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The aim of this study is to assess nurse practitioner students' perceptions and engagement with Isabel's artificial intelligence (AI) based differential diagnosis tool to support their decision-making skills during their theoretical and clinical placement training.
This pilot study used a cross-sectional design.
Twenty-six nurse practitioner students provided feedback on their use of an AI differential diagnosis tool in both academic and clinical contexts. This survey used the Post-Study System Usability Questionnaire to assess the engagement levels and usability of the AI tool. Additional questions were included to evaluate the usage patterns, adequacy in training and confidence in diagnosis.
There were mixed engagement levels: 44.4% (n = 8/18) used Isabel in two subjects—typically one or both clinical placement units—and 27.8% (n = 5/18) in one subject; students most often used the tool to confirm differential diagnoses. Usability was rated positively with the disease ranking, red flag diagnosis and link to national guideline features demonstrating the highest student usage. While most students found the tool beneficial to use during clinical placement and completing university assignments, some reported challenges due to insufficient training, impacting confidence in clinical application.
Isabel has potential as a valuable educational tool in Nurse Practitioner programs, but successful implementation depends on adequate training and support. The findings highlight the importance of comprehensive training and support to maximise AI tool utilisation, with direct implications for programme curricula, clinical education strategies and potential improvements in diagnostic reasoning skills for future nurse practitioners.
This study provides an example of integrating artificial intelligence (AI) guided clinical decision-making training in nurse practitioner (NP) education. The findings can be used by educational institutions to trial similar AI-integrated learning approaches, enhancing diagnostic competence and potentially improving patient care outcomes.
The Study adhered to the STROBE checklist for reporting.
No patient or public contribution was made to this study.
To evaluate the acceptability and feasibility among nurses of Decubitus Risk Prediction Alerts based on Artificial Intelligence (DRAAI), and to assess the feasibility of the implementation plan.
A process evaluation of a pilot implementation study using mixed methods.
Acceptability and feasibility of DRAAI among nurses from three general wards in a university hospital was assessed via questionnaire. The tailored implementation plan included thirteen strategies distributed over six domains, such as facilitation, continuous evaluation, and educational sessions. Adaptations, acceptability, and feasibility were recorded in field notes.
Fifty-five nurses completed the questionnaire and valued DRAAI's predictions, believing these could contribute to pressure ulcer (PU) prevention. Some initially faced challenges distinguishing between PU risk and PU detection. Most found it feasible to integrate DRAAI into their workflow. Adaptations included adding PU preventive measures to educational sessions and sharing frequently asked questions and answers. Overall, implementation efforts were feasible. DRAAI generated PU risk predictions for 428 unique admitted patients; 128 (30%) patients received at least one at-risk prediction. Regarding fidelity, nearly 80% (101/128) of at-risk predictions were followed by a nursing care plan.
Ongoing involvement and clear communication were crucial for successfully integrating AI into nursing workflows. Although some nurses were concerned that DRAAI might miss at-risk patients, they continued to independently identify at-risk patients.
Implementation of DRAAI served as a prompt for nurses to focus more on PU prevention. While DRAAI shows promise in improving PU prevention, future research is needed to evaluate its clinical impact.
Addressed the challenge of identifying patients at risk for developing pressure ulcers. Demonstrated feasibility and acceptability of implementing AI in clinical practice. Highlighted the need for ongoing support and communication for successful implementation.
None.
Standard for Reporting Implementation Studies (StaRI).
To test the feasibility and acceptability of a newly developed model of neonatal nurse-controlled analgesia to manage pain in the post-operative infant.
The study utilised a single-centre two-arm parallel, unblinded randomised controlled external pilot trial design.
The pilot trial was conducted in a surgical neonatal tertiary intensive care unit in Brisbane, Australia. Eligible infants were randomised to receive either post-operative pain management care via a model of neonatal nurse-controlled analgesia or standard care. Feasibility and acceptability were the primary outcomes. Seven feasibility outcomes were assessed by a traffic light system to delineate progression to a larger trial. Acceptability and clinical utility of the model of care by staff were assessed by feedback from an anonymous questionnaire that was administered at the completion of the trial period. Secondary outcomes included parental attitudes and perceptions of post-operative pain management to help establish primary outcomes for a larger randomised controlled trial.
Overall staff found the formalised model beneficial for managing post-operative pain but found the complexity of the model and ability to titrate analgesia based only on documented pain scores barriers requiring further consideration. Three of the seven feasibility outcomes failed to reach ‘greenlight’ targets to progress to a larger trial with adherence to the model, and the proportion of eligible infants not recruited was allocated a ‘redlight’. Secondary outcomes were comparable and support future study.
This pilot feasibility study has shown that a model of neonatal nurse-controlled analgesia can be safely implemented and utilised in the post-operative care of the surgical neonate. Further exploration of the barriers to model adherence and recruitment is warranted before a future larger trial is undertaken.
Though not all primary outcomes reached an acceptable range for further progression, this pilot feasibility study provided invaluable learning and has provided direction for future research into the provision of a family integrated and responsive model of analgesia.
This study is reported in line with the Consolidated Standards of Reporting Trials (CONSORT): Extension to randomised pilot and feasibility trial and the TIDieR Checklist (Template for Intervention, Description and Replication).
No patient or public contribution was utilised for this study.
Trial Registration: ACTRN12623000643673—the trial was prospectively registered
The aim of this study was to evaluate the feasibility, acceptability and preliminary effectiveness of I-PASS-structured (Identification—Patient—Action—Situation—Synthesis) bedside nursing handovers on the handover global quality and the patients trust in nurses.
Oral end-of-shift nursing handovers can become moments of patient vulnerability. Moving handovers from nurses' offices to patients' bedsides is a means of improving them; however, implementing this remains a challenge.
This was a Type-1 effectiveness–implementation hybrid study.
We measured the effectiveness using a simple interrupted time series with three measurement points before and after the introduction of I-PASS-structured bedside nursing handovers between August and November 2022. Implementation was explored using multi-method measurements of quantitative and qualitative data. As an implementation strategy, we developed a specific training session, including simulations.
Bedside nursing handovers were introduced into one surgery and one medicine ward, with the 831 handovers evaluated showing significant improvements in handover quality compared to before implementation, although handover duration increased. Patient outcomes validated this change in nursing practice. However, examining nurses' perspectives of the implementation process revealed several obstacles to using bedside nursing handovers that training alone was not strong enough to overcome.
Given the findings of the present project, the use of bedside nursing handovers should be extended to other units by developing strategies that will make the practice sustainable.
Bedside nursing handovers improved handover quality and created a true partnership with the patient: nurses feel more confident about seeing the patient quickly. Patients felt more taken into consideration and safer.
For feasibility reasons, patients and the public were not involved in the design, conduct, reporting or dissemination plans of this research. The trial was prospectively registered before the first participant was recruited under the ISRCTN # 81701569.
To evaluate the acceptability and safety of a fluid volume assessment tool integrating clinical signs, patient-reported symptoms and bioimpedance analysis as a clinical decision aid in haemodialysis.
Single-centre, 6-month clinical implementation feasibility pilot study.
A convenience sample of 50 healthcare staff and 50 hospitalised haemodialysis patients was recruited. The intervention involved a fluid volume assessment tool combining clinical signs, patient-reported symptoms and bioimpedance analysis. We utilised the Template for Intervention Description and Replication (TIDieR) checklist to describe the intervention. Nurses used a decision algorithm to guide them in setting the ultrafiltration target. Staff acceptability was assessed using the NoMAD survey, and safety outcomes, including intradialytic hypotension, intradialytic events and fluid overload, were monitored. Trends between Charlson Comorbidity Index scores and safety risks were explored.
The tool achieved high acceptability among staff, with cognitive participation (100%) and collective action (92%) being the strongest domains. Safety analysis indicated minimal adverse events (intradialytic hypotension: 10%, intradialytic events: 12%). Participants with higher Charlson Comorbidity Index scores (> 6) were more likely to experience intradialytic hypotension or intradialytic events, highlighting the need for tailored approaches for these populations.
The tool was acceptable, safe and feasible, empowering dialysis nurses to deliver real-time, individualised fluid management, reducing dependency on nephrologists and addressing operational challenges in the acute setting.
The tool promotes nursing autonomy, enhances care efficiency and ensures safe, patient-centred fluid management in resource-limited settings.
Addresses fluid management challenges in haemodialysis care through introduction of an evidence-based fluid assessment tool. Support a scalable nurse-led protocol for ultrafiltration management, promoting patient safety and workflow efficiency in acute dialysis settings.
No patient or public contribution was required for this work.
To describe a knowledge translation capacity-building initiative and illustrate the roles of nurses in practice change using an exemplar case study.
The report uses observational methods and reflection.
The Knowledge Translation Challenge program involves a multi-component intervention across several sites. The advisory committee invited eligible teams to attend capacity-building workshops. Implementation plans were developed, and successful teams receive funding for a 2 year period. Evaluation involved collecting data on program uptake and impact on practice change. Data has been collected from five cohorts. The exemplar case study employed an action-research framework.
Four nurse-led teams have demonstrated successful implementation of their practice change. The case study on implementing a clinical toolkit for clozapine management further illustrates a thoughtful planning process, and implementation journey and learnings by a team of nurses.
The Knowledge Translation Challenge program empowers nurses to use implementation science practices to enhance the quality and effectiveness of healthcare services. Success of this initiative serves as a model for addressing the persistent gap between knowledge and practice in clinical settings and the value of activating nurses to help close this gap.
As the most trusted and numerous profession, it is vital that nurses contribute to efforts to translate research evidence into clinical practice. The Knowledge Translation Challenge program supports nurses to lead practice change.
The Knowledge Translation Challenge program successfully equips nurses and other health care providers with the knowledge, skills and resources to implement practice improvements which enhance the quality and effectiveness of healthcare services and nursing practice.
The Knowledge Translation Challenge advisory committee has three patient-public partners that support teams to develop a patient-oriented approach for their projects by providing feedback on the implementation plans. Each team was also supported to include patient-public partners on their project.
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