Systematic Reviews and Meta Analysis
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Pediatrik Sepsis Hastaları için Otomatik Uyarı Sistemleri: Sistematik Bir Derleme

Year 2025,

Desi Dwi Siwi Atika Dewi , Suprihatiningsih Suprihatiningsih , Alessandra Hernanda Soselisa , Fransiska Regina Cealy , Muhammad Ulin Nuha , Nana Caterina Sandi , Tiara Royani , Ariani Arista Putri Pertiwi , Mahmasoni Masdar

https://doi.org/10.17049/jnursology.1524051

Abstract

Amaç: Pediatrik sepsis, belirtilerinin belirsizliği nedeniyle tanınması zor bir durumdur ve septik şoku önlemek için erken ve yoğun tedavi hayati önem taşır. Yapay zeka, hasta verilerine dayanarak uyarılar oluşturarak sepsis tespitini iyileştirebilir. Ancak, pediatrik sepsis taramasında yapay zeka kullanımını ele alan sistematik bir inceleme bulunmamaktadır. Bu çalışmanın araştırma sorusu: “Hastane ortamında pediatrik hastalarda sepsisin başlangıcını sağlık çalışanlarına bildirmek için hangi araçlar kullanılmaktadır?”

Yöntemler: Çalışma protokolü, PROSPERO numarası CRD42023467930 ile kaydedilmiştir. PubMed, ProQuest, ScienceDirect, Scopus ve EBSCO veritabanlarında, pediatrik hastane ortamında sepsisin erken tespiti için kullanılan araçlara odaklanarak arama yapılmıştır. Sepsis gelişmeyen hastaları içeren çalışmalar hariç tutulmuş, yalnızca İngilizce derleme makaleler dahil edilmiştir. Çalışma kalitesi, Joanna Briggs Institute (JBI) Değerlendirme Aracı ile değerlendirilmiş ve bulgular niteliksel olarak sentezlenmiştir.

Bulgular: Toplam 16 makaleden, pediatrik sepsis için otomatik uyarı sağlayabilecek 4 araç belirlenmiştir: Elektronik Tıbbi Kayıtlar (EMR), Elektronik Sağlık Kayıtları (EHR), Elektronik Uyarı Sistemi (EAS) ve Yenidoğan Ağlama Teşhis Sistemi (NCDS). En sık kullanılan araç EHR'dir. Bu sistemler, hayati belirtiler, laboratuvar sonuçları, cilt durumu ve bebeğin ağlaması gibi çeşitli verilere ihtiyaç duyar.

Sonuç: Otomatik uyarı sistemleri, tanı doğruluğunu artırır, karar verme sürecini hızlandırır ve çocuklarda sepsisle ilişkili ölüm oranlarını azaltır. Dil sınırlamaları ve araçların etkinliğini değerlendirme konusundaki yetersizlikler, daha fazla araştırmaya ihtiyaç olduğunu göstermektedir.

Keywords

Pediatri sepsis yapay zeka

References

  • 1. Agulnik A, Méndez Aceituno A, Mora Robles LN, et al. Validation of a pediatric early warning system for hospitalized pediatric oncology patients in a resource-limited setting. Cancer. 2017;123(24):4903-4913. https://doi.org/10.1002/cncr.30951
  • 2. Vincent JL. Evolution of the Concept of Sepsis. Antibiotics. 2022;11(11):11-15. https://doi.org/10.3390/antibiotics11111581
  • 3. Evans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181-1247. https://doi.org/10.1007/s00134-021-06506-y
  • 4. Weiss SL, Deutschman CS. Are septic children really just “septic little adults”? Intensive Care Med. 2018;44(3):392-394. https://doi.org/10.1007/s00134-017-5041-4
  • 5. Fleischmann C, Reichert F, Cassini A, et al. Global incidence and mortality of neonatal sepsis: A systematic review and meta-analysis. Arch Dis Child. 2021;106(8):745-752. https://doi.org/10.1136/archdischild-2020-320217
  • 6. Yuniar I, Karyanti MR, Kurniati N, Handayani D. The clinical and biomarker approach to predict sepsis mortality in pediatric patients. Paediatrica Indonesiana(Paediatrica Indonesiana). 2023;63(1):37-44. https://doi.org/10.14238/pi63.1.2023.37-44
  • 7. Cruz AT, Lane RD, Balamuth F, et al. Updates on pediatric sepsis. JACEP Open. 2020;1(5):981-993. https://doi.org/10.1002/emp2.12173
  • 8. Peshimam N, Nadel S. Sepsis in children: state-of-the-art treatment. Ther Adv Infect Dis. 2021;8(X):1-11. https://doi.org/10.1177/20499361211055332
  • 9. Mathias B, Mira JC, Larson SD. Pediatric sepsis. Curr Opin Pediatr. 2016;28(3):380-387. https://doi.org/10.1097/mop.0000000000000337
  • 10. Miranda M, Nadel S. Pediatric Sepsis: a Summary of Current Definitions and Management Recommendations. Curr Pediatr Rep. 2023;11(2):29-39. https://doi.org/10.1007/s40124-023-00286-3
  • 11. Balamuth F, Alpern ER, Abbadessa MK, et al. Improving Recognition of Pediatric Severe Sepsis in the Emergency Department: Contributions of a Vital Sign–Based Electronic Alert and Bedside Clinician Identification. Ann Emerg Med. 2017;70(6):759-768.e2. https://doi.org/10.1016/j.annemergmed.2017.03.019
  • 12. Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU. Pediatric Critical Care Medicine. 2018;19(10):E495-E503. https://doi.org/10.1097/pcc.0000000000001666
  • 13. Tabaie A, Orenstein EW, Nemati S, Basu RK, Clifford GD, Kamaleswaran R. Deep Learning Model to Predict Serious Infection Among Children with Central Venous Lines. Front Pediatr. 2021;9(November 2020). https://doi.org/10.3389/fped.2021.726870
  • 14. Matikolaie FS, Tadj C. Machine Learning-Based Cry Diagnostic System for Identifying Septic Newborns. Journal of Voice. Published online 2022. https://doi.org/10.1016/j.jvoice.2021.12.021
  • 15. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71
  • 16. Lloyd JK, Ahrens EA, Clark D, Dachenhaus T, Nuss KE. Automating a manual sepsis screening tool in a pediatric emergency department. Appl Clin Inform. 2018;9(4):803-808. https://doi.org/10.1055/s-0038-1675211
  • 17. Eisenberg MA, Freiman E, Capraro A, et al. Outcomes of Patients with Sepsis in a Pediatric Emergency Department after Automated Sepsis Screening. Journal of Pediatrics. 2021;235:239-245.e4. doi:10.1016/j.jpeds.2021.03.053
  • 18. Eisenberg M, Freiman E, Capraro A, et al. Comparison of manual and automated sepsis screening tools in a pediatric emergency department. Pediatrics. 2021;147(2). https://doi.org/10.1016/j.jpeds.2021.03.053
  • 19. Le S, Hoffman J, Barton C, et al. Pediatric Severe Sepsis Prediction Using Machine Learning. Front Pediatr. 2019;7(October):1-8. https://doi.org/10.3389/fped.2019.00413
  • 20. Dewan M, Vidrine R, Zackoff M, et al. Design, Implementation, and Validation of a Pediatric ICU Sepsis Prediction Tool as Clinical Decision Support. Appl Clin Inform. 2020;11(2):218-225. https://doi.org/10.1055/s-0040-1705107
  • 21. Depinet H, Macias CG, Balamuth F, et al. Pediatric Septic Shock Collaborative Improves Emergency Department Sepsis Care in Children. Pediatrics. 2022;149(3). https://doi.org/10.1542/peds.2020-007369
  • 22. Eisenberg M, Madden K, Christianson JR, Melendez E, Harper MB. Performance of an Automated Screening Algorithm for Early Detection of Pediatric Severe Sepsis. Pediatric Critical Care Medicine. 2019;20(12):e516-e523. https://doi.org/10.1097/pcc.0000000000002101
  • 23. Sepanski RJ, Godambe SA, Mangum CD, Bovat CS, Zaritsky AL, Shah SH. Designing a pediatric severe sepsis screening tool. Front Pediatr. 2014;16(2):56. https://doi.org/10.3389/fped.2014.00056
  • 24. Xiang L, Wang H, Fan S, et al. Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study. Front Oncol. 2021;11:1-9. https://doi.org/10.3389/fonc.2021.678743
  • 25. Stinson HR, Viteri S, Koetter P, et al. Early Experience with a Novel Strategy for Assessment of Sepsis Risk: The Shock Huddle. Pediatr Qual Saf. 2019;4(4):e197. https://doi.org/10.1097/pq9.0000000000000197
  • 26. Gibbs KD, Shi Y, Sanders N, et al. Evaluation of a Sepsis Alert in the Pediatric Acute Care Setting. Appl Clin Inform. 2021;12(3):469-478. https://doi.org/10.1055/s-0041-1730027
  • 27. Alturki A, Al-Eyadhy A, Alfayez A, et al. Impact of an electronic alert system for pediatric sepsis screening a tertiary hospital experience. Sci Rep. 2022;12(1):1-8. https://doi.org/10.1038/s41598-022-16632-2
  • 28. Zhang Z, Chen L, Xu P, et al. Effectiveness of automated alerting system compared to usual care for the management of sepsis. NPJ Digit Med. 2022;5:101. https://doi.org/10.1038/s41746-022-00650-5
  • 29. Westphal GA, Pereira AB, Fachin SM, et al. An electronic warning system helps reduce the time to diagnosis of sepsis. Rev Bras Ter Intensiva. 2018;30(4):414-422. https://doi.org/10.5935/0103-507x.20180059
  • 30. Valik JK, Ward L, Tanushi H, et al. Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data. Sci Rep. 2023;13:11760. https://doi.org/10.1038/s41598-023-38858-4
  • 31. Austrian JS, Jamin CT, Doty GR, Blecker S. Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay. Journal of the American Medical Informatics Association. 2018;25(5):523-529. https://doi.org/10.1093/jamia/ocx072
  • 32. McGreevey JD, Mallozzi CP, Perkins RM, Shelov E, Schreiber R. Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems. Appl Clin Inform. 2020;11(1):1-12. https://doi.org/10.1055/s-0039-3402715
  • 33. Hydari MZ, Telang R, Marella WM. Electronic health records and patient safety. Commun ACM. 2015;58(11):30-32. https://doi.org/10.1145/2822515
  • 34. Upadhyay S, Hu HF. A Qualitative Analysis of the Impact of Electronic Health Records (EHR) on Healthcare Quality and Safety: Clinicians’ Lived Experiences. Health Serv Insights. 2022;15:11786329211070722. https://doi.org/10.1177/11786329211070722
  • 35. Yayah Y, Rahman LOA. Peranan Electronic Health Record System terhadap Keselamatan Pasien di Perawatan Anak. JIKO J Ilm Keperawatan Orthop. 2020;4(1):23–32. http://dx.doi.org/10.46749/jiko.v4i1.34
  • 36. Kataria S, Ravindran V. Electronic Health Records: A Critical Appraisal of Strengths and Limitations. Journal of the Royal College of Physicians of Edinburgh. 2020;50(3):262-268. https://doi.org/10.4997/jrcpe.2020.309
  • 37. Hwang MI, Bond WF, Powell ES. Sepsis alerts in emergency departments: A systematic review of accuracy and quality measure impact. Western Journal of Emergency Medicine. 2020;21(5):1201-1210. https://doi.org/10.5811/westjem.2020.5.46010

Automated Alerts Systems for Pediatric Sepsis Patients: A Systematic Review

Year 2025,

Desi Dwi Siwi Atika Dewi , Suprihatiningsih Suprihatiningsih , Alessandra Hernanda Soselisa , Fransiska Regina Cealy , Muhammad Ulin Nuha , Nana Caterina Sandi , Tiara Royani , Ariani Arista Putri Pertiwi , Mahmasoni Masdar

https://doi.org/10.17049/jnursology.1524051

Abstract

Objective: Pediatric sepsis is difficult to identify due to subtle symptoms, and early aggressive management is crucial to prevent septic shock. Artificial intelligence can improve sepsis detection by triggering alerts based on patient data. No systematic review has yet discussed AI use for pediatric sepsis screening. This study aims to answer: “What tools alert healthcare providers to the onset of sepsis in pediatric patients in hospitals?”

Methods: The study protocol was registered with PROSPERO (CRD42023467930). We searched PubMed, ProQuest, ScienceDirect, Scopus, and EBSCO, focusing on pediatric hospital settings using tools for early sepsis detection, excluding studies on non-sepsis patients, and limiting inclusion to English literature reviews without a publication year restriction. The Joanna Briggs Institute (JBI) Appraisal Tool evaluated study quality, and findings were synthesized qualitatively.

Results: Out of 16 articles, four tools for automatic sepsis alerts in pediatrics were identified: Electronic Medical Records (EMR), Electronic Health Records (EHR), The Electronic Alert System (EAS), and The Newborn Cry Diagnostic System (NCDS). EHR is the most commonly used. These tools require various data, such as vital signs, lab results, skin condition, capillary refill, and even a baby's cry.

Conclusion: Automated sepsis alerts in pediatrics enhance diagnostic accuracy, expedite decision-making, and decrease sepsis-related mortality. Limitations include language restrictions and the inability to assess each tool's effectiveness or identify the optimal sepsis detection algorithm, underscoring the need for further research, including a meta-analysis.

Keywords

Pediatrics Sepsis Artificial Intelligence

References

  • 1. Agulnik A, Méndez Aceituno A, Mora Robles LN, et al. Validation of a pediatric early warning system for hospitalized pediatric oncology patients in a resource-limited setting. Cancer. 2017;123(24):4903-4913. https://doi.org/10.1002/cncr.30951
  • 2. Vincent JL. Evolution of the Concept of Sepsis. Antibiotics. 2022;11(11):11-15. https://doi.org/10.3390/antibiotics11111581
  • 3. Evans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181-1247. https://doi.org/10.1007/s00134-021-06506-y
  • 4. Weiss SL, Deutschman CS. Are septic children really just “septic little adults”? Intensive Care Med. 2018;44(3):392-394. https://doi.org/10.1007/s00134-017-5041-4
  • 5. Fleischmann C, Reichert F, Cassini A, et al. Global incidence and mortality of neonatal sepsis: A systematic review and meta-analysis. Arch Dis Child. 2021;106(8):745-752. https://doi.org/10.1136/archdischild-2020-320217
  • 6. Yuniar I, Karyanti MR, Kurniati N, Handayani D. The clinical and biomarker approach to predict sepsis mortality in pediatric patients. Paediatrica Indonesiana(Paediatrica Indonesiana). 2023;63(1):37-44. https://doi.org/10.14238/pi63.1.2023.37-44
  • 7. Cruz AT, Lane RD, Balamuth F, et al. Updates on pediatric sepsis. JACEP Open. 2020;1(5):981-993. https://doi.org/10.1002/emp2.12173
  • 8. Peshimam N, Nadel S. Sepsis in children: state-of-the-art treatment. Ther Adv Infect Dis. 2021;8(X):1-11. https://doi.org/10.1177/20499361211055332
  • 9. Mathias B, Mira JC, Larson SD. Pediatric sepsis. Curr Opin Pediatr. 2016;28(3):380-387. https://doi.org/10.1097/mop.0000000000000337
  • 10. Miranda M, Nadel S. Pediatric Sepsis: a Summary of Current Definitions and Management Recommendations. Curr Pediatr Rep. 2023;11(2):29-39. https://doi.org/10.1007/s40124-023-00286-3
  • 11. Balamuth F, Alpern ER, Abbadessa MK, et al. Improving Recognition of Pediatric Severe Sepsis in the Emergency Department: Contributions of a Vital Sign–Based Electronic Alert and Bedside Clinician Identification. Ann Emerg Med. 2017;70(6):759-768.e2. https://doi.org/10.1016/j.annemergmed.2017.03.019
  • 12. Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU. Pediatric Critical Care Medicine. 2018;19(10):E495-E503. https://doi.org/10.1097/pcc.0000000000001666
  • 13. Tabaie A, Orenstein EW, Nemati S, Basu RK, Clifford GD, Kamaleswaran R. Deep Learning Model to Predict Serious Infection Among Children with Central Venous Lines. Front Pediatr. 2021;9(November 2020). https://doi.org/10.3389/fped.2021.726870
  • 14. Matikolaie FS, Tadj C. Machine Learning-Based Cry Diagnostic System for Identifying Septic Newborns. Journal of Voice. Published online 2022. https://doi.org/10.1016/j.jvoice.2021.12.021
  • 15. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71
  • 16. Lloyd JK, Ahrens EA, Clark D, Dachenhaus T, Nuss KE. Automating a manual sepsis screening tool in a pediatric emergency department. Appl Clin Inform. 2018;9(4):803-808. https://doi.org/10.1055/s-0038-1675211
  • 17. Eisenberg MA, Freiman E, Capraro A, et al. Outcomes of Patients with Sepsis in a Pediatric Emergency Department after Automated Sepsis Screening. Journal of Pediatrics. 2021;235:239-245.e4. doi:10.1016/j.jpeds.2021.03.053
  • 18. Eisenberg M, Freiman E, Capraro A, et al. Comparison of manual and automated sepsis screening tools in a pediatric emergency department. Pediatrics. 2021;147(2). https://doi.org/10.1016/j.jpeds.2021.03.053
  • 19. Le S, Hoffman J, Barton C, et al. Pediatric Severe Sepsis Prediction Using Machine Learning. Front Pediatr. 2019;7(October):1-8. https://doi.org/10.3389/fped.2019.00413
  • 20. Dewan M, Vidrine R, Zackoff M, et al. Design, Implementation, and Validation of a Pediatric ICU Sepsis Prediction Tool as Clinical Decision Support. Appl Clin Inform. 2020;11(2):218-225. https://doi.org/10.1055/s-0040-1705107
  • 21. Depinet H, Macias CG, Balamuth F, et al. Pediatric Septic Shock Collaborative Improves Emergency Department Sepsis Care in Children. Pediatrics. 2022;149(3). https://doi.org/10.1542/peds.2020-007369
  • 22. Eisenberg M, Madden K, Christianson JR, Melendez E, Harper MB. Performance of an Automated Screening Algorithm for Early Detection of Pediatric Severe Sepsis. Pediatric Critical Care Medicine. 2019;20(12):e516-e523. https://doi.org/10.1097/pcc.0000000000002101
  • 23. Sepanski RJ, Godambe SA, Mangum CD, Bovat CS, Zaritsky AL, Shah SH. Designing a pediatric severe sepsis screening tool. Front Pediatr. 2014;16(2):56. https://doi.org/10.3389/fped.2014.00056
  • 24. Xiang L, Wang H, Fan S, et al. Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study. Front Oncol. 2021;11:1-9. https://doi.org/10.3389/fonc.2021.678743
  • 25. Stinson HR, Viteri S, Koetter P, et al. Early Experience with a Novel Strategy for Assessment of Sepsis Risk: The Shock Huddle. Pediatr Qual Saf. 2019;4(4):e197. https://doi.org/10.1097/pq9.0000000000000197
  • 26. Gibbs KD, Shi Y, Sanders N, et al. Evaluation of a Sepsis Alert in the Pediatric Acute Care Setting. Appl Clin Inform. 2021;12(3):469-478. https://doi.org/10.1055/s-0041-1730027
  • 27. Alturki A, Al-Eyadhy A, Alfayez A, et al. Impact of an electronic alert system for pediatric sepsis screening a tertiary hospital experience. Sci Rep. 2022;12(1):1-8. https://doi.org/10.1038/s41598-022-16632-2
  • 28. Zhang Z, Chen L, Xu P, et al. Effectiveness of automated alerting system compared to usual care for the management of sepsis. NPJ Digit Med. 2022;5:101. https://doi.org/10.1038/s41746-022-00650-5
  • 29. Westphal GA, Pereira AB, Fachin SM, et al. An electronic warning system helps reduce the time to diagnosis of sepsis. Rev Bras Ter Intensiva. 2018;30(4):414-422. https://doi.org/10.5935/0103-507x.20180059
  • 30. Valik JK, Ward L, Tanushi H, et al. Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data. Sci Rep. 2023;13:11760. https://doi.org/10.1038/s41598-023-38858-4
  • 31. Austrian JS, Jamin CT, Doty GR, Blecker S. Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay. Journal of the American Medical Informatics Association. 2018;25(5):523-529. https://doi.org/10.1093/jamia/ocx072
  • 32. McGreevey JD, Mallozzi CP, Perkins RM, Shelov E, Schreiber R. Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems. Appl Clin Inform. 2020;11(1):1-12. https://doi.org/10.1055/s-0039-3402715
  • 33. Hydari MZ, Telang R, Marella WM. Electronic health records and patient safety. Commun ACM. 2015;58(11):30-32. https://doi.org/10.1145/2822515
  • 34. Upadhyay S, Hu HF. A Qualitative Analysis of the Impact of Electronic Health Records (EHR) on Healthcare Quality and Safety: Clinicians’ Lived Experiences. Health Serv Insights. 2022;15:11786329211070722. https://doi.org/10.1177/11786329211070722
  • 35. Yayah Y, Rahman LOA. Peranan Electronic Health Record System terhadap Keselamatan Pasien di Perawatan Anak. JIKO J Ilm Keperawatan Orthop. 2020;4(1):23–32. http://dx.doi.org/10.46749/jiko.v4i1.34
  • 36. Kataria S, Ravindran V. Electronic Health Records: A Critical Appraisal of Strengths and Limitations. Journal of the Royal College of Physicians of Edinburgh. 2020;50(3):262-268. https://doi.org/10.4997/jrcpe.2020.309
  • 37. Hwang MI, Bond WF, Powell ES. Sepsis alerts in emergency departments: A systematic review of accuracy and quality measure impact. Western Journal of Emergency Medicine. 2020;21(5):1201-1210. https://doi.org/10.5811/westjem.2020.5.46010
There are 37 citations in total.

Details

Primary Language English
Subjects Pediatric Health and Illnesses Nursing
Journal Section Systematic Review
Authors

Desi Dwi Siwi Atika Dewi 0009-0009-1421-5209

Suprihatiningsih Suprihatiningsih 0009-0003-5364-8492

Alessandra Hernanda Soselisa 0009-0008-3861-9024

Fransiska Regina Cealy 0009-0006-2613-6004

Muhammad Ulin Nuha 0009-0001-4220-0751

Nana Caterina Sandi 0009-0007-1276-623X

Tiara Royani 0009-0005-7716-1763

Ariani Arista Putri Pertiwi 0000-0001-6439-2304

Mahmasoni Masdar 0000-0002-3174-5457

Early Pub Date March 13, 2025
Publication Date
Submission Date July 29, 2024
Acceptance Date December 16, 2024
Published in Issue Year 2025

Cite

AMA Dewi DDSA, Suprihatiningsih S, Soselisa AH, Cealy FR, Nuha MU, Sandi NC, Royani T, Pertiwi AAP, Masdar M. Automated Alerts Systems for Pediatric Sepsis Patients: A Systematic Review. Journal of Nursology. Published online March 1, 2025. doi:10.17049/jnursology.1524051

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