Development of an AI-Based Clinical Decision Support System for Standardized Nursing Diagnoses in Indonesia
DOI:
https://doi.org/10.33755/jkk.v11i4.932Keywords:
artificial intelligence, nursing diagnosis, IDHS, clinical decision support system, content validity, qualitative studyAbstract
Background: The implementation of the Indonesian Nursing Diagnosis Standards (IDHS) in clinical practice continues to encounter obstacles, including limited time for assessment, documentation burden, and inconsistency in diagnostic interpretation among nurses. Advancements in artificial intelligence (AI) offer opportunities to design clinical decision support systems that may enhance the accuracy, uniformity, and efficiency of nursing diagnoses.
Objective: This study aimed to explore nurses’ experiences, challenges, and expectations regarding the use of IDHS and to assess the initial feasibility of developing an AI-based system to support standardized nursing diagnoses.
Methods: A descriptive qualitative design was employed using in-depth interviews with five nurses from diverse clinical settings, including emergency, critical care, inpatient, outpatient, and community services. Thematic analysis was conducted to identify perceptions and needs related to IDHS and AI. Content validity of the preliminary system design was evaluated by three expert validators using the Item-Level Content Validity Index (I-CVI) and the Scale-Level Content Validity Index/Average (S-CVI/Ave).
Results: Five major themes emerged: (1) varied experiences in formulating nursing diagnoses, (2) inconsistent use of IDHS in practice, (3) challenges related to documentation and diagnostic interpretation, (4) positive attitudes toward AI integration in clinical workflows, and (5) the need for features such as automated diagnostic suggestions, clarification of clinical criteria, and integration with electronic medical records. Content validity testing demonstrated strong agreement among experts, with I-CVI values ranging from 0.80 to 1.00 and an S-CVI/Ave of 0.92.
Conclusion: The study indicates that integrating AI into the IDHS-based diagnostic process holds substantial potential to improve diagnostic quality, standardization, and clinical efficiency. The content validity results support further development and prototype testing in subsequent research phases
References
1. Wei Q, Pan S, Liu X, Hong M, Nong C, Zhang W. The integration of AI in nursing: addressing current applications, challenges, and future directions. Front Med (Lausanne). 2025;12:1545420.
2. Yelne S, Chaudhary M, Dod K, Sayyad A, Sharma R. Harnessing the power of AI: a comprehensive review of its impact and challenges in nursing science and healthcare. Cureus. 2023;15(11).
3. Ngozi AL, Dioso RIII, Anyebe EE. Factors Influencing Documentation in Nursing Care by Nurses at the Federal Medical Centre, Apir, Benue State, Nigeria. The Malaysian Journal of Nursing (MJN). 2024;16(2):63–73.
4. Seidu AA, Abdulai A, Aninanya GA. Factors influencing documentation in nursing care by nurses at the tamale teaching hospital, Ghana. UDS International Journal of Development. 2021;8(1):540–58.
5. Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, et al. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J. 2019;40(25):2058–73.
6. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. New England Journal of Medicine. 2019;380(14):1347–58.
7. Yeo M, Tahayori B, Kok HK, Maingard J, Kutaiba N, Russell J, et al. Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging. Eur Radiol Exp. 2023;7(1):17.
8. Kuo W, Hӓne C, Mukherjee P, Malik J, Yuh EL. Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proceedings of the National Academy of Sciences. 2019;116(45):22737–45.
9. Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet. 2018;392(10162):2388–96.
10. Hasan SM, Mulyanto A. The Effect Of Using SDKI And Wilkinson Book On The Accuracy Level Of Nursing Diagnosis by Poltekkes Palu Nursing Student: PENGARUH PENGGUNAAN BUKU STANDAR DIAGNOSIS KEPERAWATAN INDONESIA (SDKI) DAN DIAGNOSIS KEPERAWATAN WILKINSON EDISI 10 TERHADAP TINGKAT KETEPATAN DIAGNOSIS KEPERAWATAN MAHASISWA POLTEKKES PALU. Lentora Nursing Journal. 2022;3(1):37–43.
11. Astutik EE, Budiman MEA, Amrullah AE, Yuwanto MA. Development of A Nursing Care Documentation Module Based on SDKI, SLKI, SIKI (3S). Babali Nursing Research. 2025;6(2):341–9.
12. Aravazhi PS, Gunasekaran P, Benjamin NZY, Thai A, Chandrasekar KK, Kolanu ND, et al. The integration of artificial intelligence into clinical medicine: trends, challenges, and future directions. Disease-a-Month. 2025;101882.
13. Varghese J. Artificial intelligence in medicine: chances and challenges for wide clinical adoption. Visc Med. 2020;36(6):443–9.
