Nursing leadership and performance metrics in artificial intelligence integrated gastrointestinal surgical care: A narrative review

Table 3 Nursing-sensitive performance domains for evaluating artificial intelligence integrated gastrointestinal and complex surgical care

Performance domain	Definition	Clinical relevance	Suggested indicators
Workflow efficiency[14-16,47-49]	The extent to which AI supports prioritization without adding unnecessary digital burden	Surgical nurses often manage multiple unstable patients, drains, wounds, medications, mobilization targets, and discharge tasks during the same shift	Time spent retrieving risk information; duplicate documentation steps; task completion rates; staff-reported usability; time from alert visibility to review
Response-time optimization[44,45,47-49]	The interval between AI-generated concern and meaningful clinical action	Delays in response may worsen outcomes in sepsis, anastomotic leak, bleeding, respiratory compromise, or organ dysfunction	Time from alert to bedside reassessment; time from nursing concern to surgical review; time from review to intervention; proportion of high-priority alerts acted on within target time
Early complication recognition[5-9,25-30,44,45]	Earlier identification of deterioration before crisis-level presentation	Complications after colorectal, pancreatic, and hepatobiliary surgery often develop through subtle trends rather than one obvious sign	Time from first abnormal trend to escalation; proportion of complications preceded by documented nursing concern; unplanned ICU transfers; failure-to-rescue events
Adherence to risk-stratified pathways[10-13]	Alignment of nursing care with the patient’s predicted and evolving risk profile	High-risk patients may require intensified monitoring, earlier allied health input, or deviation from standard enhanced recovery pathways	Completion of individualized observation plans; adherence to escalation thresholds; timely nutrition, physiotherapy, wound, or stoma referral; documented pathway deviations
Documentation accuracy[17-22]	Completeness and consistency of clinically relevant nursing documentation following AI-supported alerts or risk classifications	AI systems and clinical teams both depend on accurate documentation of observations, actions, and reasoning	Proportion of AI-supported alerts with documented nursing reassessment and response; completeness of predefined clinical fields relevant to the identified risk; documentation of escalation and subsequent action; missing or incomplete required entries, assessed through structured record audit
Interdisciplinary communication[47-49]	Timely and documented transfer of clinically relevant AI-derived risk information between nurses and other professionals involved in the patient’s care	AI-derived risk information is clinically useful only if it reaches the right professionals in time	Proportion of relevant handovers containing AI-derived risk information; proportion of escalations with documented acknowledgment by the receiving team; time from nursing escalation to team acknowledgment; documentation of agreed actions or follow-up
Cognitive workload and alert burden[50-53]	The effect of AI tools on mental workload, interruption, and alarm fatigue	Excessive or poorly explained alerts may reduce trust and delay response to clinically important signals	Number of alerts per shift; proportion of actionable alerts; staff-rated alert usefulness; false or non-actionable alert frequency; missed alerts
Patient safety outcomes[44-46]	Patient-level outcomes potentially influenced by AI-supported nursing surveillance and coordination	The final aim of AI implementation is safer care, not simply more prediction	Delayed deterioration events; postoperative sepsis; unplanned ICU admission; readmission; medication errors; prolonged length of stay; patient-reported discharge preparedness

The performance domains presented in this table represent a conceptual and practice-oriented framework proposed by the authors to support future implementation and evaluation of artificial intelligence integrated surgical nursing. They should not be interpreted as a formally validated measurement instrument. AI: Artificial intelligence; ICU: Intensive care unit.