NURS 718 Week 2 Discussion Board Forum 2 on Artificial Intelligence in Clinical Decision Support Example

Discussion Board Forum 2: Emerging Technology Trends in Decision Support

NURS 718 Informatics, Technology and Trends for Transforming Health Care, Discussion Board Forum 2

Thread by [Student Name]

The hospitals and the classmates replied to are composites written for a model discussion.

Last month a vendor presented software to our health system's technology committee that reads a non-contrast head CT for a patient with suspected stroke and sends an alert to the stroke team's phones within minutes if it detects signs of a large vessel occlusion. The presenter's message was simple: faster detection, faster transfer, better outcomes. My question was less simple, and it was the one no one in the room had asked. In our two smaller hospitals, which have no neurologist on site and a radiologist reading remotely overnight, what would an alert at 2 a.m. actually change, and who would be responsible for acting on it?

In Tan and Payton's (2010) account, an information system exists to support decisions, which means a system's value depends on the decision it supports and the people who make it. Image-based stroke triage software is best understood as an escalation signal, a second reader that never sleeps, not as a diagnosis. Its potential is greatest exactly where expertise is scarce: in small hospitals, a large vessel occlusion recognized twenty minutes sooner can mean a patient is sent for thrombectomy rather than missed. In our 25-bed critical access hospital, the emergency physician on a night shift may see one stroke a month, and the remote radiologist may be reading scans from six hospitals at once. A tool that flags the most urgent scan for immediate attention addresses a real bottleneck, but only if the flag reaches someone who can act on it.

The evidence is promising but incomplete. Zhou et al. (2026) pooled diagnostic accuracy studies in a Bayesian network analysis that set side by side four approaches to detecting anterior circulation occlusion on non-contrast CT: expert readers, non-expert readers, imaging-only AI and multimodal AI that also uses clinical information. They asked specifically whether current evidence supports prospective evaluation of AI-assisted escalation pathways, a careful framing that treats the tool as part of a pathway rather than a stand-alone answer. Gregorini et al. (2026) describe AI-assisted neuroimaging platforms as already integrated into clinical and telestroke workflows to support rapid triage, while noting that many prediction models in stroke still come from retrospective, single-center data and need prospective validation.

Before our system buys such a tool, four governance questions need answers. Who receives the alert and is accountable for responding at each hospital and hour? What happens when the software flags a scan the radiologist reads as normal, or misses one the radiologist catches? How will false positives and negatives be tracked and reviewed? And how will nurses and emergency physicians be trained to treat the alert as a prompt rather than a verdict? An alert that no one owns is not decision support; it is noise with a timestamp.

Paul urged the Thessalonians to test everything and hold fast to what is good (English Standard Version Bible, 2001/2016, 1 Thessalonians 5:21). That is sound counsel for technology adoption. My position is that our system should adopt AI triage only within a defined telestroke pathway, with named responders, a monitoring plan and a review process, and should evaluate its effect on time to transfer rather than assume it. I would want six months of data on door-in-door-out times and alert accuracy before the system decides whether to expand the tool beyond the pilot hospital.

Reply to Samantha

Samantha, you argued that small hospitals should simply join a telestroke network and let the hub decide everything, since the hub has the neurologists and the experience. I agree that a network is the foundation, but recent guidance suggests the model has to fit local resources. A World Stroke Organization scientific statement on telestroke in resource-limited settings notes that most recommendations come from high-income countries and resource-intensive models that may not transfer directly, and it offers pragmatic recommendations on organization, setup and quality assurance drawn from a systematic review, a survey and expert consensus (Tunkl et al., 2026). It also defines telestroke as extending beyond hyperacute decisions to support for stroke unit care. Tan and Payton (2010) would call that an adaptive system: one shaped to the organization rather than imported whole. For your hospital, that could mean agreeing with the hub on which decisions stay local, such as swallow screening and early mobility, and which go to the remote neurologist. The statement's emphasis on quality assurance also matters for small sites, since a network that never reviews its own cases cannot tell whether remote decisions are working. Has your hospital mapped which parts of stroke care it can sustain on its own?

Reply to Luis

Luis, you raised a point I had not considered: technology fails if staff do not trust or understand it. A survey of 135 health professionals in a Greek island stroke network found fairly high stroke knowledge but a gap between how much staff valued telemedicine and how well they thought it fit local workflow and visibility, and only 63% had ever seen a telemedicine system in use (Chatzidaki et al., 2026). That finding suggests that before go-live, staff need to see the system work, ideally in simulated cases, not only hear about it. In our setting, I would add an AI triage alert to a monthly stroke simulation so nurses and physicians practice responding to it. Zaccagnini and White (2017) describe the DNP's role in evaluating technology in context, and readiness is part of context. The same survey found that prior exposure was linked to more positive attitudes, at least before other factors were accounted for, which suggests that seeing the system work may be as persuasive as any training session. What would it take in your emergency department to run a stroke simulation that includes the new technology before it goes live?

References

Chatzidaki, S., Anargyros, K., & Xanthos, T. (2026). Human readiness for stroke telemedicine in a Greek island network: A cross-sectional study. Telemedicine and e-Health. Advance online publication. https://doi.org/10.1177/15305627261492660

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Gregorini, M., Lorusso, L., Airoldi, L., Stefano, M. D., Formenti, A., Lucchi, G., Melzi, P., Perego, E., Tagliabue, E., Tetto, A., & Vaccaro, M. (2026). Artificial intelligence and emerging digital technologies across the stroke continuum: From risk prediction to real-time monitoring and rapid response. Medicina, 62(7). https://doi.org/10.3390/medicina62071254

Tan, J. K. H., & Payton, F. C. (2010). Adaptive health management information systems: Concepts, cases, and practical applications (3rd ed.). Jones and Bartlett.

Tunkl, C., Carbonera, L. A., Hubert, G., Agarwal, A., Adcock, A. K., Liberato, B., Yperzeele, L., Roushdy, T., Collantes, E. M., Bonifacio Delgadillo, D. M., Bladin, C., & Ranta, A. (2026). Telestroke care in resource-limited settings: A World Stroke Organization scientific statement. International Journal of Stroke. Advance online publication. https://doi.org/10.1177/17474930261480603

Zaccagnini, M. E., & White, K. W. (Eds.). (2017). The doctor of nursing practice essentials: A new model for advanced practice nursing (3rd ed.). Jones & Bartlett Learning.

Zhou, H., Liu, L., Fu, B., Cao, B., Ma, J., Yang, K., Bao, Y., Yang, F., & Lian, X. (2026). AI decision support for emergency triage of large vessel occlusion using noncontrast computed tomography: Systematic review and Bayesian diagnostic test accuracy network meta-analysis. Journal of Medical Internet Research, 28, Article e105068. https://doi.org/10.2196/105068