Background The palliative care unit is an emotionally challenging place where patients and their families may feel at loss. Art can allow the expression of complex feelings. We aimed to examine how cancer patients hospitalized in the palliative care unit experienced a musical intervention. Methods We conducted a qualitative study based on semi-structured interviews. The study took place in a palliative care unit from 18 January 2017 to 17 May 2017. Two artists performed in the palliative care unit once a week from 9:30 am to 5:30 pm. The data from patient interviews were analysed based on an inductive approach to the verbatim accounts. Results The accounts we gathered led us to weigh the positive emotions engendered by this musical intervention against the potential difficulties encountered. The artists opened a parenthesis in the care process and brought joy and well-being to the palliative care unit. Patients also encountered difficulties during the intervention: reference to an altered general state, to loss of autonomy; a sense of the effort required, of fatigue; an adaptation period; reference to the end of life, to death; a difficulty in choosing songs. Conclusions Although music appeared to benefit the patients, it sometimes reminded them of their altered state. The difficulties experienced by patients during the experience were also related to physical exhaustion. Additional studies are needed to determine the benefits of music for patients and their families in the palliative care unit.

ObjectivesWe 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.DesignAdjusted before–after analysis.SettingA large hospital in the Champagne-Ardenne region, France.ParticipantsPatients admitted to the ED between 13 January 2015 and 13 January 2017.InterventionImplementation of a fast-track for patients with small injuries or benign medical conditions (13 January 2016).Primary and secondary outcome measuresProportion 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.ResultsThe 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%.ConclusionsThe 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.

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Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

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.

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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.

BackgroundGeneric drug substitution is a public health policy challenge with high economic potential. Generic drugs are generally cheaper than brand-name drugs. Drugs are a significant part of the total health expenditure, especially in ambulatory care. We conducted a cross-sectional study with general practitioners in the Champagne-Ardenne region to determine physician-related factors and beliefs causing doctors to use the Not for Generic Substitution (NGS) mention.MethodsQuestionnaires were sent to General Practitioners (GPs) practicing in Champagne-Ardenne via 3 shipments, from January 2015 to May 2015. Prescriber characteristics and beliefs influencing the use of the NGS mention were assessed for frequent (≥ 5%) and less frequent (< 5%) users of the NGS mention.ResultsFactors associated with above average NGS mention use in bivariate analysis included patient comorbidity, polypharmacy, a concern that generic and brand-name drugs are not bioequivalent and belief in higher efficacy of the brand name drug. The use of an e-prescribing system (EPS) and medical practice in rural areas appeared to be associated with lower use of NGS mention in bivariate analysis but not in multivariable analysis. In multivariable analysis, patient request was associated with a higher use of the NGS mention (NGS ≥ 5%, adjusted Odds Ratio (aOR) = 2.52; 95% CI = [1.46–4.35]; p = 0.001), which was also linked to patient age over 65 (NGS ≥ 5%, aOR = 2.33; 95% CI = [1.03–5.30]; p = 0.04). The NGS mention was often used for drugs where substitution is debated in the literature (thyroid hormones, antiepileptic drugs).ConclusionThis work highlights the involvement of the doctor-patient pair for the use of the NGS mention. Patient request was the major reason for using the NGS mention, even though it was not always endorsed by prescribers. Further studies are needed to assess patient views on generic drugs and drug substitution, accounting for their health status and socio-economic condition, to help improve the relevance of the information available to them.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3652-2) contains supplementary material, which is available to authorized users.