Enhancing the Quality of Care in Long-Term Care Settings

Background The Enhanced Health for Care homes (EHCH) framework is an innovative response to provide more proactive, preventative approaches to care for residents living in care homes. It involves co-producing a shared vision with primary care. As part of EHCH a UK clinical commissioning group supported GP’s in two localities to implement their preferred delivery approach involving a new Frailty Nurse-led (FN-led) model in care homes alongside an existing General Practitioner-led (GP-led) model. This paper focuses on implementation of the new FN-led model. Methods A qualitative study design was adopted. Forty-eight qualitative semi-structured interviews were undertaken across six care home sites in a Northern locality: three implementing the FN-led and three engaged in an existing GP-led model. Participants included residents, family members, care home managers, care staff, and health professionals working within the EHCH framework. Results Two overarching themes were generated from data analysis: Unanticipated implementation issues and Unintended consequences. Unsuccessful attempts to recruit Frailty Nurses (FN) with enhanced clinical skills working at the desired level (UK NHS Band 7) led to an unanticipated evolution in the implementation process of the FN-led model towards ‘training posts’. This prompted misaligned role expectations subsequently provoking unexpected temporary outcomes regarding role-based trust. The existing, well understood nature of the GP-led model may have further exacerbated these unintended consequences. Conclusion Within the broader remit of embedding EHCH frameworks, the implementation of new FN roles needed to evolve due to unforeseen recruitment issues. Wider contextual factors are not in the control of those developing new initiatives and cannot always be foreseen, highlighting how wider factors can force evolution of planned implementation processes with unintended consequences. However, the unintended consequences in this study highlight the need for careful consideration of information dissemination (content and timing) to key stakeholders, and the influence of existing ways of working.

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Frailty nurse and GP-led models of care in care homes: the role of contextual factors impacting Enhanced health in care homes framework implementation

Sattar

Young-Murphy

Craig³

et al. 2023

BMC Geriatr

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Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data

Panchangam,

B U

et al. 2024

Healthcare

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The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk.

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Enhancing the Quality of Care in Long-Term Care Settings

Abstract: The quality of care in long-term care settings is a concern felt across the world given the growing number of dependent older people [...]

Cited by 2 publications

References 24 publications

Frailty nurse and GP-led models of care in care homes: the role of contextual factors impacting Enhanced health in care homes framework implementation

Frailty nurse and GP-led models of care in care homes: the role of contextual factors impacting Enhanced health in care homes framework implementation

Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data

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