The Impact of Radiology-Laboratory Data Fusion on Nursing Decision-Making in Critical Care Settings: A Systematic Review
Abstract
Background: The intensive care unit produces large volumes of high-stakes patient data from multiple sources. The critical care nurse plays the role of primary caregiver amidst a mentally taxing process of manually integrating siloed radiological and laboratory information from different electronic health record systems, which are prone to errors and overload due to high-pressure environments.
Aim: The aim of this review was to systematically examine the available evidence regarding the use of radiology-laboratory data fusion platforms in nursing decision-making within a critical care environment: applications, effects, and implementation challenges.
Methods: A systematic literature search was carried out from the following databases: PubMed/MEDLINE, CINAHL, Scopus, and Web of Science, between the years 2000 and 2024.
Results: There is evidence that clinical data fusion significantly enhances situational awareness, diagnostic accuracy, and speed to intervention by offering integrated visualization of correlated data. Applications in sepsis, acute kidney injury, and neurological emergencies have demonstrated particular benefit for early detection and protocol activation. Implementation faces substantial challenges, including interoperability barriers, alert fatigue risks, and workflow integration requirements.
Conclusion: The fusion of radiology and laboratory data possesses profound potential to transform critical care nursing in ways that will finally support a more holistic clinical decision-making approach. This requires nurse-centric design, vigorous training programs, and evidence-based implementation guidelines in order to overcome barriers involving technology and human factors.
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References
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
Ash, J. S., Berg, M., & Coiera, E. (2004). Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. Journal of the American Medical Informatics Association, 11(2), 104-112. https://doi.org/10.1197/jamia.M1471
Bates, D. W., Auerbach, A., Schulam, P., Wright, A., & Saria, S. (2020). Reporting and implementing interventions involving machine learning and artificial intelligence. Annals of internal medicine, 172(11_Supplement), S137-S144. https://doi.org/10.7326/M19-0872
Bedoya, A. D., Clement, M. E., Phelan, M., Steorts, R. C., O’brien, C., & Goldstein, B. A. (2019). Minimal impact of implemented early warning score and best practice alert for patient deterioration. Critical care medicine, 47(1), 49-55. DOI: 10.1097/CCM.0000000000003439
Belle, A., Thiagarajan, R., Soroushmehr, S. R., Navidi, F., Beard, D. A., & Najarian, K. (2015). Big data analytics in healthcare. BioMed research international, 2015(1), 370194. https://doi.org/10.1155/2015/370194
Ben-Assuli, O., Shabtai, I., & Leshno, M. (2013). The impact of EHR and HIE on reducing avoidable admissions: controlling main differential diagnoses. BMC medical informatics and decision making, 13(1), 49. https://doi.org/10.1186/1472-6947-13-49
Benner, P., Hughes, R. G., & Sutphen, M. (2008). Clinical reasoning, decisionmaking, and action: Thinking critically and clinically. Patient safety and quality: An evidence-based handbook for nurses.
Cho, I., Choi, W. J., & Choi, W. H. (2014). Usability Testing and Comparison of Six Electronic Nursing Record Systems: User-Task-System Evaluation. CIN: Computers, Informatics, Nursing, 32(8), 364. DOI: 10.1097/01.NCN.0000453182.72347.7c
Collins, S. A., Cato, K., Albers, D., Scott, K., Stetson, P. D., Bakken, S., & Vawdrey, D. K. (2013). Relationship between nursing documentation and patients’ mortality. American Journal of Critical Care, 22(4), 306-313. https://doi.org/10.4037/ajcc2013426
Dowding, D., Randell, R., Gardner, P., Fitzpatrick, G., Dykes, P., Favela, J., ... & Currie, L. (2015). Dashboards for improving patient care: review of the literature. International journal of medical informatics, 84(2), 87-100. https://doi.org/10.1016/j.ijmedinf.2014.10.001
Endsley, M. R. (2015). Final reflections: Situation awareness models and measures. Journal of Cognitive Engineering and Decision Making, 9(1), 101-111. https://doi.org/10.1177/1555343415573911
Fan, E., Del Sorbo, L., Goligher, E. C., Hodgson, C. L., Munshi, L., Walkey, A. J., ... & Brochard, L. J. (2018). Mechanical ventilation in adults with acute respiratory distress syndrome: An official clinical guideline of American Thoracic Society/European Society of Intensive Care medicine/Society of Critical care medicine. Pulmonologiya, 28(4), 399-410. https://doi.org/10.18093/0869-0189-2018-28-4-399-410
Funk, M., Fennie, K. P., Stephens, K. E., May, J. L., Winkler, C. G., & Drew, B. J. (2017). Association of implementation of practice standards for electrocardiographic monitoring with nurses’ knowledge, quality of care, and patient outcomes: findings from the Practical Use of the Latest Standards of Electrocardiography (PULSE) Trial. Circulation: cardiovascular quality and outcomes, 10(2), e003132. https://doi.org/10.1161/CIRCOUTCOMES.116.003132
Gibson, C. M., Holmes, D., Mikdadi, G., Presser, D., Wohns, D., Yee, M. K., ... & Krucoff, M. W. (2019). Implantable cardiac alert system for early recognition of ST-segment elevation myocardial infarction. Journal of the American College of Cardiology, 73(15), 1919-1927. https://doi.org/10.1016/j.jacc.2019.01.014
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: a systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121-127.https://doi.org/10.1136/amiajnl-2011-000089
Graber, M. L., Kissam, S., Payne, V. L., Meyer, A. N., Sorensen, A., Lenfestey, N., ... & Singh, H. (2012). Cognitive interventions to reduce diagnostic error: a narrative review. BMJ quality & safety, 21(7), 535-557. https://doi.org/10.1136/bmjqs-2011-000149
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., Hinder, S., ... & Shaw, S. (2017). Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of medical Internet research, 19(11), e8775. https://doi.org/10.2196/jmir.8775
Henneman, E. A., Gawlinski, A., & Giuliano, K. K. (2012). Surveillance: a strategy for improving patient safety in acute and critical care units. Critical Care Nurse, 32(2), e9-e18. https://doi.org/10.4037/ccn2012166
Hripcsak, G., Duke, J. D., Shah, N. H., Reich, C. G., Huser, V., Schuemie, M. J., ... & Ryan, P. B. (2015). Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Studies in health technology and informatics, 216, 574. https://pubmed.ncbi.nlm.nih.gov/26262116/
Hulsen, T., Jamuar, S. S., Moody, A. R., Karnes, J. H., Varga, O., Hedensted, S., ... & McKinney, E. F. (2019). From big data to precision medicine. Frontiers in medicine, 6, 34. https://doi.org/10.3389/fmed.2019.00034
Hwang, E. J., Park, S., Jin, K. N., Kim, J. I., Choi, S. Y., Lee, J. H., ... & Park, C. M. (2019). DLAD Development and Evaluation Group. Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open, 2(3), e191095.
Ienca, M., Schneble, C., Kressig, R. W., & Wangmo, T. (2021). Digital health interventions for healthy ageing: a qualitative user evaluation and ethical assessment. BMC geriatrics, 21(1), 412. https://doi.org/10.1186/s12877-021-02338-z
Kellum, J. A., Lameire, N., & KDIGO AKI Guideline Work Group. (2013). Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Critical care, 17(1), 204. https://doi.org/10.1186/cc11454
Lenhart, M. K., Savitsky, E., & Eastridge, B. (Eds.). (2012). Combat casualty care: Lessons learned from OEF and OIF. Government Printing Office.
Levy, M. M., Evans, L. E., & Rhodes, A. (2018). The surviving sepsis campaign bundle: 2018 update. Intensive care medicine, 44(6), 925-928. https://doi.org/10.1007/s00134-018-5085-0
Lopez, K. D., Yao, Y., Cho, H., Dos Santos, F. C., Madandola, O. O., Bjarnadottir, R. I., ... & Keenan, G. M. (2022). Conducting a representative national randomized control trial of tailored clinical decision support for nurses remotely: methods and implications. Contemporary clinical trials, 118, 106712. https://doi.org/10.1016/j.cct.2022.106712
Mehta, R. L., Cerdá, J., Burdmann, E. A., Tonelli, M., García-García, G., Jha, V., ... & Remuzzi, G. (2015). International Society of Nephrology's 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): a human rights case for nephrology. The Lancet, 385(9987), 2616-2643. https://doi.org/10.1016/S0140-6736(15)60126-X
Potter, P., Wolf, L., Boxerman, S., Grayson, D., Sledge, J., Dunagan, C., & Evanoff, B. (2005). Understanding the cognitive work of nursing in the acute care environment. JONA: the journal of nursing administration, 35(7), 327-335.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., ... & Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ digital medicine, 1(1), 18. https://doi.org/10.1038/s41746-018-0029-1
Raita, Y., Camargo Jr, C. A., Liang, L., & Hasegawa, K. (2021). Big data, data science, and causal inference: a primer for clinicians. Frontiers in Medicine, 8, 678047. https://doi.org/10.3389/fmed.2021.678047
Rodriguez, A. C., Lee, D. A., & Makic, M. B. F. (2017). Situational awareness in critical care: An aviation approach to reduce error. Journal of PeriAnesthesia Nursing, 32(6), 650-652. https://doi.org/10.1016/j.jopan.2017.08.001
Sánchez, B., Ferrer, R., Suarez, D., Romay, E., Piacentini, E., Gomà, G., ... & Edusepsis Study Group. (2017). Declining mortality due to severe sepsis and septic shock in Spanish intensive care units: A two-cohort study in 2005 and 2011. Medicina intensiva, 41(1), 28-37. https://doi.org/10.1016/j.medin.2016.09.004
Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, D., Biessmann, F., & Wolf-Ostermann, K. (2021). Application scenarios for artificial intelligence in nursing care: rapid review. Journal of medical Internet research, 23(11), e26522. https://doi.org/10.2196/26522
Singh, H., Meyer, A. N., & Thomas, E. J. (2014). The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ quality & safety, 23(9), 727-731. https://doi.org/10.1136/bmjqs-2013-002627
Trbovich, P., Prakash, V., Stewart, J., Trip, K., & Savage, P. (2010). Interruptions during the delivery of high-risk medications. JONA: The Journal of Nursing Administration, 40(5), 211-218. DOI: 10.1097/NNA.0b013e3181da4047
Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., ... & Liu, H. (2018). Clinical information extraction applications: a literature review. Journal of biomedical informatics, 77, 34-49. https://doi.org/10.1016/j.jbi.2017.11.011
West, V. L., Borland, D., & Hammond, W. E. Innovative information visualization of electronic health record data: a systematic. wounds (figure 2), 7, 8. doi:10.1136/amiajnl-2014-002955
Wu, J., Jin, Y. U., Li, H., Xie, Z., Li, J., Ao, Y., & Duan, Z. (2015). Evaluation and significance of C-reactive protein in the clinical diagnosis of severe pneumonia. Experimental and therapeutic medicine, 10(1), 175-180. https://doi.org/10.3892/etm.2015.2491
Xu, X., Mangina, E., & Campbell, A. G. (2021). HMD-based virtual and augmented reality in medical education: a systematic review. Frontiers in Virtual Reality, 2, 692103. https://doi.org/10.3389/frvir.2021.692103
Yaraghi, N., Du, A. Y., Sharman, R., Gopal, R. D., & Ramesh, R. (2015). Health information exchange as a multisided platform: Adoption, usage, and practice involvement in service co-production. Information Systems Research, 26(1), 1-18. https://doi.org/10.1287/isre.2014.0547
Young, J. Q., Van Merrienboer, J., Durning, S., & Ten Cate, O. (2014). Cognitive load theory: implications for medical education: AMEE Guide No. 86. Medical teacher, 36(5), 371-384. https://doi.org/10.3109/0142159X.2014.889290
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Copyright (c) 2024 Shaykhah Nazal Naqaa Alshammri, Abdullah Salem Abdulaziz Al-Malq, Zami Obaid H Alshammari, Mohammed Faydh Hamdan Alanazi, Sultan Nahar Al Shammari, Khalid Salem Almuhawwis, Maher Abbas Alshammari, Hamoud Awadh Al Enazi, Salem Abdulaziz Alnazhah
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