scholarly journals Applying the Modified Early Warning Score (MEWS) to assess geriatric patients in home care settings: A qualitative study of nurses’ and general practitioners’ experiences

2019 ◽  
Author(s):  
Kristin Jeppestøl ◽  
Line Kildal Bragstad ◽  
Marit Kirkevold Kirkevold
Keyword(s):  
Decision Making ◽  
Home Care ◽  
Registered Nurses ◽  
General Practitioners ◽  
Clinical Reasoning ◽  
Early Warning ◽  
Geriatric Patients ◽  
Functional Decline ◽  
Early Warning Score ◽  
Design Data

Abstract Background : Acute functional decline is a common clinical syndrome in geriatric health care and is typically characterised by nonspecific symptoms and presentations with a mix of physical, psychological, social and functional manifestations. Early warning score (EWS) systems are widely implemented in nursing homes and home care to detect clinical deterioration. The effects of EWS systems have been thoroughly evaluated in hospital care settings, but few studies have evaluated EWS systems in community healthcare. The purpose of this study was to describe the experiences of registered nurses and general practitioners experiences when using the Modified Early Warning Score (MEWS) to support clinical reasoning and decision-making with geriatric home care patients who suffer from acute functional decline. Method: A qualitative methodology was used with a descriptive exploratory design. Data were collected from seven focus group interviews. General practitioners (GPs), and registered nurses (RNs) were purposively sampled from large, medium and small municipalities in Norway. Data were analysed using an inductive content analysis method. Results: MEWS was used as an additional decision-making tool with elderly home care patients when acute functional decline was detected. RNs and GPs highlighted that MEWS supported the clinical reasoning and decision-making process. Additionally , comprehensive reasoning skills and specific knowledge of the patients were needed. RNs identified the need for contextual adjustments to the use of MEWS in home care settings. Implementing MEWS has improved the collaboration and clinical practice of RNs and GPs. The adherence to MEWS follow-up recommendations was adjusted to the home care setting, accounting for potentially limited medical availability. Conclusion : MEWS supported RNs and GPs in conducting comprehensive clinical assessments and reasoning when acute functional decline was detected. Interdisciplinary communication and collaboration appeared to be strengthened, and the GP's work was streamlined. Several limitations were identified with the use of MEWS reference values with geriatric patients, which could lead to ambiguity and misjudgements . MEWS trigger recommendations were experienced as inappropriate to comply within home care. This study identifies the need for a modified evidence-based EWS adjusted for geriatric patients in home care.

scholarly journals Applying the Modified Early Warning Score (MEWS) to assess geriatric patients in home care settings: A qualitative study of nurses’ and general practitioners’ experiences

2020 ◽  
Author(s):  
Kristin Jeppestøl ◽  
Marit Kirkevold ◽  
Line Kildal Bragstad
Keyword(s):  
Decision Making ◽  
Home Care ◽  
General Practitioners ◽  
Clinical Reasoning ◽  
Early Warning ◽  
Geriatric Patients ◽  
Functional Decline ◽  
Clinical Syndrome ◽  
Early Warning Score ◽  
Design Data

Abstract Background: Acute functional decline is a common clinical syndrome in geriatric health care that is typically characterised by nonspecific symptoms and presents with a mix of physical, psychological, social and functional manifestations.Early warning score (EWS) systems are widely implemented in nursing homes and home care to detect clinical deterioration. The effects of EWS systems have been thoroughly evaluated in hospital care settings, but few studies have evaluated these systems in community health care.The purpose of this study is to describe the experiences of registered nurses (RNs) and general practitioners (GPs) when using the Modified Early Warning Score (MEWS) to support clinical reasoning and decision-making with geriatric home care patients who suffer from acute functional decline.Method: A qualitative methodology was used with a descriptive exploratory design. Data were collected from seven focus group interviews. GPs and RNs were purposively sampled from large, medium and small municipalities in Norway. Data were analysed using an inductive content analysis method.Results: MEWS was used as an additional decision-making tool with elderly home care patients when acute functional decline was detected. RNs and GPs emphasised that MEWS supported the clinical reasoning and decision-making process. Additionally, those applying MEWS required comprehensive reasoning skills and specific knowledge of the patients. RNs identified the need for contextual adjustments to the use of MEWS in home care settings. Implementing MEWS has improved the collaboration and clinical practice of RNs and GPs. The adherence to MEWS follow-up recommendations was adjusted to the home care setting, accounting for potentially limited medical availability.Conclusion: MEWS supported RNs and GPs in conducting comprehensive clinical assessments and reasoning when acute functional decline was detected. Interdisciplinary communication and collaboration appeared to be strengthened, and GPs’ work was streamlined. Several limitations were identified with the use of MEWS reference values with geriatric patients, which could lead to ambiguity and misjudgements. MEWS trigger recommendations were experienced as inappropriate in the home care context. This study identifies the need for a modified, evidence-based EWS adjusted for geriatric patients in home care.

Modified Early Warning Score and VitalPac Early Warning Score in geriatric patients admitted to emergency department

2016 ◽  
Vol 23 (6) ◽  
pp. 406-412 ◽  
Author(s):  
Zerrin Defne Dundar ◽  
Mehmet Ergin ◽  
Mehmet A. Karamercan ◽  
Kursat Ayranci ◽  
Tamer Colak ◽  
...  
Keyword(s):  
Emergency Department ◽  
Early Warning ◽  
Geriatric Patients ◽  
Early Warning Score

Development of A Pharmaceutical Early Warning Scoring Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Hsiao-Ko Chang ◽  
Hui-Chih Wang ◽  
Chih-Fen Huang ◽  
Feipei Lai
Keyword(s):  
Time Series ◽  
Cardiac Arrest ◽  
Early Warning ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Vital Signs ◽  
Sliding Window ◽  
Early Warning Score ◽  
Series Data ◽  
Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a pharmaceutical early warning model to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose a new early warning score model for detecting cardiac arrest via pharmaceutical classification and by using a sliding window; we apply learning-based algorithms to time-series data for a Pharmaceutical Early Warning Scoring Model (PEWSM). By treating pharmaceutical features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits, and replenishers and regulators of water and electrolytes. The best AUROC of bits is 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, LSTM yields better performance with time-series data. The proposed PEWSM, which offers 4-hour predictions, is better than the National Early Warning Score (NEWS) in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

scholarly journals Experiences of a National Early Warning Score (NEWS) intervention in care homes during the COVID-19 pandemic: a qualitative interview study

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e045469
Author(s):  
Rachel Stocker ◽  
Siân Russell ◽  
Jennifer Liddle ◽  
Robert O Barker ◽  
Adam Remmer ◽  
...  
Keyword(s):  
Early Warning ◽  
Healthcare Professionals ◽  
Care Home ◽  
Qualitative Interview ◽  
Interview Study ◽  
Care Homes ◽  
Early Warning Score ◽  
Service Utilisation ◽  
Qualitative Interview Study ◽  
National Early Warning Score

BackgroundThe COVID-19 pandemic has taken a heavy toll on the care home sector, with residents accounting for up to half of all deaths in Europe. The response to acute illness in care homes plays a particularly important role in the care of residents during a pandemic. Digital recording of a National Early Warning Score (NEWS), which involves the measurement of physical observations, started in care homes in one area of England in 2016. Implementation of a NEWS intervention (including equipment, training and support) was accelerated early in the pandemic, despite limited evidence for its use in the care home setting.ObjectivesTo understand how a NEWS intervention has been used in care homes in one area of North-East England during the COVID-19 pandemic, and how it has influenced resident care, from the perspective of stakeholders involved in care delivery and commissioning.MethodsA qualitative interview study with care home (n=10) and National Health Service (n=7) staff. Data were analysed using thematic analysis.ResultsUse of the NEWS intervention in care homes in this area accelerated during the COVID-19 pandemic. Stakeholders felt that NEWS, and its associated education and support package, improved the response of care homes and healthcare professionals to deterioration in residents’ health during the pandemic. Healthcare professionals valued the ability to remotely monitor resident observations, which facilitated triage and treatment decisions. Care home staff felt empowered by NEWS, providing a common clinical language to communicate concerns with external services, acting as an adjunct to staff intuition of resident deterioration.ConclusionsThe NEWS intervention formed an important part of the care home response to COVID-19 in the study area. Positive staff perceptions now need to be supplemented with data on the impact on resident health and well-being, workload, and service utilisation, during the pandemic and beyond.

Sign in / Sign up

Export Citation Format

Share Document