Expected value prioritization of prompts and reminders

Area of Science:

• Health Informatics
• Decision Analysis
• Preventive Medicine

Background:

• Computer-based prompting systems significantly improve preventive service delivery rates.
• The high volume of recommended preventive services exceeds practical clinical visit capacity.
• Prioritization of prompts is crucial for efficient healthcare delivery.

Purpose of the Study:

• To describe and evaluate two approaches for prioritizing preventive services prompts.
• To apply expected value decision making for optimizing prompt selection.
• To address the challenge of limited time in clinical settings for preventive care.

Main Methods:

• Developed a static, global prioritization method for preventive service prompts.
• Developed a dynamic prioritization method using influence diagrams and patient-specific data.
• Incorporated epidemiologic data and expert judgment into both prioritization models.
• Required compromises between normative processes and user satisfaction.

Main Results:

• A static prioritization system has been operational for nearly seven years.
• A dynamic, patient-specific prioritization method is currently under development.
• Both methods are labor-intensive, requiring significant data and expert input.
• User satisfaction necessitated deviations from strictly normative decision-making processes.

Conclusions:

• Prioritization strategies are vital for managing the complexity of preventive service delivery.
• Expected value decision making offers a framework for optimizing preventive care prompts.
• Balancing normative approaches with practical user needs is key to successful implementation.
• Further development of dynamic, patient-specific systems holds promise for enhanced preventive care.