Artificial intelligence in mapping nursing diagnoses, interventions, and outcomes for diabetes management

Beatriz Barboza Fernandes; Rodrigo de Araujo Marques; Rosane Barreto Cardoso; Rejane Prado dos Santos; Thaíssa Felix Affonso

doi:10.17533/udea.iee.v44n1e07

Authors

Beatriz Barboza Fernandes Federal University of Rio de Janeiro
Rodrigo de Araujo Marques Federal University of Rio de Janeiro
Rosane Barreto Cardoso Federal University of Rio de Janeiro https://orcid.org/0000-0001-8052-8697
Rejane Prado dos Santos Federal University of Rio de Janeiro
Thaíssa Felix Affonso Federal University of Rio de Janeiro https://orcid.org/0009-0001-6719-8643

DOI:

https://doi.org/10.17533/udea.iee.v44n1e07

Keywords:

diabetes mellitus type 2, nursing process, nursing diagnosis, treatment outcome, standardized nursing terminology, nursing care, artificial intelligence, cross-mapping

Abstract

Objective. To map nursing diagnoses, nursing outcomes, and nursing interventions based on the clinical indicators described in the Type 2 Diabetes Diagnosis and Management Manual, using artificial intelligence (AI) (GPT-4®).

Methods. Descriptive study with adapted cross-mapping. GPT-4® was applied with a structured prompt to identify clinical indicators in the manual and correlate them with nursing classifications.

Results. AI identified 43 clinical indicators, and after manual review, 30 were confirmed, with 23 overlapping between methods. From these, 30 nursing diagnoses, 30 expected outcomes, and 30 interventions were mapped. Manual mapping consolidated 15 nursing diagnoses, 15 outcomes, and 15 interventions.

Conclusion. AI proved effective in expediting and standardizing cross-mapping in nursing. However, human clinical judgment was indispensable to validate and adjust inconsistencies, capturing nuances not identified by AI. The integration of AI with clinical reasoning can strengthen care systematization, support evidence-based protocols, and improve outcomes in patients with diabetes.

|Abstract

= 1 veces | PDF

= 0 veces|

Downloads

Download data is not yet available.

Author Biographies

Beatriz Barboza Fernandes, Federal University of Rio de Janeiro

Nurse, master's student.

Rodrigo de Araujo Marques, Federal University of Rio de Janeiro

Nurse.

Rosane Barreto Cardoso, Federal University of Rio de Janeiro

Nurse, Ph.D.

Rejane Prado dos Santos, Federal University of Rio de Janeiro

Nurse, master's student.

Thaíssa Felix Affonso, Federal University of Rio de Janeiro

Nurse, master's student.

References

1. World Health Organization. Diabetes [Internet]. World Health Organization. 2024. Available from: https://www.who.int/news-room/fact-sheets/detail/diabetes

2. Conselho Federal de Enfermagem (BR). Resolução nº 736, de 17 de janeiro de 2024. Dispõe sobre a implementação do Processo de Enfermagem em todo contexto socioambiental onde ocorre o cuidado de enfermagem [Internet]. Brasília, DF: COFEN; 2024. Available from: https://www.cofen.gov.br/resolucao-cofen-no-736-de-17-de-janeiro-de-2024/

3. Zhang T, Wu X, Peng G, Zhang Q, Chen L, Cai Z, et al. Effectiveness of standardized nursing terminologies for nursing practice and healthcare outcomes: A systematic review. International Journal of Nursing Knowledge. 2021; 32(4):220–8.

4. Diagnóstico e manejo do diabetes tipo 2 (HEARTS-D) [Internet]. Paho.org. 2020. Available from: https://www.paho.org/pt/documentos/diagnostico-e-manejo-do-diabetes-tipo-2-hearts-d

5. Rodrigues JAP, Lacerda MR, Galvão CM, Cubas MR. Use of the International Classification for Nursing Practice in the construction of a care protocol. Revista Brasileira de Enfermagem. 2022;75(4): e20210488.

6. Karacan E. Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models. Journal of Nursing Care and Quality. 2024; 39(3):206-21.

7. McGrow K. Artificial intelligence in nursing: A journey from data to wisdom. Nursing. 2025; 55(4):16-24.

8. Lucena A de F, Barros ALBL de. Mapeamento cruzado: uma alternativa para a análise de dados em enfermagem. Acta Paulista de Enfermagem. 2005; 18(1):82–8.

9. Diyab A, Frost RM, Fedoruk BD, Diyab A. Engineered prompts in ChatGPT for educational assessment in software engineering and computer science. Education Sciences. 2025; 15(2):156.

10. Heather HT, Kamitsuru S, Lopes CT. Diagnósticos de Enfermagem da NANDA-I. Artmed Editora; 2024.

11. Moorhead S, Johnson M, Massone M, Swanson E. NOC Classificação dos Resultados de Enfermagem. Grupo GEN; 2024.

12. Butcher HK, Bulechek GM, Dochermann JM, Wagner CM. NIC - Classificação das Intervenções de Enfermagem. Grupo GEN; 2020.

13. O'Connor S, Peltonen LM, Topaz M, Chen LA, Michalowski M, Ronquillo C, Stiglic G, Chu CH, Hui V, Denis-Lalonde D. Prompt engineering when using generative AI in nursing education. Nurse Education and Practice. 2024; 74:103825.

14. Serra EB, Ferreira AGN, Pascoal LM, Rolim ILTP. Nursing diagnoses in diabetic patients: an integrative review. Revista Enfermagem UERJ. 2020; 28: e48274.

15. Garizábalo-Dávila CM, Cañon-Montañez W, Rodríguez-Acelas AL. Nursing outcomes and social support intervention for diabetes self-management: consensus study. Revista Cuidarte. 2024; 15(3):e3742.

16. American Diabetes Association. Diabetes & DKA (Ketoacidosis) | ADA [Internet]. diabetes.org. 2024. Available from: https://diabetes.org/about-diabetes/complications/ketoacidosis-dka/dka-ketoacidosis-ketones

17. Carlos AG, Dias V da N, Perracini MR, Doná F, Sousa AGP, Gazzola JM. Equilíbrio postural e fatores associados ao risco de quedas em idosos com diabetes mellitus tipo 2. Revista Brasileira de Geriatria y Gerontolologia. 2024; 27:e230161.

18. Diniz FS, Rodrigues JA, Vaez AC, Santos AD, Araújo D da C, Silva B de A, de Andrade JS, Sousa PHSF. Plano de cuidado de enfermagem para usuários com diabetes Mellitus. Brazilian Journal of Development. 2021 ;7(2):16278-92.

19. Rodríguez-Suárez CA, González-de la Torre H, Hernández-De Luis MN, Fernández-Gutiérrez DÁ, Martínez-Alberto CE, Brito-Brito PR. Effectiveness of a Standardized Nursing Process Using NANDA International, Nursing Interventions Classification and Nursing Outcome Classification Terminologies: A Systematic Review. Healthcare (Basel). 2023; 11(17):2449.

20. Aríztegui Echenique AM, San Martín Rodríguez L, Marín Fernández B. [Effectiveness of nursing interventions in the control of type 2 diabetes mellitus]. Anales del Sistema Sanitario de Navarra. 2020; 43(2):159-167.

21. Fennelly O, Grogan L, Reed A, Hardiker NR. Use of standardized terminologies in clinical practice: A scoping review. International Journal of Medical Informatics. 2021; 149(1):104431.