Text Mining Patient-Doctor Online Forum Data from the Largest Online Health Community in China

The present study uses the data from the largest online health community in China, www.haodf.com, to examine what are the salient topics that Chinese health consumers discussed with their doctors online. The preliminary research found that there are 146,915 posts by patients and 123,059 posts by doctors from Aug. 2006 to Apr. 2014 on this open online forum. In total, there are 10,685 doctors have participated online forum discussion during this time period. The text mining results on topic modeling are still pending. But we already found the promising and unique quality of this data. We are also looking forward to more inspiring research questions to motivate us for this research

Crowdsourcing Argumentation Structures in Chinese Hotel Reviews

Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.Comment: 6 pages,3 figures,This article has been submitted to "The 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC2017)

Physician’s Usage Of Mobile Clinical Applications In A Community Hospital: A Longitudinal Analysis Of Adoption Behavior

It is widely believed that mobile clinical information systems can facilitate patient care, increase treatment capacity, reduce healthcare costs, and improve efficiency. Yet, there is limited research to substantiate these claims in healthcare delivery settings, partly due to lack of widespread adoption and use. This study summarizes our results on the adoption and usage trends in a community hospital which deployed several mobile clinical applications for daily patient care. We analyze twenty-two months of usage data to understand trends in physicians’ adoption and use of specific mobile applications. Applying a novel, semi-parametric, group-based, statistical methodology, we obtain developmental trajectories depicting how usage evolves from initial ‘trial’ adoption to long-term institutionalization. We examine this longitudinal developmental pattern to understand how users can be clustered and profiled, and provide insights indicating that the potential impact of social influence needs to be further explored to develop new approaches to facilitate adoption

Joint RNN Model for Argument Component Boundary Detection

Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline. Existing ACBD methods heavily depend on task-specific knowledge, and require considerable human efforts on feature-engineering. To tackle these problems, in this work, we formulate ACBD as a sequence labeling problem and propose a variety of Recurrent Neural Network (RNN) based methods, which do not use domain specific or handcrafted features beyond the relative position of the sentence in the document. In particular, we propose a novel joint RNN model that can predict whether sentences are argumentative or not, and use the predicted results to more precisely detect the argument component boundaries. We evaluate our techniques on two corpora from two different genres; results suggest that our joint RNN model obtain the state-of-the-art performance on both datasets.Comment: 6 pages, 3 figures, submitted to IEEE SMC 201

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Revisiting Medical Errors: Collaborative Errors

Medical error is a label used to refer to preventable adverse events in the healthcare setting. Errors in medical practice and service can occur at various timepoints and contexts, driven by both human and non-human factors. As healthcare continuously evolves, particularly against the backdrop of a digital landscape, it has become even more of a necessity to conduct a comprehensive examination of the causes and potential solutions for the wide array of medical errors that can occur. Conventionally, medical errors have been studied from the clinical perspective to prevent and remedy errors such as diagnostic errors, medication errors, surgical errors, and errors in medical protocol. The digitalization of healthcare practice provides new opportunities to conduct longitudinal analysis, but also presents challenges relatively new to medical error research, but familiar in the world of data quality, including data that is siloed across different timepoints and entities. As the field moves towards prevention-focused care practice, we anticipate that longitudinal data about managed care bundled by patients will become more available. This study conducts an exploratory literature review of the factors contributing to medical errors, emphasizing the interdisciplinary nature and collaborative mode in defining and mitigating errors. The medical and healthcare literature discusses the medical practice and service within a visit, test, surgery, and transfer extensively. The error research literature identifies human errors, such as, slips and mistakes, and others from individual episodes. Other literature focuses on specific types, causes, and contexts of medical errors, such as culture, leadership, training, and systems. Many empirical medical error studies are available for certain service or project period. Other studies focus on transfers of patients. We also reviewed literature on non-medical errors, such as, nuclear plants and airlines. We reviewed many organizational process literatures that discusses errors stem from knowledge sharing and boundary shifting. We also reviewed data quality literature that embeds various contexts in quality of data. We aim to review and synthesize the literature across disciplines for studying the medical errors based on a patient over time, cross multiple services, visits, and transfers in order to account for the interdisciplinary phenomenon of medical errors and collaborative errors. Based on this review, we propose a longitudinal framework and concepts to understand collaborative medical errors based on patients’ experience over time. We present several propositions on how specialized collaborative efforts might contribute to creating and solving medical errors. In addition, this review also explores the role of automation, technology, role-based communication, and evidence-based approaches in mitigating errors. This research significantly contributes to the field by challenging traditional perspectives on medical errors, expanding the scope of error analysis, and offering practical strategies for error reduction. It underscores the critical role of interdisciplinary collaboration in healthcare and provides a solid foundation for future studies in the pursuit of safer and higher-quality patient care

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

How to Utilize Data Visualization Method to Analyze Information Systems Related Medical Errors

Medical errors, such as misdiagnosis, incorrect drug dispensing, surgical injuries, even patient name errors, or misuse computer information systems in healthcare systems can cause serious consequences to patients. A meta-analysis estimates that medical errors cause approximately 22,000 preventable deaths in the United State each year (Rodwin et al., 2020). To help reduce medical errors and enhance patient safety, The Agency for Healthcare Research and Quality (AHRQ), one of twelve agencies within the United States Department of Health and Human Services, have created repository of medical error cases, which are often reports and case studies authored by clinical professionals (e.g., physicians, nurses, and hospital managers). These narratives provide patients and peer healthcare professionals with valuable lessons regarding the causes, contexts, and consequences of various medical errors, and some are following up with comments or suggestions from experienced professionals. In addition, those medical reports often have many attributes subjectively tagged by the report authors such as Computer Information Systems related, EHR related, or Telemedicine related. Some research has applied machine-learning methods (e.g., BERT) to mine medical error reports (Xu et al. 2021). The present study focuses on the attributes of medical error reports and seeks to identify and visualize the correlation between types of medical errors and the types of information systems related errors, and hope to provide patients, clinical professionals, healthcare administrators, and policy makers with straightforward illustrations to understand the medical error issues. We have acquired over 500 medical error reports from AHRQ, from 2003 to 2021. The attributes of these reports include case title, error types, clinical area, safety target, target audience, setting of care, approach to improve safety, etc. Medical errors are categorized into seven major types: Active Errors, Cognitive Errors ( Mistakes ), Epidemiology of Errors and Adverse Events, Latent Errors, Near Miss, Non-cognitive Errors ( Slips & Lapses ), and Other. Our preliminary study explores all the medical error reports with one analysis focusing on the error cases which can be improved by clinical information systems related approaches, such as Computerized Adverse Event Detection (CAED), Computer-Assisted Therapy (CAT), Clinical Information Systems (CIS), Computerized Decision Support (CDS), Computerized Provider Order Entry (CPOE), Electronic Health Records (EHR), and Telemedicine. The preliminary result (see the chart below) shows that in each of the seven error types, EHR is a major type of approach to improve in six out of seven error categories

What Can Online Doctor Reviews Tell Us? A Deep Learning Assisted Study of Telehealth Service

The present study develops a novel deep learning method which assists text mining of online doctor reviews to extract underlying sentiment scores. Those scores can be used to estimate a healthcare service quality model to investigate how the online doctor reviews impact the online doctor consultation demand. Based on the data from the largest online health platforms in China, our model results show that the underlying sentiment scores have statistically significant impacts on the demand of online doctor consultation. Theoretically, the present study constructs an innovative deep learning algorithm with a better performance than four widely used text mining methods, which can be applied to text mining of many online forums or social media texts. Empirically, our model results show what factors impact the health service quality and online doctor consultation demand, and following those factors, healthcare professionals can improve their service