Evaluating Large Language Model–Supported Instructions for Medication Use: First Steps Toward a Comprehensive Model
This study demonstrates the potential of LLM-supported generation to produce prescription directions and improve communication between health professionals and patients within the e-prescribing system.
Authors
- Cristiane dos Santos Dias (ORCID: https://orcid.org/0000-0001-6559-3300)
- Gláucia Miranda Varella Pereira (ORCID: https://orcid.org/0000-0003-4697-5295)
- Antônio Pereira de Souza Júnior
- Eura Martins Lage (ORCID: https://orcid.org/0000-0002-7614-695X)
- Isaias José Ramos de Oliveira (ORCID: https://orcid.org/0000-0001-9232-4309)
- Igor Fernandes Gomes (ORCID: https://orcid.org/0000-0003-2474-383X)
- Ricardo Joao Cruz-Correia (ORCID: https://orcid.org/0000-0002-3764-5158)
- Zilma Silveira Nogueira Reis (ORCID: https://orcid.org/0000-0001-6374-9295)
- Érico Franco Mineiro (ORCID: https://orcid.org/0000-0002-0602-1991)
- Leonardo Rocha (ORCID: https://orcid.org/0000-0002-4913-4902)
- Adriana Silvina Pagano
- Davi Dos Reis de Jesus
- Vinicius Araujo Basilio
Institutions
- Universidade Federal de Minas Gerais (BR)
- Universidade Estadual de Campinas (UNICAMP) (BR)
- Universidade do Porto (PT)
- Ministério da Saúde (BR)
- Federal University of São João del-Rei (BR)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2024-10-19
- DOI
- https://doi.org/10.1016/j.mcpdig.2024.09.006
- Citations
- 8
- Primary Topic
- Pharmaceutical Practices and Patient Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.75
Funders
- Bill and Melinda Gates Foundation
- Conselho Nacional de Desenvolvimento Científico e Tecnológico