Stakeholders in safety: patient reports on unsafe clinical behaviors distinguish hospital mortality rates

Patient safety research has adapted concepts and methods from the workplace safety literature (safety climate, incident reporting) to explain why patients experience unintentional harm during clinical treatment in hospital (adverse events). Consequently, patient safety has primarily been studied through data generated by health care staff. However, because adverse events relate to patient injuries, it is suggested that patients and their families may also have valuable insights for investigating patient safety in hospitals. We conceptualized this idea by proposing that patients are stakeholders in hospital safety who, through their experiences of treatments and independence from institutional culture, can provide valid and supplementary data on unsafe clinical care. In 59 United Kingdom hospitals we investigated whether patient evaluations of care (N = 23,287 surveys) and the safety information contained in health care complaints (N = 2,017, containing 2.5 million words) explained variance in excess patient deaths (hospital mortality) beyond staff evaluations of care (N = 49,302 surveys) and incident reports (N = 242,859). The severity of reports on unsafe clinical behaviors (error and neglect) communicated in patient' health care complaints explained additional variance in hospital-level mortality rates beyond that of staff-generated data. The results indicate that patients provide valid and supplementary data on unsafe care in hospitals. Generalized to other organizational domains, the findings suggest that nonemployee stakeholders should be included in assessments of safety performance if they experience or observe unsafe behaviors. Theoretically, it is necessary to further examine how concepts such as safety climate can incorporate the observations and outcomes of stakeholders in safety

Identification of Consumer Adverse Drug Reaction Messages on Social Media

The prevalence of social media has resulted in spikes of data on the Internet which can have potential use to assist in many aspects of human life. One prospective use of the data is in the development of an early warning system to monitor consumer Adverse Drug Reactions (ADRs). The direct reporting of ADRs by consumers is playing an increasingly important role in the world of pharmacovigilance. Social media provides patients a platform to exchange their experiences regarding the use of certain drugs. However, the messages posted on those social media networks contain both ADR related messages (positive examples) and non-ADR related messages (negative examples). In this paper, we integrate text mining and partially supervised learning methods to automatically extract and classify messages posted on social media networks into positive and negative examples. Our findings can provide managerial insights into how social media analytics can improve not only postmarketing surveillance, but also other problem domains where large quantity of user-generated content is available

Am I hurt?: Evaluating Psychological Pain Detection in Hindi Text using Transformer-based Models

The automated evaluation of pain is critical for developing effective pain management approaches that seek to alleviate while preserving patients’ functioning. Transformer-based models can aid in detecting pain from Hindi text data gathered from social media by leveraging their ability to capture complex language patterns and contextual information. By understanding the nuances and context of Hindi text, transformer models can effectively identify linguistic cues, sentiment and expressions associated with pain enabling the detection and analysis of pain-related content present in social media posts. The purpose of this research is to analyse the feasibility of utilizing NLP techniques to automatically identify pain within Hindi textual data, providing a valuable tool for pain assessment in Hindi-speaking populations. The research showcases the HindiPainNet model, a deep neural network that employs the IndicBERT model, classifying the dataset into two class labels {pain, no_pain} for detecting pain in Hindi textual data. The model is trained and tested using a novel dataset, दर्द-ए-शायरी (pronounced as Dard-e-Shayari) curated using posts from social media platforms. The results demonstrate the model’s effectiveness, achieving an accuracy of 70.5%. This pioneer research highlights the potential of utilizing textual data from diverse sources to identify and understand pain experiences based on psychosocial factors. This research could pave the path for the development of automated pain assessment tools that help medical professionals comprehend and treat pain in Hindi speaking populations. Additionally, it opens avenues to conduct further NLP-based multilingual pain detection research, addressing the needs of diverse language communities

Customer Complaints Auto-assignment using Machine Learning Algorithms

Managing customer complaints is a very challenging activity, especially when it comes to a government entity like Roads a Transport Authority (RTA) that manages the roads infrastructure and services. RTA for the management of the customer complaints requires a sophisticated and intelligent responsive system for the large volume of cases and calls, which result in needs to a lot of human resources to receive, record and handle customer calls and cases. The key customer-save course of action is the complaints-handling process. Customers who complain to service providers and are well treated by the process are less likely to churn than customers who have no cause for complaint. In other words, a well-designed, easy-to-engage and the responsive complaints-handling process can build loyalty. (Buttle, 2016) Knowing how essential to have a well-designed complaint management system, organizations work to leverage the advantages of technologies enhancement to efficiently manage customer cases with minimal resources utilization. The critical success for that is the utilization of the most valuable asset to the organization (customer complaint data). The data gets its increasing values with the advancement of data analytics and its application in recent year. For many organizations, the data analytics usage does not go beyond the traditional descriptive analysis that describes what happened and take the necessary corrective action. Although there were a lot of attempts and research to utilize machine learning algorithms to classify customer complaints, most of falls in the area of sentiments analysis or high level topic molding identifying customer feelings or deciding what topic he/she is talking or complaining about. Actually, organization such RTA needs more that, it is the time to optimize the benefit of using Artificial Intelligence power in operational system beyond the high level text classification. The real need for RTA is to equip complaint management system with AI algorithms that help in classifying and auto-assigning the complaint to the respective department based directly without the need for human intervention. The advantage RTA has is that, it has implemented an important change in complaint management system by classifying (labeling) most of the common scenarios of complaints based on the historical data which paves the way to the use of AI-Text Classification algorithms. This project is an attempt to extend the benefits of data analytics to help not only in understanding the customer\u27s pain points but also to help in managing customer complaints end to end using the application of machine learning and artificial intelligence

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental healthconditions (e.g., depression) from various sources of information, includingsocial media. Yet, deployment of such models in real-world healthcareapplications faces challenges including poor out-of-domain generalization andlack of trust in black box models. In this work, we propose approaches fordepression detection that are constrained to different degrees by the presenceof symptoms described in PHQ9, a questionnaire used by clinicians in thedepression screening process. In dataset-transfer experiments on three socialmedia datasets, we find that grounding the model in PHQ9's symptomssubstantially improves its ability to generalize to out-of-distribution datacompared to a standard BERT-based approach. Furthermore, this approach canstill perform competitively on in-domain data. These results and ourqualitative analyses suggest that grounding model predictions inclinically-relevant symptoms can improve generalizability while producing amodel that is easier to inspect.<br

How Fair Is IS Research?

While both information systems and machine learning are not neutral, the identification of discrimination is more difficult if a system learns from data and discrimination can be introduced at several stages. Therefore, this article investigates if IS Research has taken up with this topic. A literature analysis is conducted and its discussion shows that technology, organization, and human aspects have to be considered, making it a topic not only for data scientist or computer scientist, but for information systems researchers as well

Mining Social Media to Understand Consumers' Health Concerns and the Public's Opinion on Controversial Health Topics.

Social media websites are increasingly used by the general public as a venue to express health concerns and discuss controversial medical and public health issues. This information could be utilized for the purposes of public health surveillance as well as solicitation of public opinions. In this thesis, I developed methods to extract health-related information from multiple sources of social media data, and conducted studies to generate insights from the extracted information using text-mining techniques. To understand the availability and characteristics of health-related information in social media, I first identified the users who seek health information online and participate in online health community, and analyzed their motivations and behavior by two case studies of user-created groups on MedHelp and a diabetes online community on Twitter. Through a review of tweets mentioning eye-related medical concepts identified by MetaMap, I diagnosed the common reasons of tweets mislabeled by natural language processing tools tuned for biomedical texts, and trained a classifier to exclude non medically-relevant tweets to increase the precision of the extracted data. Furthermore, I conducted two studies to evaluate the effectiveness of understanding public opinions on controversial medical and public health issues from social media information using text-mining techniques. The first study applied topic modeling and text summarization to automatically distill users' key concerns about the purported link between autism and vaccines. The outputs of two methods cover most of the public concerns of MMR vaccines reported in previous survey studies. In the second study, I estimated the public's view on the ac{ACA} by applying sentiment analysis to four years of Twitter data, and demonstrated that the the rates of positive/negative responses measured by tweet sentiment are in general agreement with the results of Kaiser Family Foundation Poll. Finally, I designed and implemented a system which can automatically collect and analyze online news comments to help researchers, public health workers, and policy makers to better monitor and understand the public's opinion on issues such as controversial health-related topics.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120714/1/owenliu_1.pd

Making sense of text: artificial intelligence-enabled content analysis

Purpose: The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis. Design/methodology/approach: To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency. Findings: Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency. Research limitations/implications: This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches. Practical implications: For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines. Originality/value: To the best of the authors' knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis