Background: A bundle is a set of three to five evidence-based interventions designed to improve the quality and outcomes of care processes. Numerous international studies have evaluated the effectiveness of Bundles/Care Bundles (BCB) in reducing morbidity and mortality. The Institute for Healthcare Improvement (IHI) has defined the concept of a bundle but has not outlined the development process. Objective: To identify the BCB development process in clinical risk scenarios from September 2005 to September 2023. Methods: A systematic review was conducted following PRISMA guidelines to identify studies describing the BCB development process in managing clinical risk situations. The databases consulted included PubMed, Embase, and CINAHL, along with manual searches on institutional websites. Relevant studies concerning the BCB development process were included. Results: A total of 1372 studies were retrieved, of which 16 were included. Duplicates were removed, and titles and abstracts were analyzed. The identified methods for BCB development include IHI guidelines, expert opinions, international guidelines, and flowcharts. The most common BCBs relate to the prevention of ventilator-associated pneumonia, surgical site infections, catheter-associated infections, and sepsis. Conclusions: This study has identified the development processes of Care Bundles (BCBs) in clinical risk scenarios, highlighting how these tools facilitate compliance monitoring among members of the healthcare team. The review has revealed effective methods for designing evidence-based BCBs. However, the scarcity of studies on the methodology for developing BCBs is a limitation, suggesting the need for further research. In Italy, there is a growing interest in the use of care packages. It is essential to encourage research that optimizes the effectiveness of intervention strategies.

Background/Objectives: The increasing medical and nursing care complexity in hospitalized children represents a significant challenge for healthcare systems. However, the link between these two dimensions remains partially explored. This study aims to decipher the relationship between Diagnosis-Related Group (DRG) weight and nursing care complexity in hospitalized children and to identify the determinants of medical complexity. Methods: This retrospective study, conducted in an Italian university hospital, included children aged 2 to 11 years admitted to the hospital in 2022 with a minimum hospital stay of 2 days. Data were gathered from the Neonatal Pediatric Professional Assessment Instrument and the Hospital Discharge Register. DRG weight was used as an indicator of medical complexity, while the number of nursing diagnoses (NDs) documented in the first 24 h from hospital admission and the nursing actions (NAs) recorded during the patient’s hospital stay were used to measure nursing care complexity. Correlation analyses were conducted to explore the associations between DRG weight, NDs, and NAs. Stepwise regression was run to identify the key determinants of medical complexity across sociodemographic, clinical, organizational, and nursing variables. Results: Among 914 patients (mean age of 6.11 ± 2.90 years), the median DRG weight was 0.6982 (IQR: 0.5522). Patients had an average of 3.89 ± 2.83 NDs and a median of 17 NAs (IQR: 8). Significant correlations were found between NDs and NAs (r_s = 0.507; p < 0.001), as well as between DRG weight and the frequency of NDs (r_s = 0.232; p < 0.001) and NAs (r_s = 0.184; p < 0.001). Stepwise regression indicated that the number of NAs, surgical DRG, scheduled admissions, and ND frequency were significant determinants of DRG weight (R² = 0.311; adjusted R² = 0.308; p < 0.001). Conclusions: In children, DRG weight is also influenced by nursing care complexity, alongside clinical and organizational factors. An integrated approach is essential to enhance pediatric care and patient outcomes.

This study examined variability in nursing complexity among pediatric patients within the same Diagnosis-related group (DRG) and assessed whether exceeding DRG-specific length-of-stay (LOS) thresholds was associated with higher nursing complexity. A retrospective study analyzed electronic health records of pediatric inpatients admitted in 2022 to a tertiary hospital, focusing on the 20 most frequent DRGs. Nursing complexity was assessed using nursing diagnoses (NDs) recorded within 24 h of admission and nursing actions (NAs) documented during hospitalization. Group differences were assessed using independent-samples t-tests and Mann-Whitney U tests. Blockwise multivariable logistic regression estimated associations with LOS threshold exceedance: the first model included ND count adjusted for age, sex, DRG weight, and comorbidities, while the second additionally incorporated NA count. Among 1204 patients, 4247 NDs and 22014 NAs were recorded. NDs ranged 1-14 and NAs 5-59, with marked intra- and inter-DRG variability. Patients exceeding LOS thresholds (2.6%) had higher ND and NA counts (mean 6.29 vs 3.45 and median 27 vs 17, respectively; both p < 0.001). In the fully adjusted model, each additional ND increased the odds of LOS threshold exceedance by 25% (OR = 1.25, 95% CI 1.09-1.43, p = 0.001), and each additional NA by 9% (OR = 1.09, 95% CI 1.05-1.14, p < 0.001). Nursing complexity varies within and across DRGs and is higher among patients exceeding LOS thresholds. Standardized nursing data may complement DRG classifications to better reflect nursing complexity and inform future reimbursement models.

Antimicrobial resistance is a global health issue, and the WHO has made significant efforts in the development of tools for its monitoring. However, such tools are underutilized, due to limited knowledge, technical capacity, and scarcity of economic resources. AMR surveillance can be conducted using WHOnet and R, two free-of-charge software tools widely adopted in both clinical practice and scientific research. WHOnet is designed for managing laboratory data and antimicrobial susceptibility test results, while R is a programming language dedicated to statistical computing and data visualization. The combined use of these tools enables a reproducible workflow for retrospective AMR trend analysis. This paper provides step-by-step instructions on how to perform such analysis and also provides the respective R code. The described code and software results are shown using real-world data from an Italian hospital as an example. The standardization of the analysis process and the rapid availability of data on antimicrobial resistance are critical for both clinicians and public health professionals. They would allow for empirical decisions on antimicrobial treatment based on the specific epidemiological characteristics of the hospital or community setting.

Aim: To estimate national incidence, temporal trends, and regional variability of hospital-acquired pressure ulcers in Italy from 2018 to 2024 using age-adjusted models and regional estimates. Design: Retrospective nationwide observational study using hospital administrative data. Methods: All Italian Hospital Discharge Records (SDOs) for adults aged ≥ 18 years with hospital stays ≥ 5 days between 2018 and 2024 were analysed. Records with pre-existing or principal diagnoses of pressure ulcer and excluded MDC/DRG categories were omitted according to adapted AHRQ PSI 03 specifications. The final dataset represented eligible hospitalizations considered at risk for hospital-acquired pressure ulcers. Crude and age-adjusted rates per 10,000 eligible discharges were estimated using logistic regression models. Results: Eligible discharges declined from approximately 1.7 million in 2018 to 1.4 million in 2020, increasing to 1.5 million in 2024. Within this population, coded hospital-acquired pressure ulcer events decreased from 3657 to 1888, then increased to 2728. Age-adjusted national rates ranged from 13.5 to 21.3 per 10,000 eligible discharges, showing temporal fluctuations during the study period, including a reduction during 2020–2021 followed by a gradual return toward pre-pandemic levels. Substantial regional variability was observed, with lower median annual adjusted rates in regions such as Friuli Venezia Giulia and Toscana and higher values in Lazio and Abruzzo. Conclusions: This nationwide analysis provides an initial descriptive overview of temporal and regional variability in coded hospital-acquired pressure ulcer events identified through an adapted PSI 03-based administrative indicator in Italy. The findings may contribute to future methodological discussion and exploratory development of nursing-sensitive indicators using national administrative healthcare databases. Implications for the profession and/or patient care: The integration of nursing-sensitive administrative indicators into national quality monitoring systems may represent an initial methodological area for future benchmarking activities, indicator validation processes, and descriptive evaluation of preventive care practices using national healthcare administrative databases. Impact (addressing): Problem: limited national evidence on hospital-acquired pressure ulcers and interregional variability in Italy. Main findings: temporal fluctuations in age-adjusted rates and persistent regional heterogeneity in coded pressure ulcer events. Impact: administrative data may represent a preliminary and exploratory source for the study of nursing-sensitive outcomes and patient safety indicators at national level.