Introduction
Background
Liver transplantation is a highly complex surgical procedure, and effective perioperative management is crucial for optimizing patient outcomes [1‐2]. Globally, liver transplantation activity has been increasing to address the growing burden of liver disease. In 2021, over 34,600 liver transplantations were performed worldwide, representing a 20% increase compared to 2015 [3]. The United States reported 9,527 liver transplantations in 2022, representing a 52% increase over the past decade [4]. Studies [5‐6] have consistently highlighted the critical contributions of nurses in perioperative management, postoperative recovery, and long-term patient care. With the increasing demand for cost-effective, safe, and high-quality healthcare, there is a growing emphasis on using standardized nursing languages to enhance consistency and precision in nursing care [7].
NNN (NANDA-I, NIC, NOC) linkages have been widely adopted in the USA and Europe to standardize nursing practice and improve patient outcomes [8]. Studies [9‐10] show they have been successfully applied in critical care, oncology, and chronic disease management, where standardized frameworks facilitate consistent care delivery, enhance communication among healthcare teams, and support data-driven decision-making.
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However, their application in highly specialized fields, such as liver transplantation, remains limited. The complexities of liver transplant nursing, including managing the risks of potential complications such as rejection, infection, and complex medication regimens, require a tailored NNN framework to address these challenges effectively. By incorporating structured nursing diagnoses (NANDA-I), interventions (NIC), and outcomes (NOC), the framework equips nurses with the tools to deliver personalized care while systematically evaluating patient progress [11‐12].
In China, while NANDA-I is being increasingly incorporated into nursing practice, the broader implementation of NNN linkages, particularly the Nursing Outcome Classification (NOC) and Nursing Interventions Classification (NIC), still needs further advancement [13‐14]. As a result, this hinders standardized documentation and limits the ability to comprehensively assess and evaluate nursing interventions [15‐16].
Globally, specialist nurses and advanced practice nurses (APNs) have increasingly taken on key roles in developing clinical standards, shaping policy, and implementing standardized nursing terminologies in specific areas of practice [17‐18]. The study of NNN linkages is particularly relevant to their practice [19]. These nurses are instrumental in guiding the use of NNN linkages to improve patient care, while facilitating their integration into electronic health records (EHRs). Given their expertise, APNs are well-positioned to lead efforts aimed at applying NNN linkages in liver transplantation care, ultimately contributing to better clinical outcomes.
Objective
The objective of this study is to observe and evaluate the application of NANDA-I, NOC, and NIC (NNN) linkages in the perioperative management of liver transplantation recipients, providing a foundation for future improvements in nursing practice, nursing management, and the development of advanced practice nurses (APNs) in this specialized field.
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Methods
Study design
This prospective observational cohort study was conducted to evaluate the application of the NANDA-I, NOC, and NIC (NNN) linkage framework in the postoperative management of liver transplant recipients. The study adhered to the STROBE guidelines to ensure methodological rigor.
Setting
Research is conducted within the liver transplantation unit of a major tertiary hospital in Eastern China.In this hospital, postoperative nursing outcomes (NOC) were assessed by the responsible nurse, reviewed by an advanced practice nurse (APN), and quality-checked by the head nurse. Nursing interventions (NIC) were finalized collaboratively by the APN and an informatics nurse at discharge, ensuring a comprehensive and patient-centered care plan.
Participants
The study employed consecutive sampling. We enrolled 49 liver transplant patients admitted between December 2022 and August 2023. Adult patients (≥ 18 years) who underwent deceased donor liver transplantation and provided informed consent were included. Patients were excluded if they met any of the following criteria: cognitive impairments, receipt of multiple organ transplants, lack of initial NOC assessments, hospitalization for less than 14 days post-transplant, or death due to surgical complications. Additionally, five patients were excluded as they participated in a preliminary trial phase to test the applicability of NOC indicators.
Sample size calculation
The sample size was calculated for paired-sample t-tests to evaluate differences in nursing outcome scores (NOC indicators). Assuming a medium effect size (d = 0.5), a significance level of 0.05, and 80% power, a minimum of 32 patients was required. This study included 44 patients, exceeding the required sample size and ensuring sufficient power for the multiple comparisons across time points.
Variables and data collection
In previous research, the NNN-link system was developed using care plans from 300 liver transplant patients (2019–2021). The Delphi process included two rounds of expert evaluations conducted in March and April 2022. The panel consisted of 30 liver transplant specialists and nursing informatics experts from 15 tertiary hospitals and academic institutions. There are three types of questions: general expert information, assessment of expert authority, and evaluation of a preliminary set of 15 nursing diagnoses, 91 outcomes, and 275 interventions. Panel members rated each item for importance and relevance using a 5-point Likert scale. Items were iteratively refined based on feedback, with scores for importance and relevance analyzed alongside expert suggestions. This process achieves high authority coefficients (0.83 > 0.8) and moderate Kendall’s coefficients [20] (0.50, 0.38, 0.35), which reflected the complexity of liver transplant care. At last, the Delphi process identified 15 nursing diagnoses, 42 nursing outcomes, and 106 nursing interventions.
Based on these findings, an in-hospital expert panel provided objective, multidisciplinary perspectives to refine the NNN-link system and reach consensus [21]. This study further aimed to validate and optimize the framework through its clinical application.
A multidisciplinary research team was established, including a nursing department deputy director (team leader), advanced practice nurses (APN) specializing in liver transplantation, nursing informatics specialists, postgraduate nursing students, and senior transplant center nurses. The team leader oversaw the research process, facilitated decision-making, and coordinated team efforts. APNs managed nurse training, quality control, and provided feedback on clinical practicality. Informatics specialists organized meetings and performed data analysis, while postgraduate students conducted literature reviews, data entry, and accuracy verification. The liver transplant nursing team, supervised by APNs, conducted patient assessments, identified nursing diagnoses, evaluated NOC indicators, and implemented nursing interventions with standardized documentation.
Before the study, we obtained approval and collaboration from the director and head nurse of the liver transplant unit. Nurses involved in the study were selected from the liver transplant ICU and inpatient wards and required to have at least 5 years of clinical experience. These nurses had structured training organized by the research team. The training is consisted of 3 h of theoretical instruction and 5 h of practical bedside rounds. It mainly focused on NNN linkage application, assessment protocols, and documentation standards. Competency assessments were conducted to ensure readiness before study implementation.
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Initially, a trial phase was conducted to evaluate the suitability of the NOC indicators. From March to August 2023, eligible liver transplant patients were prospectively enrolled. The head nurse participated in preoperative discussions and identified eligible participants. Postoperatively, the responsible nurse performed nursing assessments and completed NOC baseline scoring within 10 a.m. on day of surgery, 3d, 7d, and 14d [22], and discharge using the 15 NANDA-NOC linkage tables (covering 413 NOC indicators). A standardized five-point scale (1 = least favorable, 5 = most favorable) was used for scoring. Scores were reviewed by APNs, with discrepancies resolved through discussions. Persistent issues were escalated to the head nurse during rounds. Upon discharge, APNs conducted a final review of NOC scores to ensure completeness and accuracy.
Nursing interventions were evaluated by APNs and informatics specialists, who compared nursing records from the hospital information system (HIS) with standardized NIC interventions based on established guidelines. Matches were identified, and discrepancies were discussed with the team leader for final determination.
Bias
NOC scoring was conducted through bedside assessments, medical record reviews, patient surveys, and discussions with physicians. Rapport with liver transplant patients ensured trust and cooperation, with subjective data verified item by item to ensure accuracy and reliability.
Measurement
NOC indicators trial use
In November 2022, the research team developed NANDA-NOC linkage tables for liver transplant patients by matching 42 NOCs to 549 NOC indicators and corresponding measurement scale. Patients’ sociodemographic were collected through structured interviews conducted during the admission process.
The trial application involved two liver transplant patients (one male, one female), where advanced practice nurse(APN) and informatics nurses applied the NANDA-NOC linkage tables. If certain NOC indicators were not applicable, reasons were recorded, and the team discussed whether to delete or modify them. A total of 136 NOC indicators were removed, primarily due to inapplicability to adult liver transplant patients or hospital care settings.
In January 2023, the modified NANDA-NOC linkage tables were applied to another liver transplant patient, and no further modifications were needed. In February 2023, two more patients were scored using the revised NOC linkage tables by trained senior nurses. The consistency between different raters was analyzed using the Kappa coefficient [23], which ranged from 0.773 to 0.871, indicating good agreement.
Statistical methods
Data were organized and analyzed using Excel and SPSS 25.0. Descriptive analyses were performed for demographic data: continuous variables were expressed as mean ± standard deviation (SD), while categorical data were presented as frequencies and percentages.
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Differences in the nursing outcome scores for the liver transplant recipients’ postoperative NANDA-I, NOC, and NIC linkages were assessed using paired-sample t-tests and repeated measures analysis of variance (ANOVA). One-way repeated measures ANOVA was conducted to analyze changes in NOC scores across four postoperative time points (day of surgery, 3d, 7d, and 14d) for all 15 NANDA-NOC indicators. For indicators meeting the assumption of sphericity, standard ANOVA results were reported.
Seven NOC indicators violate the sphericity assumption, Greenhouse-Geisser correction was applied to ensure statistical rigor, which are Acute Rejection (W = 0.68, P < 0.001), Acute Pain (W = 0.45, P < 0.001), Activity Intolerance (W = 0.74, P = 0.03), Risk for Falls in Adults(W = 0.74, P = 0.03), Self-Care Deficit (W = 0.57, P < 0.001), Risk for Impaired Oral Mucous Membrane Integrity (W = 0.65, P < 0.001) and Potential Complication: Venous Thromboembolism (W = 0.48, P < 0.001).
To address the issue of multiple comparisons, Bonferroni correction was applied to post hoc pairwise comparisons between the time points. With six pairwise comparisons, the threshold for statistical significance was adjusted to P < 0.008 (0.05/6). Post hoc tests were performed for each indicator to identify specific time points with significant differences. No missing data occurred in this study due to the implementation of rigorous quality assurance measures.
Result
Demographic data
The study investigated 44 liver transplant recipients, including 34 males and 10 females, with ages ranging from 18 to 73 years. The average age was 53.20 ± 11.63 years. The hospital stays ranged from 15 to 77 days, with an average length of stay of 38.73 ± 13.97 days. ICU stays varied between 4 and 45 days, with an average ICU stay of 10.59 ± 7.51 days. Details are shown in Table 1.
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Table 1
Demographic data (n = 44)
Characters | Categories | N(%) / Mean ± SD |
|---|---|---|
Gender | Male | 34 (77.30) |
Female | 10 (22.70) | |
Age (years) | 53.20 ± 11.63 | |
Education Level | Bachelor’s Degree | 12 (27.27) |
Middle School | 20 (45.45) | |
Primary School | 10 (22.73) | |
Illiterate | 2 (4.55) | |
Occupation | Worker | 17 (38.64) |
Farmer | 2 (4.55) | |
Retired | 16 (36.36) | |
Unemployed | 6 (13.63) | |
Student | 2 (4.55) | |
Other | 1 (2.27) | |
Medical Payment Method | Health Insurance | 37 (84.09) |
Self-Pay | 7 (15.91) | |
Primary Caregiver | Spouse | 26 (59.09) |
Parents | 1 (2.27) | |
Children | 14 (31.82) | |
Cousins | 3 (6.82) | |
Marital Status | Married | 42 (95.46) |
Divorced | 1 (2.27) | |
Single | 1 (2.27) | |
Preoperative Diagnosis | Liver Failure | 25 (56.82) |
Liver Cancer | 13 (29.55) | |
Decompensated Cirrhosis | 5 (11.36) | |
Wilson’s Disease | 1 (2.27) | |
Length of Hospital Stay (days) | 38.73 ± 13.97 | |
Length of ICU Stay (days) | 10.59 ± 7.51 | |
Discharge Status | Recovered and Discharged Home | 41 (93.18) |
Transferred to Local Hospital | 3 (6.82) | |
Surgical Method | Orthotopic Liver Transplantation | 42 (95.46) |
Split-Liver Transplantation | 1 (2.27) | |
Modified Piggyback Liver Transplantation | 1 (2.27) |
Statistics on nursing diagnoses
As shown in Table 2, twelve nursing diagnoses were utilized with a 100% usage rate, indicating their universal application to all recipients.
Statistics on nursing interventions
A total of 96.59% of nursing interventions were implemented on average, with 61 interventions achieving a 100% usage rate and 90 interventions showing usage rates of 80% or higher. These results highlight the comprehensive and consistent application of nursing interventions tailored to the needs of liver transplant recipients. Detailed data can be found in Supplementary File 1.
Nursing outcomes and trends
As detailed in Table 2, A paired-sample t-test was conducted to compare the nursing outcome indicator scores on the day of discharge with those recorded on the first postoperative day across 15 nursing diagnoses. The results shows statistically significant improvements in all nursing outcome scores (P < 0.05).
Table 2
Usage rate and t-Test results of nursing outcome scores
NOC (#) | Cases | Usage Rate (%) | Day Of Surgery | Discharge | Effect Size | t | P |
|---|---|---|---|---|---|---|---|
#1 | 44 | 100.00 | 39.48 ± 3.39 | 106.57 ± 4.71 | 16.35 | -81.52 | < 0.001 |
#2 | 44 | 100.00 | 53.45 ± 2.86 | 98.48 ± 2.26 | 17.47 | -84.04 | < 0.001 |
#3 | 44 | 100.00 | 213.80 ± 22.84 | 336.30 ± 6.69 | 7.28 | -37.36 | < 0.001 |
#4 | 44 | 100.00 | 49.16 ± 1.12 | 115.66 ± 7.03 | 13.21 | -64.15 | < 0.001 |
#5 | 44 | 100.00 | 86.95 ± 2.63 | 92.86 ± 2.72 | 2.21 | -9.98 | < 0.001 |
#6 | 44 | 100.00 | 63.41 ± 2.72 | 224.36 ± 8.03 | 26.85 | -124.58 | < 0.001 |
#7 | 44 | 100.00 | 150.91 ± 4.26 | 166.05 ± 4.65 | 16.22 | -14.82 | < 0.001 |
#8 | 44 | 100.00 | 97.95 ± 3.69 | 135.98 ± 5.82 | 7.81 | -39.89 | < 0.001 |
#9 | 44 | 100.00 | 21.27 ± 1.11 | 103.16 ± 3.77 | 29.47 | -143.23 | < 0.001 |
#10 | 44 | 100.00 | 135.59 ± 5.40 | 144.57 ± 4.94 | 1.74 | -8.77 | < 0.001 |
#11 | 44 | 100.00 | 5.84 ± 2.51 | 6.66 ± 2.40 | 0.33 | -7.50 | < 0.001 |
#12 | 44 | 100.00 | 101.84 ± 6.46 | 128.32 ± 2.01 | 5.54 | -24.37 | < 0.001 |
#13 | 40 | 90.91 | 25.39 ± 1.20 | 34.18 ± 0.99 | 7.99 | -34.20 | < 0.001 |
#14 | 39 | 88.64 | 171.55 ± 5.63 | 283.73 ± 7.55 | 16.85 | -86.66 | < 0.001 |
#15 | 39 | 88.64 | 38.82 ± 1.42 | 39.64 ± 0.81 | 0.58 | -3.48 | < 0.001 |
According to Table 3, the results show significant differences in 14 NOC indicators across the four postoperative time points (P < 0.008). However, the NOC score for “Potential Complication: Hyperglycemia/Hypoglycemia” did not show significant changes (P = 0.01 > 0.008). 9 nursing diagnoses demonstrated continuous improvement, while the NOC scores for 5 nursing diagnoses exhibited considerable fluctuations, suggesting the need for further research to confirm the effectiveness of nursing interventions (Fig. 1). The NOC scores for “Potential Complication: Hemorrhage” (η² = 0.811) and “Potential Complication: Electrolyte Imbalance” (η² = 0.265) remained low on the day of surgery and postoperative Day 3, with no significant improvements observed. Similarly, the NOC scores for “Risk for Impaired Oral Mucous Membrane Integrity” (η² = 0.811) and “Risk for Impaired Skin Integrity” (η² = 0.246) remained low on postoperative Days 3 and 7, showing no significant increases. Lastly, the NOC score for “Disturbed Sleep Pattern” (η² = 0.714) significantly declined on postoperative Day 3 and did not exhibit any notable improvement by discharge.
Table 3
ANOVA results for NANDA-NOC linkage scale scores across postoperative time points
NOC (#) | Day 0 | Day 3 | Day 7 | Day 14 | Cases | F | P | Effect Size |
|---|---|---|---|---|---|---|---|---|
#1 | 39.48 ± 3.39 | 73.43 ± 14.34 | 90.50 ± 13.69 | 97.50 ± 7.98 | 44 | 363.35 | < 0.001 | 0.864 |
#2 | 53.45 ± 2.86 | 81.59 ± 10.94 | 91.86 ± 7.42 | 94.75 ± 4.81 | 44 | 491.43 | < 0.001 | 0.896 |
#3 | 213.80 ± 22.84 | 284.16 ± 37.35 | 302.80 ± 33.20 | 320.45 ± 20.96 | 44 | 138.06 | < 0.001 | 0.707 |
#4 | 49.16 ± 1.12 | 72.05 ± 14.04 | 91.00 ± 15.09 | 102.57 ± 12.88 | 44 | 290.19 | < 0.001 | 0.835 |
#5 | 86.95 ± 2.63 | 87.82 ± 2.72 | 88.39 ± 2.33 | 89.48 ± 1.86 | 44 | 18.74 | < 0.001 | 0.246 |
#6 | 63.41 ± 2.72 | 155.75 ± 37.62 | 181.77 ± 33.60 | 202.48 ± 19.34 | 44 | 311.73 | < 0.001 | 0.845 |
#7 | 150.91 ± 4.26 | 152.36 ± 4.79 | 157.27 ± 5.05 | 159.84 ± 5.09 | 44 | 56.22 | < 0.001 | 0.495 |
#8 | 97.95 ± 3.69 | 112.68 ± 10.50 | 120.05 ± 9.20 | 126.50 ± 7.17 | 44 | 164.57 | < 0.001 | 0.742 |
#9 | 21.27 ± 1.11 | 48.32 ± 20.41 | 73.89 ± 18.89 | 87.82 ± 12.82 | 44 | 272.19 | < 0.001 | 0.826 |
#10 | 135.59 ± 5.40 | 135.95 ± 4.86 | 138.61 ± 4.28 | 140.18 ± 4.09 | 44 | 18.59 | < 0.001 | 0.245 |
#11 | 5.84 ± 2.51 | 5.82 ± 2.60 | 5.93 ± 2.58 | 6.16 ± 2.52 | 44 | 3.98 | 0.01 | 0.065 |
#12 | 101.84 ± 6.46 | 116.82 ± 7.77 | 124.52 ± 4.53 | 126.14 ± 3.27 | 44 | 245.51 | < 0.001 | 0.811 |
#13 | 25.39 ± 1.20 | 27.48 ± 1.95 | 30.91 ± 2.04 | 31.82 ± 2.12 | 44 | 142.91 | < 0.001 | 0.714 |
#14 | 171.55 ± 5.63 | 235.05 ± 21.93 | 253.34 ± 26.22 | 269.66 ± 13.46 | 44 | 402.69 | < 0.001 | 0.875 |
#15 | 38.82 ± 1.42 | 38.05 ± 1.76 | 37.61 ± 3.06 | 38.77 ± 1.52 | 44x | 5.70 | 0.007 | 0.090 |
Fig. 1
Score Trends of NANDA-NOC Linkage Scale
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Discussion
This study represents the first real-world clinical application of the NNN framework to evaluate its applicability and effectiveness [24]. The research team utilized a tool validated through expert consensus and prior pilot studies [21]. These findings, consistent with prior research [25], highlight the need for real-world validation to confirm the practical utility of standardized nursing frameworks.
The NOC indicators offered a standardized framework for assessing liver transplant patients, enabling a comprehensive understanding of patient needs, promoting recovery, and improving quality of life [26]. Significant differences in NOC scores for the 15 nursing diagnoses were observed between the day of surgery and the day of discharge, validating the feasibility of the NNN-linkage framework in measuring perioperative changes and evaluating nursing interventions at five predefined time points. These findings align with previous studies in other clinical areas [27‐28], which demonstrate the utility of NOC in identifying critical clinical changes and guiding effective nursing practices. For example Gencbas et al. [29] reported significant improvements in incontinence-related outcomes and quality of life in elderly women using NNN linkages. In oncology, Mello et al. [30] confirmed the applicability of NOC indicators in managing acute and chronic pain in cancer patients, “”.
In this study, Advanced Practice Nurses (APNs) played a pivotal role in ensuring the accuracy and implementation of the NNN framework. Irvine’s Nursing Role Effectiveness Model [31] links specific outcomes to the independent, dependent, and interdependent roles of nurses. Diagnoses Risk for “Impaired Oral Mucous Membrane Integrity” (η² = 0.811), “Risk for Impaired Skin Integrity” (η² = 0.246) and “Disturbed Sleep Pattern” (η² = 0.090) fell under the independent role of nurses, requiring targeted interventions for which nurses were directly responsible. Prolonged mechanical ventilation, limited mobility, and physical restraints increased the risk of pressure injuries at multiple sites, while tracheal intubation and fasting before gastrointestinal recovery heightened the vulnerability of oral mucosa. These findings underscore the necessity of proactive preventive measures within the first 14 postoperative days to address oral and pressure injury risks [32]. Similarly, “Disturbed Sleep Pattern"is prevalent among liver transplant recipients, significantly impacting recovery and quality of life [33]. This highlights the critical need for targeted sleep care interventions during the early postoperative period to promote recovery and improve patient outcomes.
The dependent role of nurses involves clinical judgment and activities related to executing medical orders and treatments. In this study, diagnoses “Potential Complication: Hemorrhage”, “Potential Complication: Electrolyte Imbalance” (η² = 0.265), and “Potential Complication: Hyperglycemia/ Hypoglycemia” were identified as collaborative problems. Liver transplant patients face heightened bleeding risks within the first seven postoperative days due to reduced procoagulant pathways and impaired anticoagulation and fibrinolysis mechanisms [34]. Electrolyte imbalances are attributed to intraoperative blood loss, stress responses, and hepatic dysfunction [35], while early hyperglycemia often results from immunosuppressant and corticosteroid use [36]. Nurses must closely monitor the occurrence and progression of these physiological complications, ensuring timely interventions to mitigate risks.
APNs were instrumental in promoting continuity, coordination, and integration of patient care. They can facilitate communication between nurses and other healthcare providers, guiding nurses to increase the frequency of NOC evaluations to proactively identify and address declining scores. Dynamic adjustments to nursing care plans, emphasizing high-frequency monitoring and tailored interventions during critical periods, were key recommendations. Furthermore, APNs can advocate for the integration of the NNN framework into electronic health systems (EHS), enabling real-time monitoring and alert systems.
Lower NIC utilization rates (17/106) were attributed to the lack of integration of NIC-based care plans into existing hospital practices, limiting documentation. This finding aligns with prior research [37‐38], indicating the need for training [39] and integration efforts to improve documentation and implementation of NIC-based care plans. APNs can advocate for policies mandating the use of standardized frameworks such as NIC in clinical documentation and care planning. Collaborating closely with multidisciplinary teams, APNs can ensure NIC interventions become integral to patient care strategies, enhancing overall nursing practice.
Limitation
Firstly, this study was conducted at a single center in mainland China, which may limit the generalizability of the findings. Cultural and institutional factors specific to the study site, may further restrict the generalizability. Secondly, some indicators relied on patient self-reports, which may introduce reporting bias, as patients could underestimate or overestimate their actual conditions due to various factors. Thirdly, the absence of a control group limits the ability to compare outcomes against standard care or other interventions. This limitation underscores the need for future studies to incorporate controlled designs to better evaluate the effectiveness of the NNN framework. Finally, the observational nature of this study precludes causal inferences.
Conclusion
This study demonstrates the successful application of NANDA-I, NOC, and NIC (NNN) linkages in the postoperative management of liver transplant patients, with advanced practice nurses (APNs) playing a critical role in ensuring accuracy and implementation. The findings highlight the sensitivity of NOC indicators to patient condition changes and their role in improving nursing outcomes, providing insights for enhancing care protocols.
Future research should focus on identifying core NOC indicators, addressing NIC utilization barriers. With APN leadership, these findings can guide healthcare organizations and policymakers in standardizing nursing documentation, enhancing NNN training, and integrating the framework into electronic health records to improve care quality.
Declarations
Ethics approval and consent to participate
This study protocol was approved by the Institutional Review Board and Ethics Review Committee of Shulan Hospital (NO. KY2022041). The research carried out in accordance with the Declaration of Helsinki and its subsequent amendments.
Consent for publication
Not Applicable.
Informed consent
was obtained from all participants, participants could decide whether or not to participate and could withdraw at any time without consequences.
Clinical trial number
Not applicable. This study did not involve a clinical trial.
Competing interests
The authors declare no competing interests.
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