Organizational Factors Associated With Nurses’ Competence in Averting Failure to Rescue in Acute Care Settings

Background: Organizational characteristics are the main concerns of nursing practice in acute care settings. The present study aimed to assess the organizational factors associated with nurses’ competence in averting Failure to Rescue (FTR) in acute care settings. Methods: This was a descriptive and correlational study. A purposive sampling technique was used to collect the necessary data from the study respondents. In total, 173 of the 204 eligible registered nurses providing sudden, urgent, and emergency direct care to patients in the identified acute care settings of Olabisi Onabanjo University Teaching Hospital in Sagamu City, Nigeria participated in this study. A self-structured 38-item questionnaire, including 4 parts (demographic characteristics, knowledge on FTR, competence in averting FTR, & organizational factors) was employed for data collection. The obtained data were analyzed in SPSS V. 22 using descriptive statistics (i.e. frequency, percentages, mean, standard deviation, & tables) as well as Spearman’s Rho correlation to test the hypotheses based on the assumptions that the variables were measured on an ordinal scale at P<0.05. Results: Most nurses presented a high level of knowledge regarding FTR with a mean score of 5.91. Besides, they were moderately competent in averting FTR with a mean score of 29.3. A significant correlation was also detected between organizational characteristics and the studied nurses’ level of competence in averting FTR (P=0.026). Conclusion: The present study data revealed that FTR could be reduced in acute care settings by the modification of organizational factors.

Comparison of delirium detection tools in acute care

Background Delirium is a frequent psychopathological syndrome in geriatric patients. It is sometimes the only symptom of acute illness and bears a high risk for complications. Therefore, feasible assessments are needed for delirium detection. Objective and methods Rapid review of available delirium assessments based on a current Medline search and cross-reference check with a special focus on those implemented in acute care hospital settings. Results A total of 75 delirium detection tools were identified. Many focused on inattention as well as acute onset and/or fluctuating course of cognitive changes as key features for delirium. A range of assessments are based on the confusion assessment method (CAM) that has been adapted for various clinical settings. The need for a collateral history, time resources and staff training are major challenges in delirium assessment. Latest tests address these through a two-step approach, such as the ultrabrief (UB) CAM or by optional assessment of temporal aspects of cognitive changes (4 As test, 4AT). Most delirium screening assessments are validated for patient interviews, some are suitable for monitoring delirium symptoms over time or diagnosing delirium based on collateral history only. Conclusion Besides the CAM the 4AT has become well-established in acute care because of its good psychometric properties and practicability. There are several other instruments extending and improving the possibilities of delirium detection in different clinical settings.

Risk-Adjusting Mortality in the Nationwide Veterans Affairs Healthcare System

Background The US Veterans Affairs (VA) healthcare system began reporting risk-adjusted mortality for intensive care (ICU) admissions in 2005. However, while the VA’s mortality model has been updated and adapted for risk-adjustment of all inpatient hospitalizations, recent model performance has not been published. We sought to assess the current performance of VA’s 4 standardized mortality models: acute care 30-day mortality (acute care SMR-30); ICU 30-day mortality (ICU SMR-30); acute care in-hospital mortality (acute care SMR); and ICU in-hospital mortality (ICU SMR). Methods Retrospective cohort study with split derivation and validation samples. Standardized mortality models were fit using derivation data, with coefficients applied to the validation sample. Nationwide VA hospitalizations that met model inclusion criteria during fiscal years 2017–2018(derivation) and 2019 (validation) were included. Model performance was evaluated using c-statistics to assess discrimination and comparison of observed versus predicted deaths to assess calibration. Results Among 1,143,351 hospitalizations eligible for the acute care SMR-30 during 2017–2019, in-hospital mortality was 1.8%, and 30-day mortality was 4.3%. C-statistics for the SMR models in validation data were 0.870 (acute care SMR-30); 0.864 (ICU SMR-30); 0.914 (acute care SMR); and 0.887 (ICU SMR). There were 16,036 deaths (4.29% mortality) in the SMR-30 validation cohort versus 17,458 predicted deaths (4.67%), reflecting 0.38% over-prediction. Across deciles of predicted risk, the absolute difference in observed versus predicted percent mortality was a mean of 0.38%, with a maximum error of 1.81% seen in the highest-risk decile. Conclusions and Relevance The VA’s SMR models, which incorporate patient physiology on presentation, are highly predictive and demonstrate good calibration both overall and across risk deciles. The current SMR models perform similarly to the initial ICU SMR model, indicating appropriate adaption and re-calibration.

Implementation of the Frailty Care Bundle (FCB) to promote mobilisation, nutrition and cognitive engagement in older people in acute care settings: protocol for an implementation science study

Background: Older people are among the most vulnerable patients in acute care hospitals. The hospitalisation process can result in newly acquired functional or cognitive deficits termed hospital associated decline (HAD). Prioritising fundamental care including mobilisation, nutrition, and cognitive engagement can reduce HAD risk. Aim: The Frailty Care Bundle (FCB) intervention aims to implement and evaluate evidence-based principles on early mobilisation, enhanced nutrition and increased cognitive engagement to prevent functional decline and HAD in older patients. Methods: A hybrid implementation science study will use a pragmatic prospective cohort design with a pre-post mixed methods evaluation to test the effect of the FCB on patient, staff, and health service outcomes. The evaluation will include a description of the implementation process, intervention adaptations, and economic costs analysis. The protocol follows the Standards for Reporting Implementation Studies (StaRI). The intervention design and implementation strategy will utilise the behaviour change theory COM-B (capability, motivation, opportunity) and the Promoting Action on Research Implementation in Health Services (i-PARIHS). A clinical facilitator will use a co-production approach with staff. All patients will receive care as normal, the intervention is delivered at ward level and focuses on nurses and health care assistants (HCA) normative clinical practices. The intervention will be delivered in three hospitals on six wards including rehabilitation, acute trauma, medical and older adult wards. Evaluation: The evaluation will recruit a volunteer sample of 180 patients aged 65 years or older (pre 90; post 90 patients). The primary outcomes are measures of functional status (modified Barthel Index (MBI)) and mobilisation measured as average daily step count using accelerometers. Process data will include ward activity mapping, staff surveys and interviews and an economic cost-impact analysis. Conclusions: This is a complex intervention that involves ward and system level changes and has the potential to improve outcomes for older patients.

Does Seasonal Influenza Related Hospital Occupancy Surge Impact Hospital Staff Sickness Presenteeism and Productivity Costs?

Background—The workload of public hospital staff is heightened during seasonal influenza surges in hospitals serving densely populated cities. Such work environments may subject staff to increased risk of sickness presenteeism. Presenteeism is detrimental to nurses’ health and may lead to downstream productivity loss, resulting in financial costs for hospital organizations. Aims—This study aims to quantify how seasonal influenza hospital occupancy surge impacts nurses’ sickness presenteeism and related productivity costs in high-intensity inpatient metropolitan hospitals. Methods—Full-time nurses in three Hong Kong acute-care hospitals were surveyed. Generalized estimating equations (GEE) was applied to account for clustering in small number of hospitals. Results—A total of 71.3% of nurses reported two or more presenteeism events last year. A 6.8% increase in hospital inpatient occupancy rate was associated with an increase of 19% (1.19, 95% CI: 1.06–1.34) in nurse presenteeism. Presenteeism productivity loss costs between nurses working healthy (USD1983) and worked sick (USD 2008) were not significantly different, while sick leave costs were highest (USD 2703). Conclusion—Presenteeism prevalence is high amongst acute-care hospital nurses and workload increase during influenza flu surge significantly heightened nurse sickness presenteeism. Annual presenteeism productivity loss costs in this study of USD 24,096 were one of the highest reported worldwide. Productivity loss was also considerably high regardless of nurses’ health states, pointing towards other potential risk factors at play. When scheduling nurses to tackle flu surge, managers may want to consider impaired productivity due to staff presenteeism. Further longitudinal research is essential in identifying management modifiable risk factors that impact nurse presenteeism and impairing downstream productivity loss.

Reconstructing transmission chains of SARS-CoV-2 amid multiple outbreaks in a geriatric acute-care hospital

Background There is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS–CoV–2) in geriatric settings. Methods We performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID–19) in four outbreak–affected wards, and all SARS–CoV–2 RT–PCR positive HCWs from a Swiss university–affiliated geriatric acute–care hospital that admitted both Covid–19 and non–Covid–19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS–CoV–2 involving patients and HCWs, in order to determine who infected whom. We evaluated general transmission patterns according to type of case (HCWs working in dedicated Covid–19 cohorting wards: HCWcovid; HCWs working in non–Covid–19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid–19: patientnoso) by deriving the proportion of infections attributed to each type of case across all posterior trees and comparing them to random expectations. Results During the study period (March 1 to May 7, 2020) we included 180 SARS–CoV–2 positive cases: 127 HCWs (91 HCWcovid, 36 HCWoutbreak) and 53 patients. The attack rates ranged from 10–19% for patients, and 21% for HCWs. We estimated that there were 16 importation events (3 patients, 13 HCWs) that jointly led to 16 secondary cases. Most patient–to–patient transmission events involved patients having shared a ward (97.6%, 95% credible interval [CrI] 90.4–100%), in contrast to those having shared a room (44.4%, 95%CrI 27.8–62.5%). Transmission events tended to cluster by type of case: patientnoso were almost twice as likely to be infected by other patientnoso than expected (observed:expected ratio 1.91, 95%CrI 1.08 – 4.00, p = 0.02); similarly, HCWoutbreak were more than twice as likely to be infected by other HCWoutbreak than expected (2.25, 95%CrI 1.00–8.00, p = 0.04). The proportion of infectors of HCWcovid were as expected as random. The proportions of high transmitters (≥2 secondary cases) were significantly higher among HCWoutbreak than patientnoso in the late phases (26.2% vs. 13.4%, p<2.2e–16) of the outbreak. Conclusions Most importation events were linked to HCW. Unexpectedly, transmission between HCWcovid was more limited than transmission between patients and HCWoutbreak. This highlights gaps in infection control and suggests possible areas of improvements to limit the extent of nosocomial transmission.