Is my patient ready for a safe transfer to a lower-intensity care setting? Nursing complexity as an independent predictor of adverse events risk after ICU discharge

An Investigation of the Barriers to Care of Adult Patients With a Tracheostomy in Intensive Care Units and General Wards: Secondary Analysis of Qualitative Interview Data

AIMS AND OBJECTIVES

To investigate the barriers experienced by intensive care nurses and registered nurses and to provide optimal nursing for adult patients with a temporary tracheostomy in intensive care and general wards.

BACKGROUND

Tracheostomy is widely used in intensive care units, around 20% of intensive care unit patients undergo tracheostomy insertions and expect high quality of care. Caring for patients with a tracheostomy is complex and challenging task. An investigation of barriers to care for adult patients with a temporary tracheostomy in a hospital setting is essential to ensure that these patients receive the highest quality of care and to identify areas for improvement.

DESIGN

This paper applied secondary analysis to data from two qualitative studies, including narrative interviews and maximum variation sampling.

METHODS

Secondary analysis of primary qualitative datasets is appropriate when the analysis extends rather than exceeds the primary. The analysis was based on interview data collected from six intensive care nurses and six registered nurses from two university teaching hospitals in Norway. The interviews were audio-recorded and transcribed. The data was analysed using the qualitative analysis suggested by Graneheim and Lundman. This study adhered to the consolidated criteria for reporting in a qualitative research (COREQ) checklist.

RESULTS

Four main themes were identified as barriers to care for adult patients with a temporary tracheostomy in the hospital: encountering ambivalence, inadequate staffing levels, lack of patient continuity of care and lack of systematic follow-up.

CONCLUSIONS

Understanding barriers to care is crucial for hospitals and healthcare organisations to develop targeted interventions and educational programs to address these barriers and improve the care provided to adult patients with tracheostomies in hospital settings.

Factors associated with early readmission to Intensive Care Units. A systematic review

INTRODUCTION

Readmission to the Intensive Care Unit (ICU) determines worse outcomes such as higher mortality, increased hospital and ICU stay, as well as higher economic costs. When deciding which patient is suitable for transfer from the ICU, factors associated with readmission must be considered to avoid it. Knowledge of these factors helps professionals identify those patients with a higher probability of readmission, prioritizing their care, establishing and preparing interventions that seek to reduce the risk of readmission.

OBJECTIVE

Determine factors associated with early readmission in patients transferred from the ICU to general hospitalization wards of the same hospital.

METHOD

Studies were retrieved from databases: CINAHL, EMBASE, BVS, PubMed, SCOPUS and WOS identifying original studies on adult patients readmitted early to the ICU during the same hospitalization, in any language and without time limit. Studies of patient readmission after seven days, review articles, editorials, protocols, clinical guidelines, qualitative studies and opinion surveys were excluded.

RESULTS

Of 755 files found, 28 articles made up the review. The most analyzed factors were age, sex, severity of the disease, comorbidity, length of stay in the ICU, mechanical ventilation and nocturnal discharge. Those most frequently associated with readmission were age, severity of illness, comorbidity, and length of ICU stay. NEWS, MEWS, and SWIFT scores were also factors associated with readmission.

CONCLUSION

More research is needed to identify those modifiable factors that can decrease readmission rates. Using readmission prediction instruments at the time of discharge could support the decision of which patient is most prepared for it.

Nursing care complexity as a predictor of adverse events in patients transferred from ICU to hospital ward after general surgery

OBJECTIVES

Predicting the likelihood of adverse events following discharge from the intensive care unit (ICU) can contribute to improving the quality of surgical care. This study aimed to evaluate the impact of nursing care complexity as a predictor of adverse event development in general surgery patients transferred from the ICU to the hospital ward.

METHODS

A prospective observational study was conducted with 100 patients in the ICU and general surgical inpatient unit of a training and research hospital in Istanbul, Turkey. The Nursing Care Complexity tool was used by ICU and hospital ward nurses to measure nursing complexity.

RESULTS

A total of 65 adverse events developed in 51 patients during hospital ward hospitalization after discharge from the ICU. Nursing care complexity evaluations by the ICU nurses predicted overall and some specific adverse events, while hospital ward nurses' evaluations predicted ICU readmission and some follow-up abnormalities such as patients' blood pressure, pulse rate, and laboratory results.

CONCLUSION

The results of the current study validate that nursing care complexity can serve as a valuable tool for predicting the risk of adverse events and ICU readmission following discharge from the ICU.

IMPLICATIONS FOR CLINICAL PRACTICE

The use of the Nursing Care complexity tool by the ICU and even hospital ward nurses after ICU discharge may have a significant impact on patient outcomes and contribute to the recognition of nursing efforts.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.