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Generating recipient-centered explanations about drug prescription

B De Carolis¹, F de Rosis, F Grasso

¹Dipartimento di Informatica, Università di Bari, Italy.

Artificial Intelligence in Medicine

|May 1, 1996

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This study developed an Explanation Generator to create tailored drug prescription explanations for indirect users, improving information delivery beyond the direct prescriber. The system adapts content and style for better patient understanding of medication information.

Area of Science:

Medical Informatics
Natural Language Generation
Human-Computer Interaction

Background:

Knowledge-based systems in drug prescription generate explanations for users.
Indirect users, distinct from prescribers, are key recipients of these explanations.
Understanding indirect users' information needs and physician attitudes is crucial for effective explanation generation.

Purpose of the Study:

To describe the generation of written explanations for indirect users of a drug prescription knowledge-based system.
To design an Explanation Generator that addresses the specific needs of indirect users.
To adapt explanation content, order, and style based on the intended indirect user.

Main Methods:

Utilized studies on indirect users' information needs and physicians' explanatory attitudes.

Related Experiment Videos

Integrated text planning techniques with ATN-based (Augmented Transition Network) surface generation.

Implemented a double modeling component for adaptive explanation delivery.

Main Results:

Successfully generated written explanations for indirect users in the drug prescription domain.
Demonstrated the ability to adapt explanation content, order, and style.
Provided examples of computer-generated texts for analysis.

Conclusions:

The developed Explanation Generator offers a method for tailoring explanations to indirect users.
The approach has potential advantages in communicating complex medical information.
Further analysis is needed to fully understand the advantages and limitations of this method.