Area of Science:

• Clinical decision support
• Health informatics
• Medical predictive modeling

Background:

• Clinicians face challenges selecting from numerous clinical predictive tools due to a lack of standardized comparative effectiveness assessments.
• Most available predictive tools lack implementation data and comparative effectiveness evaluations.

Purpose of the Study:

• To develop a conceptual framework, GRASP (Grade and Assess Predictive tools), for standardized, evidence-based grading of clinical predictive tools.
• To assist clinicians in selecting efficient predictive tools for clinical practice and guideline recommendations.

Main Methods:

• A literature review identified criteria for evaluating predictive tools.
• The GRASP framework was developed and applied to grade five tools: LACE Index, Centor Score, Well's Criteria, Modified Early Warning Score, and Ottawa knee rule.
• Expert review and feedback were incorporated to refine the framework and tool gradings.

Main Results:

• The GRASP framework grades tools based on evaluation phase, level of evidence, and direction of evidence.
• The Ottawa knee rule received the highest grade due to demonstrated positive post-implementation impact.
• The LACE Index received the lowest grade, showing only pre-implementation positive predictive performance.

Conclusions:

• The GRASP framework offers a standardized, evidence-based method for assessing predictive tools, considering pre-implementation, implementation, and post-implementation impact.
• An online GRASP platform could provide clinicians and guideline developers with structured evidence for tool selection.
• Maintaining GRASP's currency necessitates semi-automated methods for updating assessments with new evidence.