Nursing workload on the day of discharge from the intensive care unit is associated with readmission

A nursing note-aware deep neural network for predicting mortality risk after hospital discharge

BACKGROUND

ICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their utilization is challenging. Natural language processing (NLP) can extract structured features from clinical text. This study proposes the Crucial Nursing Description Extractor (CNDE) to predict post-ICU discharge mortality rates and identify high-risk patients for unplanned readmission by analyzing electronic nursing records.

OBJECTIVE

Developed a deep neural network (NurnaNet) with the ability to perceive nursing records, combined with a bio-clinical medicine pre-trained language model (BioClinicalBERT) to analyze the electronic health records (EHRs) in the MIMIC III dataset to predict the death of patients within six month and two year risk.

DESIGN

A cohort and system development design was used.

SETTING(S)

Based on data extracted from MIMIC-III, a database of critically ill in the US between 2001 and 2012, the results were analyzed.

PARTICIPANTS

We calculated patients' age using admission time and date of birth information from the MIMIC dataset. Patients under 18 or over 89 years old, or who died in the hospital, were excluded. We analyzed 16,973 nursing records from patients' ICU stays.

METHODS

We have developed a technology called the Crucial Nursing Description Extractor (CNDE), which extracts key content from text. We use the logarithmic likelihood ratio to extract keywords and combine BioClinicalBERT. We predict the survival of discharged patients after six months and two years and evaluate the performance of the model using precision, recall, the F1-score, the receiver operating characteristic curve (ROC curve), the area under the curve (AUC), and the precision-recall curve (PR curve).

RESULTS

The research findings indicate that NurnaNet achieved good F1-scores (0.67030, 0.70874) within six months and two years. Compared to using BioClinicalBERT alone, there was an improvement in performance of 2.05 % and 1.08 % for predictions within six months and two years, respectively.

CONCLUSIONS

CNDE can effectively reduce long-form records and extract key content. NurnaNet has a good F1-score in analyzing the data of nursing records, which helps to identify the risk of death of patients after leaving the hospital and adjust the regular follow-up and treatment plan of relevant medical care as soon as possible.

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.

Instruments to measure complexity of care based on nursing workload in intensive care units: A systematic review

OBJECTIVE

To establish an evidence-based recommendation on the use of validated scoring systems that measure nursing workload in relation to the complexity of care in adult Intensive Care Units.

METHODS

A systematic review based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) was conducted (PROSPERO registration: CRD42021251272). We searched for validation studies until July 2023 using the bibliographic databases CINAHL, Scopus, Pubmed, WOS, Cochrane Database, SCIELO, Cuiden and Cuidatge. Reference selection and data extraction was performed by two independent reviewers. The assessment of risk of bias was performed using QUADAS-2 and the overall quality according to COSMIN and GRADE approach.

RESULTS

We included 22 articles identifying 10 different scoring systems. Reliability, criterion validity and hypothesis testing were the most frequently measurement properties reported. The NAS was the only tool to demonstrate a Class A recommendation (the best performing instrument).

CONCLUSIONS

NAS is the best currently available scoring system to assess complexity of care from nursing workload in ICU. However, it barely met the criteria for a class A recommendation. Future efforts should be made to develop, evaluate, and implement new systems based on innovative approaches such as intensity or complexity of care.

IMPLICATIONS FOR CLINICAL PRACTICE

The results facilitate decision making as it establishes a ranking of which instruments are recommended, promising or not recommended to measure the nursing workload in the intensive care units.

Cost comparisons and factors related to cost per stay in intensive care units in Belgium

BACKGROUND

Given the variability of intensive care unit (ICU) costs in different countries and the importance of this information for guiding clinicians to effective treatment and to the organisation of ICUs at the national level, it is of value to gather data on this topic for analysis at the national level in Belgium. The objectives of the study were to assess the total cost of ICUs and the factors that influence the cost of ICUs in hospitals in Belgium.

METHODS

This was a retrospective cohort study using data collected from the ICUs of 17 Belgian hospitals from January 01 to December 31, 2018. A total of 18,235 adult ICU stays were included in the study. The data set was a compilation of inpatient information from analytical cost accounting of hospitals, medical discharge summaries, and length of stay data. The costs were evaluated as the expenses related to the management of hospital stays from the hospital's point of view. The cost from the hospital perspective was calculated using a cost accounting analytical methodology in full costing. We used multivariate linear regression to evaluate factors associated with total ICU cost per stay. The ICU cost was log-transformed before regression and geometric mean ratios (GMRs) were estimated for each factor.

RESULTS

The proportion of ICU beds to ward beds was a median [p25-p75] of 4.7% [4.4-5.9]. The proportion of indirect costs to total costs in the ICU was 12.1% [11.4-13.3]. The cost of nurses represented 57.2% [55.4-62.2] of direct costs and this was 15.9% [12.0-18.2] of the cost of nurses in the whole hospital. The median cost per stay was €4,267 [2,050-9,658] and was €2,160 [1,545-3,221] per ICU day. The main factors associated with higher cost per stay in ICU were Charlson score, mechanical ventilation, ECMO, continuous hemofiltration, length of stay, readmission, ICU mortality, hospitalisation in an academic hospital, and diagnosis of coma/convulsions or intoxication.

CONCLUSIONS

This study demonstrated that, despite the small proportion of ICU beds in relation to all services, the ICU represented a significant cost to the hospital. In addition, this study confirms that nursing staff represent a significant proportion of the direct costs of the ICU. Finally, the total cost per stay was also important but highly variable depending on the medical factors identified in our results.

Developing a reflection and analysis tool (We-ReAlyse) for readmissions to the intensive care unit: A quality improvement project

BACKGROUND

Readmissions to the intensive care unit are associated with poorer patient outcomes and health prognoses, alongside increased lengths of stay and mortality risk. To improve quality of care and patients' safety, it is essential to understand influencing factors relevant to specific patient populations and settings. A standardized tool for systematic retrospective analysis of readmissions would help healthcare professionals understand risks and reasons affecting readmissions; however, no such tool exists.

PURPOSE

This study's purpose was to develop a tool (We-ReAlyse) to analyze readmissions to the intensive care unit from general units by reflecting on affected patients' pathways from intensive care discharge to readmission. The results will highlight case-specific causes of readmission and potential areas for departmental- and institutional-level improvements.

METHOD

A root cause analysis approach guided this quality improvement project. The tool's iterative development process included a literature search, a clinical expert panel, and a testing in January and February 2021.

RESULTS

The We-ReAlyse tool guides healthcare professionals to identify areas for quality improvement by reflecting the patient's pathway from the initial intensive care stay to readmission. Ten readmissions were analyzed by using the We-ReAlyse tool, resulting in key insights about possible root causes like the handover process, patient's care needs, the resources on the general unit and the use of different electronic healthcare record systems.

CONCLUSIONS

The We-ReAlyse tool provides a visualization/objectification of issues related to intensive care readmissions, gathering data upon which to base quality improvement interventions. Based on the information on how multi-level risk profiles and knowledge deficits contribute to readmission rates, nurses can target specific quality improvements to reduce those rates.

IMPLICATIONS FOR CLINICAL PRACTICE AND RESEARCH

With the We-ReAlyse tool, we have the opportunity to collect detailed information about ICU readmissions for an in-depth analysis. This will allow health professionals in all involved departments to discuss and either correct or cope with the identified issues. In the long term, this will allow continuous, concerted efforts to reduce and prevent ICU readmissions. To obtain more data for analysis and to further refine and simplify the tool, it may be applied to larger samples of ICU readmissions. Furthermore, to test its generalizability, the tool should be applied to patients from other departments and other hospitals. Adapting it to an electronic version would facilitate the timely and comprehensive collection of necessary information. Finally, the tool's emphasis comprises reflecting on and analyzing ICU readmissions, allowing clinicians to develop interventions targeting the identified problems. Therefore, future research in this area will require the development and evaluation of potential interventions.

Transitional Care Programs for Patients with High Nursing Activity Scores Reduce Unplanned Readmissions to Intensive Care Units

Background and Objectives: The main objective of a transitional care program (TCP) is to detect patients with early deterioration following intensive care unit (ICU) discharge in order to reduce unplanned ICU readmissions. Consensus on the effectiveness of TCPs in preventing unscheduled ICU readmissions remains lacking. In this case study assessing the effectiveness of TCP, we focused on the association of unplanned ICU readmission with high nursing activities scores (NASs), which are considered a risk factor for ICU readmission. Materials and Methods: This retrospective observational study analyzed the data of patients admitted to a single-center ICU between January 2016 and December 2019, with an NAS of >53 points at ICU discharge. The following data were extracted: patient characteristics, ICU treatment, acute physiology and chronic health evaluation II (APACHE II) score at ICU admission, Charlson comorbidity index (CCI), 28-day mortality rate, and ICU readmission rate. The primary outcome was the association between unplanned ICU readmissions and the use of a TCP. The propensity score (PS) was calculated using the following variables: age, sex, APACHE II score, and CCI. Subsequently, logistic regression analysis was performed using the PS to evaluate the outcomes. Results: A total of 143 patients were included in this study, of which 87 (60.8%) participated in a TCP. Respiratory failure was the most common cause of unplanned ICU readmission. The unplanned ICU readmission rate was significantly lower in the TCP group. In the logistic regression model, TCP (odds ratio, 5.15; 95% confidence interval, 1.46−18.2; p = 0.01) was independently associated with unplanned ICU readmission. Conclusions: TCP intervention with a focus on patients with a high NAS (>53 points) may prevent unplanned ICU readmission.

Nursing Activities Score at Discharge from the Intensive Care Unit Is Associated with Unplanned Readmission to the Intensive Care Unit

This study evaluated the accuracy of predicting unplanned the intensive care unit (ICU) readmission using the Nursing Activities Score (NAS) at ICU discharge based on nursing workloads, and compared it to the accuracy of the prediction made using the Stability and Workload Index for Transfer (SWIFT) score. Patients admitted to the ICU of Sapporo Medical University Hospital between April 2014 and December 2017 were included, and unplanned ICU readmissions were retrospectively evaluated using the SWIFT score and the NAS. Patient characteristics, such as age, sex, the Charlson Comorbidity Index, and sequential organ failure assessment score at ICU admission, were used as covariates, and logistic regression analysis was performed to calculate the odds ratios for the SWIFT score and NAS. Among 599 patients, 58 (9.7%) were unexpectedly readmitted to the ICU. The area under the receiver operating characteristic curve of NAS (0.78) was higher than that of the SWIFT score (0.68), and cutoff values were 21 for the SWIFT and 53 for the NAS. Multivariate analysis showed that the NAS was an independent predictor of unplanned ICU readmission. The NAS was superior to the SWIFT in predicting unplanned ICU readmission. NAS may be an adjunctive tool to predict unplanned ICU readmission.

Association between nursing cost and patient outcomes in intensive care units: A retrospective cohort study of Belgian hospitals

INTRODUCTION

Hospitals with better nursing resources report more favourable patient outcomes with almost no difference in cost as compared to those with worse nursing resources. The aim of this study was to assess the association between nursing cost per intensive care unit bed and patient outcomes (mortality, readmission, and length of stay).

METHODOLOGY

This was a retrospective cohort study using data collected from the intensive care units of 17 Belgian hospitals from January 01 to December 31, 2018. Hospitals were dichotomized using median annual nursing cost per bed. A total of 18,235 intensive care unit stays were included in the study with 5,664 stays in the low-cost nursing group and 12,571 in the high-cost nursing group.

RESULTS

The rate of high length of stay outliers in the intensive care unit was significantly lower in the high-cost nursing group (9.2% vs 14.4%) compared to the low-cost nursing group. Intensive care unit readmission was not significantly different in the two groups. Mortality was lower in the high-cost nursing group for intensive care unit (9.9% vs 11.3%) and hospital (13.1% vs 14.6%) mortality. The nursing cost per intensive care bed was different in the two groups, with a median [IQR] cost of 159,387€ [140,307-166,690] for the low-cost nursing group and 214,032€ [198,094-230,058] for the high-cost group. In multivariate analysis, intensive care unit mortality (OR = 0.80, 95% CI: 0.69-0.92, p < 0.0001), in-hospital mortality (OR = 0.82, 95% CI: 0.72-0.93, p < 0.0001), and high length of stay outliers (OR = 0.48, 95% CI: 0.42-0.55, p < 0.0001) were lower in the high-cost nursing group. However, there was no significant effect on intensive care readmission between the two groups (OR = 1.24, 95% CI: 0.97-1.51, p > 0.05).

CONCLUSIONS

This study found that higher-cost nursing per bed was associated with significantly lower intensive care unit and in-hospital mortality rates, as well as fewer high length of stay outliers, but had no significant effect on readmission to the intensive care unit. .

Patient experiences of an intensive care Liaison Nurse support service

OBJECTIVES

To illuminate patients' experiences of being a part of an liaison nurse support service focused on supporting recently transferred intensive care unit patients.

RESEARCH METHODOLOGY/DESIGN

A qualitative inductive descriptive design including in-depth interviews was chosen.

SETTING

A project including an liaison nurse support service-intervention was undertaken during a 16-week period at a University hospital in Sweden. The liaison nurse support service was available Monday-Friday 10 am - 6 pm and nurses visited the patient 1-4 times after transfer to the ward.

MAIN OUTCOME MEASURES

Of the 109 patients who were visited by the liaison nurse support service, 14 agreed to be interviewed about their experiences of the transfer. Data was analysed by inductive content analysis.

FINDINGS

One overall theme, An advocate in a vulnerable situation emerged from the data. Four subthemes were identified: Ensures transfer of information between the intensive care unit and the general ward, Makes the circumstances understandable and coordinates between the care levels and Offers emotional support and stability in an uncertain situation.

CONCLUSION

The liaison nurse support service contributed to ensuring accurate transfer of information, solved problems when the patient themselves did not have control or strength and provided emotional support.