Recommendations for nursing practice CONCLUSION ∙ Integrating AI into nursing practice can streamline workflows

The gap

Evidence profile

Sourced from the recommendations and future work of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 5 journals. Those papers have been cited 70 times in total.

Supporting evidence — 6 representative gaps

• Radiology artificial intelligence for prioritized imaging and diagnosis of lung cancer: qualitative interview analysis of stakeholder perspectives in Northern Ireland (2026) · Frontiers in Medicine · doi

  - Patients and clinicians highlight the importance of person-to- person communication, and many have had excellent experiences in hospital settings. Measures should be in place to ensure that this will not be impacted by AI integration. - Delays in time to scan and referral were noted – any dissatisfac- tion in the current system were related to workflow issues, rather than staff interaction. AI may have a place in workflow automa- tion, and this may be a priority in AI implementation in lung cancer pathway. - Patients and clinicians both express a desire to be informed of the performance of any AI model used in clinical practice. Developers should provide transparent data in relation to the performance of their AI solution and clinicians using the system (or appointed ‘AI champions’) should be able to interrogate performance metrics. Training data and transparency of AI technology is important to both clinicians and members of the public to ensure trust in the technology. - There were mixed opinions on the need for patients to be informed about the use of AI in their care, from both clinicians and public indicating the need for further research in this area. In the interim, this should be considered on a local level to ensure that due procedure is being followed in relation to legal and ethi- cal frameworks. - Acceptability of AI amongst clinicians and the public could be propelled by training and education. Particularly, case-based training/practical training is desired by clinicians alongside AI integration. Training in AI is also desired by the general public. Social media has been proposed as means to do this, however further research should be conducted into its use in this popula- tion and in this subject, which is already fraught with misinforma- tion and media hype. Formation of specialist groups of clinicians in professional bodies, working in tandem with AI system experts should provide accessible information to patients to meet this need. Further co-design of education tools should be considered to meet this objective.

  generalrecommendations
  Keywords: clinicians training patients tion public ensure system performance need further person place integration workflow informed
• C8 Health, a Platform for the Implementation of Best Practices: Survey-Based Usability Study. (2026) · JMIR Human Factors · doi

  In future research, quantifying improvements in patient care and institutional productivity following implementation of the app may assist in valuing the app in the context of outcomes and efficiency metrics. There is also a need for multicen- ter and longitudinal studies to assess sustained use. Future research should explore the integration of C8 Health with electronic health record systems to create a more compre- hensive clinical decision support ecosystem. This integration could enable automated protocol selection based on patient characteristics, streamlined documentation workflows, and enhanced tracking of clinical outcomes. Future studies should also incorporate prospective pre-post designs with baseline data collection before implementation to better isolate the platform’s effect on clinical workflow and protocol adher- ence. Overall, the strong usability and perceived value of the C8 Health app pave the way for it to become a transforma- tive tool in perioperative care, setting a new standard for technology-driven support in anesthesiology and beyond.

  generalfuture work
  Keywords: future health clinical patient care implementation outcomes integration support protocol quantifying improvements institutional productivity following
• Advancing Patient-Centered Nursing Practices Through AI-Driven Clinical Decision Support Systems and Personalized Care Plans (2026) · International Journal of Computer Applications Technology and Research · doi

  Importantly, the implementation of AI in nursing informatics requires robust change management strategies to ensure clinical staff are confident in interpreting and applying [9]. When properly system-generated integrated, as demonstrated by diverse pilot studies, AI tools can seamlessly complement existing workflows without introducing cognitive overload or excessive alert fatigue [8]. This adaptability has been a decisive factor in sustaining adoption rates across multiple clinical environments and in ensuring positive nurse perceptions of AI-CDSS utility. 2.3 Evidence for Personalised Care Plans The evidence base for AI-enabled personalised care plans is growing, with multiple studies highlighting improvements in both clinical outcomes and patient satisfaction when CDSS tools are tailored to individual needs [12]. Unlike generic protocols, personalised care plans leverage AI to account for a patient’s comorbidities, lifestyle factors, and historical treatment thereby producing more nuanced responses, recommendations [7]. In cardiovascular care, AI-CDSS platforms have been shown to optimise medication regimens by balancing clinical efficacy with patient-reported side effects [9]. Similarly, in oncology nursing, predictive algorithms can anticipate a patient’s tolerance to specific chemotherapy cycles, enabling supportive care adjustments that improve adherence rates [10]. These systems also provide dynamic updates to care plans as new clinical data become available, ensuring that interventions remain aligned with a patient’s evolving condition [8]. incorporate communication modules From a patient engagement perspective, AI-personalised plans often that deliver targeted education materials and self-management prompts [13]. These resources are automatically adjusted based on a patient’s health literacy level, cultural background, and preferred communication channels, to higher satisfaction and better adherence to prescribed regimens [11]. leading Evidence also indicates that such systems can significantly reduce hospital readmissions. For example, in patients with heart failure, AI-generated discharge instructions personalised to the patient’s biomarker trends and home environment have resulted in measurable declines in 30-day readmission rates [12]. pathways As illustrated in Figure 1, the integration of AI into the care planning process represents a logical culmination of decades of technological evolution in CDSS. The transition from rigid, standardised personalised to recommendations reflects a broader movement in healthcare toward precision nursing. This shift ensures that care delivery is not only clinically sound but also contextually relevant, supporting bot

  generalrecommendations
  Keywords: care patient personalised clinical plans cdss nursing rates evidence management generated tools multiple ensuring satisfaction
• Advancing Patient-Centered Nursing Practices Through AI-Driven Clinical Decision Support Systems and Personalized Care Plans (2026) · International Journal of Computer Applications Technology and Research · doi

  allowing them to interpret why a system suggests a particular care pathway [35]. This interpretability strengthens trust, as transparent www.ijcat.com 213 International Journal of Computer Applications Technology and Research Volume 12–Issue 12, 202 – 217, 2023, ISSN:-2319–8656 DOI:10.7753/IJCATR1212.1021 nurses can cross-reference machine outputs with clinical judgment before implementing interventions. One promising direction is federated learning, which enables AI models to be trained across multiple healthcare institutions without directly sharing patient data [33]. This method preserves privacy while pooling knowledge from diverse populations, ultimately improving predictive accuracy and reducing the risk of biased recommendations. context-aware Enhanced model architectures now combine predictive analytics with enabling recommendations that account for both structured clinical data integrating and unstructured narrative notes contextual insights, AI-CDSS can better adapt to nuanced patient needs in nursing practice. reasoning, [37]. By Moreover, advances in continuous learning frameworks allow models to update with new evidence or local practice changes without requiring complete retraining [40]. This dynamic adaptability aligns with nursing’s need for up-to-date, evidence-informed decision-making. As illustrated in Figure 5, these innovations are not isolated; they interact within an evolving AI-personalised nursing ecosystem that merges data science with frontline clinical workflows [34]. Collectively, these advancements mark a transition from static, opaque tools toward transparent, adaptable AI solutions that enhance nursing autonomy. 8.2 Integration with IoT and Remote Monitoring The fusion of AI-CDSS with Internet of Things (IoT) technologies is enabling personalised care to extend beyond the hospital into home-based follow-up. IoT devices such as wearable heart monitors, glucose sensors, and smart pill dispensers can transmit continuous streams of patient data to nursing teams for real-time monitoring [38]. of signs AI algorithms process these incoming data to detect early warning proactive deterioration, interventions that may prevent hospital readmissions [36].

  generalrecommendations
  Keywords: nursing clinical patient care transparent interventions learning models without predictive recommendations enabling cdss practice continuous
• The evolving role of nursing informatics in the era of artificial intelligence (2025) · International Nursing Review · cited 70× · doi

  Recommendations for nursing practice CONCLUSION ∙ Integrating AI into nursing practice can streamline work- flows and improve patient outcomes by automating routine tasks and enhancing decision-making through predictive analytics (Gopal et al., 2019). ∙ Collaboration between nurses, data scientists, and health- care professionals is essential for successfully adopting AI tools. Effective communication and teamwork will be key to maximizing the benefits of AI in clinical practice. ∙ Nursing informatics and AI can directly impact patient care. Real-world examples of successful implementations demonstrate improved diagnosis, treatment planning, and personalized care approaches.

  generalrecommendations
  Keywords: nursing practice care patient recommendations conclusion integrating streamline flows improve outcomes automating routine tasks enhancing
• Effects of Type and Timing of Clinician-Facing AI Support on Patient Trust in Medical Consultations: 2 Vignette Experiments (2026) · Journal of Medical Internet Research · doi

  Future research should examine more explicitly how physician communication and disclosure strategies shape patient responses to clinician-facing AI-CDSS. In particular, it would be valuable to disentangle the effects of AI use itself from the effects of how AI use and results are communicated to patients. Relatedly, future research should also examine whether responses to different forms of AI-assisted work- flows vary as a function of individual differences, alignment of AI results with the physician’s assessment, and the specific health outcome (ie, favorable vs unfavorable). Additionally, studies in real clinical settings that obtain direct patient feedback on different AI-CDSS workflows, test the feasibil- ity and desirability of sequential decision-making processes, and measure actual behavior rather than behavioral intentions would provide valuable insight. Ultimately, multistakeholder research is needed to ensure that AI-CDSS are not only technically effective but also trusted and accepted in clinical practice by all human stakeholders.

  generalfuture work
  Keywords: cdss future examine physician patient responses valuable effects different clinical explicitly communication disclosure strategies shape

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