Consideration for Assessing Data/Models/Tools Expiration Supporting Drug Development and Clinical Decision Making.

Area of Science:

• Biomedical Informatics
• Data Science
• Drug Development

Background:

• Decision-making relies heavily on data, models, and tools.
• Data is evaluated within a specific context of use (COU).
• Implicit assumptions about data quality and relevance may not always hold true.

Purpose of the Study:

• To explore factors impacting data information value over time.
• To postulate occasions where data value diminishes, necessitating reassessment.
• To consider data expiration and its impact on decision-making.

Main Methods:

• Conceptual analysis of data value over time.
• Examination of drug development as a case study.
• Identification of factors influencing data relevance and information value.

Main Results:

• Data relevance and information value are not static and can degrade over time.
• Drug development presents scenarios where data value diminishes.
• Periodic review and condition reassessment are necessary for data utility.

Conclusions:

• Data expiration and time-dependent status changes should be considered for decision-making.
• Patient privacy, consent, and regulatory compliance are critical factors for ongoing data use.
• Understanding data information value dynamics is essential for both drug development and clinical decision-making.