Examining adaptive models of care implemented in hospital ICUs during the COVID-19 pandemic: a qualitative study.

BACKGROUND: The emergence of the COVID-19 pandemic led to an increased demand for hospital beds, which in turn led to unique changes to both the organisation and delivery of patient care, including the adoption of adaptive models of care. Our objective was to understand staff perspectives on adaptive models of care employed in intensive care units (ICUs) during the pandemic. METHODS: We interviewed 77 participants representing direct care staff (registered nurses) and members of the nursing management team (nurse managers, clinical educators and nurse practitioners) from 12 different ICUs. Thematic analysis was used to code and analyse the data. RESULTS: Our findings highlight effective elements of adaptive models of care, including appreciation for redeployed staff, organising aspects of team-based models and ICU culture. Challenges experienced with the pandemic models of care were heightened workload, the influence of experience, the disparity between model and practice and missed care. Finally, debriefing, advanced planning and preparation, the redeployment process and management support and communication were important areas to consider in implementing future adaptive care models. CONCLUSION: The implementation of adaptive models of care in ICUs during the COVID-19 pandemic provided a rapid solution for staffing during the surge in critical care patients. Findings from this study highlight some of the challenges of implementing redeployment as a staffing strategy, including how role clarity and accountability can influence the adoption of care delivery models, lead to workarounds and contribute to adverse patient and nurse outcomes.

Assessing the Performance of a Modified LACE Index (LACE-rt) to Predict Unplanned Readmission After Discharge in a Community Teaching Hospital.

BACKGROUND: The LACE index was designed to predict early death or unplanned readmission after discharge from hospital to the community. However, implementing the LACE tool in real time in a teaching hospital required practical unavoidable modifications. OBJECTIVE: The purpose of this study was to validate the implementation of a modified LACE index (LACE-rt) and test its ability to predict readmission risk using data in a hospital setting. METHODS: Data from the Canadian Institute for Health Information's Discharge Abstract Database (DAD), the National Ambulatory Care Reporting System (NACRS), and the hospital electronic medical record for one large community hospital in Toronto, Canada, were used in this study. A total of 3855 admissions from September 2013 to July 2014 were analyzed (N=3855) using descriptive statistics, regression analysis, and receiver operating characteristic analysis. Prospectively collected data from DAD and NACRS were linked to inpatient data. RESULTS: The LACE-rt index was a fair test to predict readmission risk (C statistic=.632). A LACE-rt score of 10 is a good threshold to differentiate between patients with low and high readmission risk; the high-risk patients are 2.648 times more likely to be readmitted than those at low risk. The introduction of LACE-rt had no significant impact on readmission reduction. CONCLUSIONS: The LACE-rt is a fair tool for identifying those at risk of readmission. A collaborative cross-sectoral effort that includes those in charge of providing community-based care is needed to reduce readmission rates. An eHealth solution could play a major role in streamlining this collaboration.

Analyzing Readmissions Patterns: Assessment of the LACE Tool Impact.

This paper will discuss the assessment of the use of the LACE tool at North York General Hospital (NYGH). The LACE tool estimates the readmission risk of patients. This paper describes the tool and a modified LACE score implementation and use at NYGH. We also describe our statistical analysis for the LACE effectiveness in order to inform future decisions in resource allocations. We will look at suggestions for adjustments in the way the LACE tool is used as well as implications for service delivery and patients' quality of life. Our study shows that the modified LACE is a predictive tool for readmission risk in day-to-day hospital activity, but that implementation of LACE alone cannot reduce readmission rates unless coupled with efforts of those in charge of providing community-based care.