Patient classification systems used to classify nursing intensity and assess nursing staffing resources in home health care: A scoping review

[Proposing a workload indicator for hospital at home patients in Osakidetza - Basque Public Health Service]

INTRODUCTION AND OBJECTIVES

The Hospital at Home (HaH) setting currently lacks adequate workload indicators. This study suggests an indicator that can help in improving professional resources allocation.

MATERIALS AND METHODS

Prospective data was collected during May 2021 from patients treated in nine HaH units of Osakidetza-Basque Health Service (North of Spain). Direct care and travel times of healthcare staff was recorded. Data on inpatient days, number of visits, sociodemographic variables, health status, and patient pathologies, among others, were collected. The proposed indicator encompasses both the average visit time and the visit rates. It is called intensity and represents the average daily workload time per patient.

RESULTS

A total of n = 1,171 users were included in the analyses. Their mean age was 69.8 years, 45.5% were women and 25% lived more than 12 km away from the corresponding HaH unit. Workload variations were observed for nursing-only and medical-nursing teams, depending on the type of day and patient classification group. The average nursing-only teams workload time on working days was 10.82 min and on non-working days it was 14.78 min. The average workload time for medical-nursing teams, during the same days, was 20.40 min and 4.59 min, respectively. It was observed that certain patient types, like those in palliative care, represented a high workload for medical-nursing teams on working days.

CONCLUSIONS

The intensity indicator can help answering the question of how many patients can be assigned to a professional. It can also be used to adjust the staffing needs of the HaH units.

Factors associated with the workload of health professionals in hospital at home: a systematic review

BACKGROUND

Understanding the factors related to workload, could help hospital at home (HaH) managers to make decisions on the most appropriate and efficient use of the HaH services. Published studies on this topic are scarce, so we have conducted a systematic review to identify such factors according to published evidence.

METHODS

Due to the heterogeneity of HaH models, HaH was defined as a care that provides a set of medical and nursing care and attention of hospital rank to patients at home, when they no longer require hospital infrastructure but still need active monitoring and complex care. The electronic data base literature search was conducted in MEDLINE (Ovid), EMBASE (Ovid), and Cinahl (EBSCOhost) from inception to December 2021, including grey literature. Search terms related to `hospital at home´, `workload´ and `care time´ were used. There was no restriction on language, type of study or year of publication. Quality of included studies was assessed using the Critical Appraisal Skills Programme (CASP) checklist and certainty in the body of evidence was assessed using the GRADE Pro Tool. Results were summarised in a tabulated format.

RESULTS

Eighteen studies with 56,706 patients were included. Workload was measured as time, number of visits or both. The predictive factors of the workload included variables related to patient characteristics and other valid and reliable patient classification systems, as well as characteristics of the institutions where the studies were conducted. The factors associated with higher workloads were: being older, male, living in a rural environment, presenting a higher number of diagnoses, having worse functional status and being unable to assume self-care.

CONCLUSIONS

The identified predictors of workload are mostly associated with home nursing care. The results could be useful and applicable to different organisational models of HaH health systems. More studies that include physicians and proxy measures of workload are needed.

The association between nurse staffing level and length of stay in general ward and intensive care unit in Korea

BACKGROUND

Although many studies investigating the relationship between nurse staffing and quality of care have been published, the appropriate nurse-to-patient ratio with patient severity considered remains poorly understood. The aim of this study was to evaluate the impact of nurse staffing levels on length of stay (LOS) in the intensive care unit (ICU) and general ward.

METHODS

This study used a retrospective cohort study. The nurse staffing level was classified into nine grades for ICU based on a nurse-to-bed ratio, and eight grades for general wards based on a nurse-to-patient ratio. A generalized estimating equation model was used to evaluate the associations between the nurse staffing level and LOS. Subgroup analysis was conducted to assess these associations according to patient nursing needs at each hospital type.

RESULTS

This study included 13,135 ICU patients and 263,818 patients admitted to the general ward. In the ICU, the level of nurse staffing (based on the nurse-to-bed ratio) in grade 4 and above (grade 2: <0.63, grade 3: <0.77, grade 4: <0.88) was significantly associated with reduced LOS compared to grade 7 (<1.25). In the general ward, the level of nurse staffing in grade 4 and above was linked to reduced LOS compared to grade 7.

CONCLUSION

The results of this study show that an appropriate nurse-to-patient ratio is associated with a shorter LOS. In particular, hospitals with a higher proportion of severely ill patients require a larger staff of nurses, making it necessary to develop standards for determining nurse staffing level with patient severity taken into account.

Evaluation of Evidence-Based Interventions for the Nurse Coach Scope of Practice Within the Omaha System Guidelines Corpus

The purpose of this study was to examine nurse coach scope of practice in relation to existing evidence-based guideline interventions using the Omaha System. The majority of interventions were within scope for nurse coach practice, and problem, category, and target terms showed differential nurse coach practice applicability across interventions. The Omaha System terminology was aligned with nurse coach practice in that both represent and employ comprehensive and holistic perspectives. This study provides a platform for multiple initiatives in nurse coach quality and documentation and provides a methodology for examining the Omaha System guidelines and interventions for other interprofessional roles.

Management of work stress and burnout among community nurses arising from the COVID-19 pandemic

The COVID-19 pandemic has increased workload demands for many NHS staff including those working in the community. Nurse managers can make a difference by being authentic leaders, nurturing a supportive organisation where the workload is managed participatively and self-kindness is legitimate. Unfortunately some staff may experience burnout and this article presents a personal management plan to address the symptoms of burnout and aid recovery, although it cannot promote a total recovery if the cause of the symptoms remains unaddressed.

Machine learning-based patient classification system for adult patients in intensive care units: A cross-sectional study

AIM

This study aimed to develop a patient classification system that stratifies patients admitted to the intensive care unit based on their disease severity and care needs.

BACKGROUND

Classifying patients into homogenous groups based on clinical characteristics can optimize nursing care. However, an objective method for determining such groups remains unclear.

METHODS

Predictors representing disease severity and nursing workload were considered. Patients were clustered into subgroups with different characteristics based on the results of a clustering algorithm. A patient classification system was developed using a partial least squares regression model.

RESULTS

Data of 300 patients were analysed. Cluster analysis identified three subgroups of critically patients with different levels of clinical trajectories. Except for blood potassium levels (p = .29), the subgroups were significantly different according to disease severity and nursing workload. The predicted value ranges of the regression model for Classes A, B and C were <1.44, 1.44-2.03 and >2.03. The model was shown to have good fit and satisfactory prediction efficiency using 200 permutation tests.

CONCLUSIONS

Classifying patients based on disease severity and care needs enables the development of tailored nursing management for each subgroup.

IMPLICATIONS FOR NURSING MANAGEMENT

The patient classification system can help nurse managers identify homogeneous patient groups and further improve the management of critically ill patients.