The Double-Edged Sword of Clinical Decision Support in Labor & Delivery: A Systematic Review of its Impact on Nursing Judgment

Majeb Mubark Aldosari (1) , Azzah Atitallah Ali   Alzahrani (2) , Abdulrahman Saad   Almutairi (3) , Rasha Saleh   ALhawass (4) , Amira Nasser   Aljaber (5) , Ahmed Essa Zalah (6) , Wael Awad ALhaidari (7) , Ohoud Sulaiman Alotibi (8) , Ayshah Mohamed Mohamed Al Abdin (9) , Abdulaziz Sori Almejlad (10)
(1) Prince Sultan Health Centre, Ministry of Health, Saudi Arabia,
(2) Al-Malaz Phc , Cluster 1 Al-Riyadh, Ministry of Health, Saudi Arabia,
(3) Riyadh, Al Yamamah Hospital, Ministry of Health, Saudi Arabia,
(4) Primary Health Care Hazem Umm Al Sahek , Safwa, Ministry of Health, Saudi Arabia,
(5) Prince Mohammed Bin Abdulaziz Hospital, Riyadh, Ministry of Health, Saudi Arabia,
(6) Alaqiq General Hospital , Ministry of Health, Saudi Arabia,
(7) King Fahad Hospital Medina, Ministry Of Health, Saudi Arabia,
(8) PHC Almansorh, Ministry of Health, Saudi Arabia,
(9) Madinah Women, Maternity And Children Hospital, Ministry of Health, Saudi Arabia,
(10) Judaidat Arar Hospital, Ministry of Health, Saudi Arabia

Abstract

Background: The integration of Clinical Decision Support (CDS) systems in Electronic Health Records (EHRs) has become the cornerstone of modern obstetric practice, aimed at standardizing and improving patient safety. In the high-stakes environment of Labor and Delivery (L&D), CDS tools, specifically for fetal heart rate (FHR) interpretation and oxytocin administration, are widely used in practice. These systems exert a profound influence on L&D nurses' workflow and clinical decision-making, as they are the primary agents of continuous patient monitoring.


Aim: This review synthesizes the literature from 2015 to 2024 to explore the multifaceted impact of EHR-embedded CDS on nursing judgment, specifically on its effect on nursing autonomy, patient safety, and the phenomenon of alert fatigue.


Methods: A narrative review was conducted by searching the databases PubMed, CINAHL, and Web of Science. Search terms were "clinical decision support," "nursing," "labor and delivery," "fetal heart rate," "oxytocin," "patient safety," "autonomy," and "alert fatigue."


Results: The findings show a complex and often conflicting interplay between CDS and nursing practice. CDS systems can enhance safety by providing an organized framework for FHR assessment and imposing evidence-based oxytocin protocols, thus leading to a reduction in adverse events. They can, at the same time, erode nursing autonomy by promoting algorithmic thought, deskilling, and replacing holism in clinical judgment. Furthermore, high levels of non-actionable or excessively sensitive alerts are one of the biggest contributors to alert fatigue, which consequently leads to workarounds, desensitization, and safety issues that eliminate the intended benefits.


Conclusion: CDS in L&D is a double-edged sword. Its optimal application depends on a human-factors design that produces systems to support, rather than supplant, the nurse's critical thinking. Strategies need to address escalating alert specificity, smoothly integrating CDS into nursing workflow, and building a culture in which technology supplements, but never substitutes for, expert nursing judgment. Safe obstetric care in the future hinges on a complementary partnership of nurse intuition and computerized intelligence.

Full text article

Generated from XML file

References

Ahmed Elbilgahy, A., Elsayed AbdelAziz Wady, D., & Gamal Badawy, G. (2023). Effect of Implementing an Educational Intervention about Managing Alarm Fatigue on Improving Clinical Practices of Pediatric Critical Care Nurses. Egyptian Journal of Health Care, 14(4), 709-723. https://doi.org/10.21608/ejhc.2023.331210

Al-Mutawtah, M., Campbell, E., Kubis, H. P., & Erjavec, M. (2023). Women’s experiences of social support during pregnancy: a qualitative systematic review. BMC Pregnancy and Childbirth, 23(1), 782. https://doi.org/10.1186/s12884-023-06089-0

Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., Kaushal, R., & With the HITEC Investigators. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC medical informatics and decision making, 17(1), 36. https://doi.org/10.1186/s12911-017-0430-8

Baker III, A. M., Christmas, J. T., Sheehan, R. A., Cadwell, S. M., Fraker, S., Finer, A., ... & Mehta, P. C. (2023). Impact of Adherence to a Standardized Oxytocin Induction Protocol on Obstetric and Neonatal Outcomes. The Joint Commission Journal on Quality and Patient Safety, 49(1), 34-41. https://doi.org/10.1016/j.jcjq.2022.10.003

Bayes, S., & Ewens, B. (2017). Registered nurses’ experiences of caring for pregnant and postpartum women in general hospital settings: a systematic review and meta‐synthesis of qualitative data. Journal of Clinical Nursing, 26(5-6), 599-608. https://doi.org/10.1111/jocn.13524

Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Keith, R., Kenyon, S., ... & Steer, P. (2017). Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet, 389(10080), 1719-1729. https://doi.org/10.1016/S0140-6736(17)30568-8

Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., ... & Shi, N. (2023). Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart, 10(2), e002432. https://doi.org/10.1136/openhrt-2023-002432

Clark, S. L., Nageotte, M. P., Garite, T. J., Freeman, R. K., Miller, D. A., Simpson, K. R., ... & Hankins, G. D. (2013). Intrapartum management of category II fetal heart rate tracings: towards standardization of care. American journal of obstetrics and gynecology, 209(2), 89-97. https://doi.org/10.1016/j.ajog.2013.04.030

Colomar, M., Opiyo, N., Kingdon, C., Long, Q., Nion, S., Bohren, M. A., & Betran, A. P. (2021). Do women prefer caesarean sections? A qualitative evidence synthesis of their views and experiences. PloS one, 16(5), e0251072.https://doi.org/10.1371/journal.pone.0251072

Ding, S., Huang, X., Sun, R., Yang, L., Yang, X., Li, X., ... & Wang, X. (2023). The relationship between alarm fatigue and burnout among critical care nurses: A cross‐sectional study. Nursing in critical care, 28(6), 940-947. https://doi.org/10.1111/nicc.12899

Du, Y., McNestry, C., Wei, L., Antoniadi, A. M., McAuliffe, F. M., & Mooney, C. (2023). Machine learning-based clinical decision support systems for pregnancy care: a systematic review. International Journal of Medical Informatics, 173, 105040. https://doi.org/10.1016/j.ijmedinf.2023.105040

Evans, M. I., Britt, D. W., Evans, S. M., & Devoe, L. D. (2022). Changing perspectives of electronic fetal monitoring. Reproductive Sciences, 29(6), 1874-1894. https://doi.org/10.1007/s43032-021-00749-2

Forde‐Johnston, C., Butcher, D., & Aveyard, H. (2023). An integrative review exploring the impact of Electronic Health Records (EHR) on the quality of nurse–patient interactions and communication. Journal of advanced nursing, 79(1), 48-67. https://doi.org/10.1111/jan.15484

Georgieva, A., Abry, P., Nunes, I., & Frasch, M. G. (2022). Fetal-maternal monitoring in the age of artificial intelligence and computer-aided decision support: A multidisciplinary perspective. Frontiers in Pediatrics, 10, 1007799. https://doi.org/10.3389/fped.2022.1007799

Jackson, J. K., Wickstrom, E., & Anderson, B. (2019). Oxytocin guidelines associated with compliance to national standards. MCN: The American Journal of Maternal/Child Nursing, 44(3), 128-136. DOI: 10.1097/NMC.0000000000000520

James, H. M., Papoutsi, C., Wherton, J., Greenhalgh, T., & Shaw, S. E. (2021). Spread, scale-up, and sustainability of video consulting in health care: systematic review and synthesis guided by the NASSS framework. Journal of medical Internet research, 23(1), e23775. https://doi.org/10.2196/23775

Kennedy, M. A., & Moen, A. (2017). Nurse leadership and informatics competencies: shaping transformation of professional practice. In Forecasting informatics competencies for nurses in the future of connected health (pp. 197-206). IOS Press. DOI: 10.3233/978-1-61499-738-2-197

Kissler, K., Jones, J., McFarland, A. K., & Luchsinger, J. (2020). A qualitative meta-synthesis of women’s experiences of labor dystocia. Women and Birth, 33(4), e332-e338. https://doi.org/10.1016/j.wombi.2019.08.001

Kagan, O., Owen, K., & Carroll, W. (2023). The State of Nursing Informatics Specialty in 2024: Practice, Research, and Education. CIN: Computers, Informatics, Nursing, 10-1097. DOI: 10.1097/CIN.0000000000001225

Kupfer, C., Prassl, R., Fleiß, J., Malin, C., Thalmann, S., & Kubicek, B. (2023). Check the box! How to deal with automation bias in AI-based personnel selection. Frontiers in Psychology, 14, 1118723. https://doi.org/10.3389/fpsyg.2023.1118723

McBride, A. B. (2006). Informatics and the future of nursing practice. Nursing and informatics for the 21st century, 5-12.

Miller, A., Moon, B., Anders, S., Walden, R., Brown, S., & Montella, D. (2015). Integrating computerized clinical decision support systems into clinical work: a meta-synthesis of qualitative research. International journal of medical informatics, 84(12), 1009-1018. https://doi.org/10.1016/j.ijmedinf.2015.09.005

Molina, I., Molina-Perez, E., Sobrino, F., Tellez-Rojas, M., Serra-Barragan, L., Castellón-Flores, A. M., ... & Rojas-Iturria, F. (2023). Cognitive modeling for understanding interactions between people and decision support tools in complex and uncertain environments: A study protocol. Plos one, 18(10), e0290683. https://doi.org/10.1371/journal.pone.0290683

Nggada, B. J. (2022). Induction of labour. In Current Challenges in Childbirth. IntechOpen. DOI: 10.5772/intechopen.104445

Negussie, Y., Geller, A., DeVoe, J. E., & National Academies of Sciences, Engineering, and Medicine. (2019). Healthy development from conception through early childhood. In Vibrant and healthy kids: Aligning science, practice, and policy to advance health equity. National Academies Press (US). https://doi.org/10.17226/25466

Olakotan, O. O., & Yusof, M. M. (2020). Evaluating the alert appropriateness of clinical decision support systems in supporting clinical workflow. Journal of biomedical informatics, 106, 103453. https://doi.org/10.1016/j.jbi.2020.103453

Roller, R., Burchardt, A., Samhammer, D., Ronicke, S., Duettmann, W., Schmeier, S., ... & Osmanodja, B. (2023). When performance is not enough—A multidisciplinary view on clinical decision support. Plos one, 18(4), e0282619. https://doi.org/10.1371/journal.pone.0282619

Sendelbach, S., & Funk, M. (2013). Alarm fatigue: a patient safety concern. AACN advanced critical care, 24(4), 378-386. https://doi.org/10.4037/NCI.0b013e3182a903f9

Sinno, Z. C., Shay, D., Kruppa, J., Klopfenstein, S. A., Giesa, N., Flint, A. R., ... & Poncette, A. S. (2022). The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study. Scientific Reports, 12(1), 21801. https://doi.org/10.1038/s41598-022-26261-4

Storm, J., & Chen, H. C. (2021). The relationships among alarm fatigue, compassion fatigue, burnout and compassion satisfaction in critical care and step‐down nurses. Journal of clinical nursing, 30(3-4), 443-453. https://doi.org/10.1111/jocn.15555

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y

Thayer, S. M., Faramarzi, P., Krauss, M. J., Snider, E., Kelly, J. C., Carter, E. B., ... & Raghuraman, N. (2023). Heterogeneity in management of category II fetal tracings: data from a multihospital healthcare system. American journal of obstetrics & gynecology MFM, 5(7), 101001. https://doi.org/10.1016/j.ajogmf.2023.101001

Wang, D. Y., Ding, J., Sun, A. L., Liu, S. G., Jiang, D., Li, N., & Yu, J. K. (2023). Artificial intelligence suppression as a strategy to mitigate artificial intelligence automation bias. Journal of the American Medical Informatics Association, 30(10), 1684-1692. https://doi.org/10.1093/jamia/ocad118

Winter, P. D., & Chico, T. J. (2023). Using the non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework to identify barriers and facilitators for the implementation of digital twins in cardiovascular medicine. Sensors, 23(14), 6333. https://doi.org/10.3390/s23146333

Wisner, K., Chesla, C. A., Spetz, J., & Lyndon, A. (2021). Managing the tension between caring and charting: Labor and delivery nurses' experiences of the electronic health record. Research in Nursing & Health, 44(5), 822-832. https://doi.org/10.1002/nur.22177

Zhao, Z., Zhang, Y., & Deng, Y. (2018). A comprehensive feature analysis of the fetal heart rate signal for the intelligent assessment of fetal state. Journal of clinical medicine, 7(8), 223. https://doi.org/10.3390/jcm7080223

Authors

Majeb Mubark Aldosari
Mogeba@Moh.Gov.Sa (Primary Contact)
Azzah Atitallah Ali   Alzahrani
Abdulrahman Saad   Almutairi
Rasha Saleh   ALhawass
Amira Nasser   Aljaber
Ahmed Essa Zalah
Wael Awad ALhaidari
Ohoud Sulaiman Alotibi
Ayshah Mohamed Mohamed Al Abdin
Abdulaziz Sori Almejlad
Aldosari, M. M., Alzahrani,A.A.A. , Almutairi,A.S. , ALhawass,R.S. , Aljaber,A.N. , Zalah, A. E., … Almejlad, A. S. (2024). The Double-Edged Sword of Clinical Decision Support in Labor & Delivery: A Systematic Review of its Impact on Nursing Judgment. Saudi Journal of Medicine and Public Health, 1(2), 711–718. https://doi.org/10.64483/jmph-183

Article Details

Similar Articles

<< < 1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)