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Objective: to describe the development of a predictive nursing workload classifier model, using artificial intelligence., Method: retrospective observational study, using secondary sources of electronic patient records, using machine learning. The convenience sample consisted of 43,871 assessments carried out by clinical nurses using the Perroca Patient Classification System, which served as the gold standard, and clinical data from the electronic medical records of 11,774 patients, which constituted the variables. In order to organize the data and carry out the analysis, the Dataiku® data science platform was used. Data analysis occurred in an exploratory, descriptive and predictive manner. The study was approved by the Ethics and Research Committee of the institution where the study was carried out., Results: the use of artificial intelligence enabled the development of the nursing workload assessment classifier model, identifying the variables that most contributed to its prediction. The algorithm correctly classified 72% of the variables and the area under the Receiver Operating Characteristic curve was 82%., Conclusion: a predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient's electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.

Objective: To describe the development of computerization of risk prediction scales used by nursing in the AGHUse® system., Method: An experience report of technological production at a university hospital, which followed the phases of conception, detailing, construction and prototyping., Results: Different scales were computerized, with emphasis on the Braden and Braden Q, which assess the risk of pressure injuries, and the Severo-Almeida-Kuchenbecker, which assesses the risk of falls. The process of computerization and implementation took place through registration of the scales in the software, application of them in care practice, integration and visualization of their scores with the other functionalities of the electronic medical record., Final Considerations: The functionalities developed in the computerization of risk prediction scales favored its operation, reflecting positively on nursing practice and patient safety.

Objective: To verify the predictors associated with sick leave from 15 days onwards among Nursing professionals of an emergency hospital service., Method: A cross-sectional, retrospective, and descriptive-analytical study. The sample consists of the records of sick leave (n=2,403) due to diseases of the Nursing professionals (n=197) working in an emergency hospital service in southern Brazil, from 2013 to 2018. Descriptive and statistical analysis was used, as well as the multivariate regression model., Results: There was predominance of females (72.6%), white-skinned (86.3%), with a mean age of 45.05 (SD=9.77) years old, and nursing technicians (74.6%). The prevalent cause of sick leave was related to clinical diseases (62.5%). The predictors associated with sick leave from 15 days onwards were the following: Age (OR: 0.97; 95% CI=0.95-0.99) and Musculoskeletal Diseases (OR: 8.95; 95% CI=5.30-15.11)., Conclusion: Age and musculoskeletal diseases were predictors of sick leave from 15 days onwards of the Nursing team.

Objective: To describe relationships between the ECPICID-AVC scale factors and the NANDA-I domains, classes, and Nursing Diagnoses (NDs)., Method: Cross-mapping study between the NANDA-I taxonomy and ECPICID-AVC scale was constructed based on the eight ECPICID-AVC scale factors and the 13 NANDA-I domains. A descriptive analysis was performed to present the mapped elements., Results: Areas of similarity and intersection were found between the eight ECPICID-AVC factors and nine NANDA-I domains, 19 classes, and 72 NDs. All scale factors were mapped with the Domain 1/Health Promotion, Class 2/Health Management and the ND "Frail elderly syndrome"., Final Considerations: The ECPICID-AVC scale factors were mapped with nine domains, their classes and diagnoses. This study demonstrates the importance of identifying nursing diagnoses and their relationship with factors that evaluate caregiving capacity. The ECPICID-AVC can help nurses generate nursing diagnoses regarding the caregiver's needs and their capacities related to care to focus such needs.

Objective: To analyze the ranking and percentage variation of the main causes of hospital admissions and death of Brazilian elders between 2005 and 2015, according to gender and age groups., Method: Retrospective and temporal analysis study. The six main causes of hospitalization and death of elders were collected in DATASUS according to sex and age groups (60 ~ 79, ≥80) in 2017., Results: Heart Failure (2005) and pneumonia (2015) were the two main causes of hospital admissions in both sexes and age groups, except for the younger group. Acute Myocardial Infarction was the main cause of death in 2005 and 2015. The second cause in the overall ranking was the Stroke in 2005 and Pneumonia in 2015., Conclusion: Circulatory and respiratory diseases were the main causes of hospital admissions and death among the elderly, highlighting the impotant increase in pneumonia as a cause of morbimortality.

Objective: To describe the implementation of a standardized process of effective communication for the temporary transfer of inpatient care., Method: Experience report of the implantation of a process of temporary transfer of care between professionals of the hospital wards and areas of diagnostic-therapeutic procedures of a university hospital in the south of Brazil. The process began in 2015 involving adult wards and radiology, being expanded to other areas of the hospital in 2017. The construction of the form was done through meetings and training with the professionals involved in order to contemplate the specificities of care., Result: The elaborated form contemplates relevant aspects about the patient, subsidizing the professionals during the transitions of the care, transportation and accomplishment of procedures., Final Considerations: Its implementation added security and strengthened the process of effective and structured communication among nursing professionals.