Antimicrobial treatment imprecision: an outcome-based model to close the data-to-action loop

Area of Science:

• Public Health
• Health Policy
• Infectious Disease Management

Background:

• Antimicrobial resistance (AMR) poses a significant global threat to healthcare systems and society.
• Inappropriate antimicrobial treatment is a key driver of AMR.
• Current frameworks for antimicrobial stewardship lack outcome-based measures to quantify treatment-related damage.

Purpose of the Study:

• To introduce "imprecision," a novel, mathematically grounded, outcome-based metric for assessing antimicrobial treatment appropriateness.
• To propose a framework for utilizing this metric to enhance antimicrobial stewardship interventions.
• To guide policymakers and healthcare leaders in developing more effective strategies against AMR.

Main Methods:

• Development of a new metric, "imprecision," based on mathematical principles and outcome measurement.
• Outlining a policy and healthcare leadership framework for implementing the imprecision metric.
• Leveraging learning antimicrobial systems, public engagement, implementation science, AI, and regulatory reform.

Main Results:

• The proposed "imprecision" metric offers a tangible, outcome-based measure for antimicrobial treatment appropriateness.
• The framework facilitates the development of more effective antimicrobial stewardship interventions.
• Implementation is supported by AI, public engagement, and updated regulations.

Conclusions:

• Adoption of the "imprecision" metric can lead to more sustainable patterns of antimicrobial development, regulation, and prescribing.
• AI-augmented measures of antimicrobial treatment imprecision can inform future patient pathways and drug development.
• Addressing practical, ethical, and regulatory considerations is crucial for successful implementation.