Out-Patient Attitude towards Hospitals during COVID-19 in India

COVID-19, one of the worst pandemics in recent years, have changed the face of our world. Every sector has been experiencing a tug in unexpected directions than anticipated. Often it is said that the healthcare sector is facing a boom in this COVID-19 episode, nevertheless there has been a decline of out-patient segment in hospitals. An out-patient is one who visits a hospital for treatment without staying overnight. Through this time of uncertainty where a new normal is being burgeoned, the people’s attitude towards healthcare has shifted a great deal. Predominantly out-patients are hesitant to continue with their regular physician visits by delaying or avoiding unneeded visits. People with underlying diseases, both which are at low risk or at high risk, find themselves at higher caution due to the COVID-19. This study focuses to understand the attitude of out-patients and of out-patient’s with risk during this COVID-19 pandemic towards hospitals in India. Online or Tele medical consultation has picked up momentum among out-patients during the COVID-19 Unlock phase which shines a possibility as a new normal in the healthcare industry.

Sociodemographic and clinical factors, visit expectations and driving factors for emergency department attendance for uncomplicated upper respiratory tract infection

BackgroundUpper respiratory tract infections (URTIs) account for substantial non-urgent ED attendances. Hence, we explored the reasons for such attendances using a mixed-methods approach.MethodsWe interviewed adult patients with URTI who visited the second busiest adult ED in Singapore from June 2016 to November 2018 on their expectations and reasons for attendance. A structured questionnaire, with one open-ended question was used. Using the Andersen’s Behavioural Model for Healthcare Utilisation, the topmost reasons for ED attendances were categorised into (1) contextual predisposing factors (referral by primary care physician, family, friends or coworkers), (2) contextual enabling factors (convenience, accessibility, employment requirements), (3) individual enablers (personal preference and trust in hospital-perceived care quality and efficiency) and (4) individual needs (perceived illness severity and non-improvement). Multivariable multinomial logistic regression was used to assess associations between sociodemographic and clinical factors, patient expectations for ED visits and the drivers for ED attendance.ResultsThere were 717 patients in the cohort. The mean age of participants was 40.5 (SD 14.7) years, 61.2% were males, 66.5% without comorbidities and 40.7% were tertiary educated. Half had sought prior medical consultation (52.4%) and expected laboratory tests (55.7%) and radiological investigations (46.9%). Individual needs (32.8%) and enablers (25.1%) were the main drivers for ED attendance. Compared with ED attendances due to contextual enabling factors, attendances due to other drivers were more likely to be aged ≥45 years, had prior medical consultation and expected radiological investigations. Having a pre-existing medical condition (adjusted OR (aOR) 1.78, 95% CI 1.05 to 3.04) and an expectation for laboratory tests (aOR 1.64, 95% CI 1.01 to 2.64) were associated with individual needs while being non-tertiary educated (aOR 2.04, 95% CI 1.22 to 3.45) and having pre-existing comorbidities (aOR 1.79, 95% CI 1.04 to 3.10) were associated with individual enablers.ConclusionsMeeting individual needs of perceived illness severity or non-improvement was the topmost driver of ED visits for URTI, while contextual enabling factors such as convenience was the lowest. Patients’ sociodemographic and clinical factors and visit expectations influence their motivations for ED attendances. Addressing these factors and expectations can alleviate the overutilisation of ED services.

Online medical consultation: covid-19 system using software object-oriented approach

The internet has been a source of medical information, it has been used for online medical consultation (OMC). OMC is now offered by many providers internationally with diverse models and features. In OMC, consultations and treatments are available 24/7. The covid-19 pandemic across-the-board, many people unable to go to hospital or clinic because the spread of the virus. This paper tried to answer two research questions. The first one on how the OMC can help the patients during covid-19 pandemic. A literature review was conducted to answer the first research question. The second one on how to develop system in OMC related to covid-19 pandemic. The system was developed by Visual Studio 2019 using software object-oriented approach. Online expert review was conducted within 6 experts from health and academic industry to verify the model. Also, the system was validated by 11 users from heath and academic industry to confirm its usability. The statistical package for social science (SPSS 22) was used to analyze the collected data. The result of expert review confirmed that covid-19 system can help the patients. Also, the validity of the system was confirmed by 11 users from health and academic industry.

Composition-driven symptom phrase recognition for Chinese medical consultation corpora

Background Symptom phrase recognition is essential to improve the use of unstructured medical consultation corpora for the development of automated question answering systems. A majority of previous works typically require enough manually annotated training data or as complete a symptom dictionary as possible. However, when applied to real scenarios, they will face a dilemma due to the scarcity of the annotated textual resources and the diversity of the spoken language expressions. Methods In this paper, we propose a composition-driven method to recognize the symptom phrases from Chinese medical consultation corpora without any annotations. The basic idea is to directly learn models that capture the composition, i.e., the arrangement of the symptom components (semantic units of words). We introduce an automatic annotation strategy for the standard symptom phrases which are collected from multiple data sources. In particular, we combine the position information and the interaction scores between symptom components to characterize the symptom phrases. Equipped with such models, we are allowed to robustly extract symptom phrases that are not seen before. Results Without any manual annotations, our method achieves strong positive results on symptom phrase recognition tasks. Experiments also show that our method enjoys great potential with access to plenty of corpora. Conclusions Compositionality offers a feasible solution for extracting information from unstructured free text with scarce labels.

Discursive strategies of self-promotion by doctors in online medical consultations in China: an e-commercialised practice

The online mode of medical consultation has been gaining increasing popularity. Online medical consultation (OMC) in China is largely mediated through e-healthcare websites which are featured with an online evaluation system for patients and caregivers to assess OMC doctors’ service. The evaluation system facilitates an e-commercialised way for delivering healthcare services. It is of interest to study how doctors make efforts to promote themselves in the e-commercialised OMC practice, in particular how language is used to elicit positive comments and evaluations in doctors’ self-promotion. However, this, to my best knowledge, has not been studied. The present study thus examines discursive strategies for eliciting feedback by doctors who contribute to OMCs on a widely used e-healthcare website in China. By the approach of mediated discourse analysis, five strategies have been identified. These discursive strategies are discussed in relation to the disruption of stereotypical roles of doctor and patient and the influence of non-stereotypical positions on power relations between doctors and patients. This study provides a new perspective on doctor-patient relationship and serves as a starting point for further studying neoliberal medical discourse.

Enhancing unsupervised medical entity linking with multi-instance learning

Background A lot of medical mentions can be extracted from a huge amount of medical texts. In order to make use of these medical mentions, a prerequisite step is to link those medical mentions to a medical domain knowledge base (KB). This linkage of mention to a well-defined, unambiguous KB is a necessary part of the downstream application such as disease diagnosis and prescription of drugs. Such demand becomes more urgent in colloquial and informal situations like online medical consultation, where the medical language is more casual and vaguer. In this article, we propose an unsupervised method to link the Chinese medical symptom mentions to the ICD10 classification in a colloquial background. Methods We propose an unsupervised entity linking model using multi-instance learning (MIL). Our approach builds on a basic unsupervised entity linking method (named BEL), which is an embedding similarity-based EL model in this paper, and uses MIL training paradigm to boost the performance of BEL. First, we construct a dataset from an unlabeled large-scale Chinese medical consultation corpus with the help of BEL. Subsequently, we use a variety of encoders to obtain the representations of mention-context and the ICD10 entities. Then the representations are fed into a ranking network to score candidate entities. Results We evaluate the proposed model on the test dataset annotated by professional doctors. The evaluation results show that our method achieves 60.34% accuracy, exceeding the fundamental BEL by 1.72%. Conclusions We propose an unsupervised entity linking method to the entity linking in the medical domain, using MIL training manner. We annotate a test set for evaluation. The experimental results show that our model behaves better than the fundamental model BEL, and provides an insight for future research.

Migraine and tension-type headache among undergraduate medical, dental and pharmaceutical students of University of Aleppo: a cross-sectional study

IntroductionHeadache disorders are among the most common 10 causes of disability worldwide according to the global burden of disease survey 2010. Headache is also wildly common among universities students when compared with other populations. The purpose of this study is to assess headache prevalence among Aleppo University medical, dental and pharmaceutical undergraduate students.MethodsA questionnaire-based cross-sectional study was conducted among medical, dental and pharmaceutical students at Aleppo University, Syria. We determined the type of headache according to the International Classification of Headache Disorder-III. The total number of participants was 2068. A χ2 test was used to evaluate the association between the categorical outcomes. P<0.05 was considered significant.ResultsOut of 2068 participants, 1604 (77.6%) were medical students, 205 (9.9%) were dental students and 259 (12.5%) were pharmaceutical students. The effect on daily activities was higher in chronic tension headache (96.7%) and migraine without aura (94.6%) than migraine with aura (91.3) and episodic tension headache (85.1%). Out of 1191 who had a headache, only 188 (15.9%) had a medical consultation.ConclusionsThere was no a statistically significant difference in prevalence of tension headache and migraine according to faculties. There was a statistically significant difference in patients with migraine according to academic year, living with family and smoking. The effect on daily activities was higher in chronic tension-type headache and migraine without aura. There is a significant lack of medical consultation among students and most of them took over the counter analgesics depending on personal choice.