CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals.

Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart's rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.

Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods.

BACKGROUND: Hospitals in the public and private sectors tend to join larger organizations to form hospital groups. This increasingly frequent mode of functioning raises the question of how countries should organize their health system, according to the interactions already present between their hospitals. The objective of this study was to identify distinctive profiles of French hospitals according to their characteristics and their role in the French hospital network. METHODS: Data were extracted from the national hospital database for year 2016. The database was restricted to public hospitals that practiced medicine, surgery or obstetrics. Hospitals profiles were determined using the k-means method. The variables entered in the clustering algorithm were: the number of stays, the effective diversity of hospital activity, and a network-based mobility indicator (proportion of stays followed by another stay in a different hospital of the same Regional Hospital Group within 90 days). RESULTS: Three hospital groups were identified by the clustering algorithm. The first group was constituted of 34 large hospitals (median 82,100 annual stays, interquartile range 69,004 - 117,774) with a very diverse activity. The second group contained medium-sized hospitals (with a median of 258 beds, interquartile range 164 - 377). The third group featured less diversity regarding the type of stay (with a mean of 8 effective activity domains, standard deviation 2.73), a smaller size and a higher proportion of patients that subsequently visited other hospitals (11%). The most frequent type of patient mobility occurred from the hospitals in group 2 to the hospitals in group 1 (29%). The reverse direction was less frequent (19%). CONCLUSIONS: The French hospital network is organized around three categories of public hospitals, with an unbalanced and disassortative patient flow. This type of organization has implications for hospital planning and infectious diseases control.

The prediction of hospital length of stay using unstructured data.

OBJECTIVE: This study aimed to assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gender and major ICD diagnosis. METHODS: This study was an observational retrospective cohort study and analyzed patient stays admitted between 1 January to 24 September 2019. For each stay, a patient was admitted through the Emergency Department (ED) and stayed for more than two days in the subsequent service. LOS was predicted using two random forest models. The first included unstructured text extracted from electronic health records (EHRs). A word-embedding algorithm based on UMLS terminology with exact matching restricted to patient-centric affirmation sentences was used to assess the EHR data. The second model was primarily based on structured data in the form of diagnoses coded from the International Classification of Disease 10th Edition (ICD-10) and triage codes (CCMU/GEMSA classifications). Variables common to both models were: age, gender, zip/postal code, LOS in the ED, recent visit flag, assigned patient ward after the ED stay and short-term ED activity. Models were trained on 80% of data and performance was evaluated by accuracy on the remaining 20% test data. RESULTS: The model using unstructured data had a 75.0% accuracy compared to 74.1% for the model containing structured data. The two models produced a similar prediction in 86.6% of cases. In a secondary analysis restricted to intensive care patients, the accuracy of both models was also similar (76.3% vs 75.0%). CONCLUSIONS: LOS prediction using unstructured data had similar accuracy to using structured data and can be considered of use to accurately model LOS.

[The suitability of sending nursing home residents over 75 to emergency departments]. / Pertinence des passages aux urgences des résidents d'Ehpad de 75 ans et plus.

The care provided to elderly people aged over 75 must be specific and multidisciplinary. An emergency department, which is seeing increasing numbers of patients passing through its doors, notably with the provision of an ambulatory care service, would not appear to be a suitable place for this fragile population, often with multiple pathologies. A study is looking at the suitability of the emergency department for nursing home residents, who have regular access to medical care, unlike elderly people living at home.

Forecasting the Emergency Department Patients Flow.

Emergency department (ED) have become the patient's main point of entrance in modern hospitals causing it frequent overcrowding, thus hospital managers are increasingly paying attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human (doctors, nurses…) and material(beds, boxs…) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of Troyes city hospital center, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA categories into one category and innovative time-series based models to forecast long and short term daily attendance. The models we developed for this case study shows very good performances (up to 91,24 % for the annual Total flow forecast) and robustness to epidemic periods.

Effectiveness of Prevention Interventions Using Social Marketing Methods on Behavioural Change in the General Population: A Systematic Review of the Literature.

In an effort to encourage people to adopt healthy behaviours, social marketing is increasingly used in disease prevention and health promotion. This systematic review aimed to evaluate the effect of prevention initiatives that use social marketing techniques on achieving behavioural change in the general population. We conducted a systematic review of PubMed, Embase, Science Direct, Cochrane, and Business Source Complete. Among 1189 articles identified across all databases, 10 studies met the inclusion criteria (six randomized controlled trials and four systematic reviews). The number of social marketing criteria used varies according to the studies. The results showed positive effects overall, albeit not always statistically significant. The quality of the studies was mixed: 3/4 of the systematic reviews did not meet the methodological criteria, and four out of six randomized trials had at least a high risk of bias. Social marketing is not fully exploited in prevention interventions. However, the greater the number of social marketing criteria used, the more positive the effects observed. Social marketing thus appears to be an interesting concept to bring about behavioural change, but it requires rigorous monitoring to ensure maximum effectiveness.

Analysis of Saturation in the Emergency Department: A Data-Driven Queuing Model Using Machine Learning.

Emergency department is a key component of the health system where the management of crowding situations is crucial to the well-being of patients. This study proposes a new machine learning methodology and a queuing network model to measure and optimize crowding through a congestion indicator, which indicates a real-time level saturation.

Gaps to Best Practices for Teleconsultations Performed by General Practitioners: A Descriptive Cross-Sectional Study.

The COVID-19 pandemic led to large increases in telemedicine activity worldwide. This rapid growth, however, may have impacted the quality of care where compliance with guidelines and best practices are concerned. The aim of this study was to describe the recent practices of a telemedicine activity (teleconsultations) and the breaches of best practice guidelines committed by general practitioners (GPs) in the Greater Eastern Region of France. A cross-sectional study was conducted using a 33-item questionnaire and was provided to the Regional Association of Healthcare Professionals, Union Régionale des Professionnels de Santé (URPS) to be shared amongst the GPs. Between April and June 2021, a total of 233 responses were received, showing that (i) by practicing telemedicine in an urban area, (ii) performing a teleconsultation at the patient's initiative, and (iii) carrying out more than five teleconsultations per week were factors associated with a significantly higher level of best practices in telemedicine. All in all, roughly a quarter of GPs (25.3%, n = 59) had a self-declared good telemedicine practice, and the rules of good practice are of heterogeneous application. Despite the benefits of learning on the job for teleconsultation implementation during the COVID-19 lockdowns, there may be a clear need to develop structured and adapted telemedicine training programs for private practice GPs.

Understanding the surgeon's behaviour during robot-assisted surgery: protocol for the qualitative Behav'Robot study.

INTRODUCTION: Robot-assisted surgery is spreading worldwide, accounting for more than 1.2 million procedures in 2019. Data are sparse in the literature regarding the surgeon's mechanisms that mediate risk-taking during a procedure, especially robot-assisted. This study aims to describe and understand the behaviour of the surgeons during robot-assisted surgery and the change in their behaviour with increasing experience in using the robot. METHODS AND ANALYSIS: This is a qualitative study using semistructured interviews with surgeons who perform robot-assisted surgery. An interview guide comprising open questions will be used to ensure that the points to be discussed are systematically addressed during each interview (ie, (1) difference in behaviour and preparation of the surgeon between a standard procedure and a robot-assisted procedure; (2) the influence of proprioceptive modifications, gain in stability and cognitive biases, inherent in the use of a surgical robot and (3) the intrinsic effect of the learning curve on the behaviour of the surgeons. After transcription, interviews will be analysed with the help of NVivo software, using thematic analysis. ETHICS AND DISSEMINATION: Since this project examines professional practices in the field of social and human sciences, ethics committee was not required in accordance with current French legislation (Decree no 2017-884, 9 May 2017). Consent from the surgeons is implied by the fact that the interviews are voluntary. Surgeons will nonetheless be informed that they are free to interrupt the interview at any time.Results will be presented in peer-reviewed national and international congresses and submitted to peer-reviewed journals for publication. The communication and publication of the results will be placed under the responsibility of the principal investigator and publications will be prepared in compliance with the ICMJE uniform requirements for manuscripts. TRIAL REGISTRATION NUMBER: NCT04869995.

Multimorbidity clustering of the emergency department patient flow: Impact analysis of new unscheduled care clinics.

BACKGROUND: In France, the number of emergency department (ED) admissions doubled between 1996 and 2016. To cope with the resulting crowding situation, redirecting patients to new healthcare services was considered a viable solution which would spread demand more evenly across available healthcare delivery points and render care more efficient. The objective of this study was to analyze the impact of opening new on-demand care services based on variations in patient flow at a large hospital emergency department. METHODS: We performed a before-and-after study investigating the use of unscheduled care services in the Aube region in eastern France, that focused on ED attendance at Troyes Hospital. A hierarchical clustering based on co-occurrence of diagnoses was applied which divided the population into different multimorbidity profiles. Temporal trends of the resultant clusters were also studied empirically and using regression models. A multivariate logistic regression model was constructed to adjust the periodic effect for appropriate confounders and therefore confirm its presence. RESULTS: In total, 120,722 visits to the ED were recorded over a 24-month period (2018-2019) and 16 clusters were identified, accounting for 94.76% of all visits. There was a decrease of 56.77 visits per week in seven specific clusters and an increase of use of unscheduled health care services by 328.12 visits per week. CONCLUSIONS: Using an innovative and reliable methodology to evaluate changes in patient flow through the ED, these findings may help inform public health policy experts on the implementation of unscheduled care services to ease pressure on hospital EDs.

Effect of the COVID-19 pandemic lockdown on non-COVID-19 emergency department visits in Eastern France: Reduced risk or avoidance behavior?

OBJECTIVE: To study the impact of COVID-19 pandemic lockdown on avoided emergency department visits and consequent hospitalizations. STUDY DESIGN: An observational retrospective design was used to investigate avoided visits and hospitalizations of an departmental emergency department combined with a clustering approach on multimorbidity patterns. METHODS: A multimorbidity clustering technique was applied on the emergency department diagnostics to segment the population in diseases clusters. Global visits and hospitalizations from an emergency department during the 2020 lockdown were put in perspective with the same period during 2019. Using a comparison with the five previous years, avoided hospitalizations per inhabitants during the lockdown were estimated for each diseases cluster. RESULTS: During the 8 weeks of lockdown, the number of emergency department visits have been reduced by 41.47% and resultant hospitalizations by 28.50% compared to 2019. The retrospective study showed that 14 of 17 diseases clusters had a statistically significant reduction in hospitalizations with a pronounced effect on lower acuity diagnoses and middle-aged patient, leading to 293 avoided hospitalizations per 100,000 inhabitants compared to the 5 previous years and to the 85.8 COVID-19 hospitalizations per 100,000 inhabitants. CONCLUSION: Although specific to a regional context of pandemic containment, the study suggest that COVID-19 lockdown had beneficial effects on the crowding situation of the emergency departments and hospitals with avoidance effects primarily link to reduced risks.

The Use of Telemedicine in Nursing Homes: A Mixed-Method Study to Identify Critical Factors When Connecting with a General Hospital.

Evaluating the use and impact of telemedicine in nursing homes is necessary to promote improvements in the quality of this practice. Even though challenges and opportunities of telemedicine are increasingly becoming well documented for geriatrics (such as improving access to healthcare, patient management, and education while reducing costs), there is still limited knowledge on how to better implement it in an inter-organizational context, especially when considering nursing homes. In this regard, this study aimed first to describe the telemedicine activity of nursing homes when cooperating with a general hospital; and then understand the behavioral differences amongst nursing homes while identifying critical factors when implementing a telemedicine project. We conducted a sequential, explanatory mixed-method study using quantitative then qualitative methods to better understand the results. Three years of teleconsultation data of twenty-six nursing homes (15 rural and 11 urban) conducting teleconsultations with a general hospital (Troyes Hospital, France) were included for the quantitative analysis, and eleven telemedicine project managers for the qualitative analysis. Between April 2018 and April 2021, 590 teleconsultations were conducted: 45% (n = 265) were conducted for general practice, 29% (n = 172) for wound care, 11% (n = 62) for diabetes management, 8% (n = 47) with gerontologist and 6% (n = 38) for dermatology. Rural nursing homes conducted more teleconsultations overall than urban ones (RR: 2.484; 95% CI: 1.083 to 5.518; p = 0.03) and included more teleconsultations for general practice (RR: 16.305; 95% CI: 3.505 to 73.523; p = 0.001). Our qualitative study showed that three critical factors are required for the implementation of a telemedicine project in nursing homes: (1) the motivation to perform teleconsultations (in other words, improving access to care and cooperation between professionals); (2) building a relevant telemedicine medical offer based on patients' and treating physicians' needs; and (3) it's specific organization in terms of time and space. Our study showed different uses of teleconsultations according to the rural or urban localization of nursing homes and that telemedicine projects should be designed to consider this aspect. Triggered by the COVID-19 pandemic, telemedicine projects in nursing homes are increasing, and observing the three critical factors presented above could be necessary to limit the failure of such projects.

[Study of the impact of MAIA management on the rehospitalisation of patients aged 75 and over]. / Étude de l'impact d'une prise en charge MAIA sur la réhospitalisation des patients âgés de 75 ans et plus.

Discharge from hospital is a key moment in the care of patients over 75 years of age. The organisation of the transition from hospital to home by home help and care management networks can be effective. Our aim was to evaluate impact of a case management program on 30 days rehospitalisation rates. Retrospective study of the multicentre cohort type carried out on patients monitored by the MAIA of Aube between 2018 and 2020. The risk of re-hospitalisation was significantly lower at 30 days among MAIA patients (1.6% vs. 19.5%; p < 0.0001), as well as at 90 days (4.8 % vs. 35.8 %; p < 0.0001). On the other hand, lengths of stay were longer in this group (20.9 vs. 11 days; p = 0.005) and the patients consulted the emergency department more often (40.8 % vs. 17.1 %; p < 0.0001). We could not conclude on mortality and falls. A positive impact of the Aube MAIA scheme on early and late readmission to hospital was shown.

An Efficient Simulation-Based Optimization Approach for Improving Emergency Department Performance.

In recent years, health care organizations, in particular emergency department (ED), have come under increasing pressure to provide quality care. In this context, human resources are a central aspect: a good utilization of health worker could improve quality of care. In this paper, a simulation model is proposed. The model represents an ED coupled with an optimization method to optimize the allocation of medical and para-medical human resources in the hospital center of Troyes. We aim to improve the quality of services offered to patients through the minimization of Average Waiting Time (AWT) and Average Inpatient Stay (AS). The proposed approach has proved to be effective to reduce AWT and AS by 12 minutes and 21 minutes respectively.

Impact of the implementation of a fast-track on emergency department length of stay and quality of care indicators in the Champagne-Ardenne region: a before-after study.

OBJECTIVES: We aimed to evaluate the effect of the implementation of a fast-track on emergency department (ED) length of stay (LOS) and quality of care indicators. DESIGN: Adjusted before-after analysis. SETTING: A large hospital in the Champagne-Ardenne region, France. PARTICIPANTS: Patients admitted to the ED between 13 January 2015 and 13 January 2017. INTERVENTION: Implementation of a fast-track for patients with small injuries or benign medical conditions (13 January 2016). PRIMARY AND SECONDARY OUTCOME MEASURES: Proportion of patients with LOS ≥4 hours and proportion of access block situations (when patients cannot access an appropriate hospital bed within 8 hours). 7-day readmissions and 30-day readmissions. RESULTS: The ED of the intervention hospital registered 53 768 stays in 2016 and 57 965 in 2017 (+7.8%). In the intervention hospital, the median LOS was 215 min before the intervention and 186 min after the intervention. The exponentiated before-after estimator for ED LOS ≥4 hours was 0.79; 95% CI 0.77 to 0.81. The exponentiated before-after estimator for access block was 1.19; 95% CI 1.13 to 1.25. There was an increase in the proportion of 30 day readmissions in the intervention hospital (from 11.4% to 12.3%). After the intervention, the proportion of patients leaving without being seen by a physician decreased from 10.0% to 5.4%. CONCLUSIONS: The implementation of a fast-track was associated with a decrease in stays lasting ≥4 hours without a decrease in access block. Further studies are needed to evaluate the causes of variability in ED LOS and their connections to quality of care indicators.