Understanding Discussions of Health Issues on Twitter: A Visual Analytic Study.

Social media allows for the exploration of online discussions of health issues outside of traditional health spaces. Twitter is one of the largest social media platforms that allows users to post short comments (i.e., tweets). The unrestricted access to opinions and a large user base makes Twitter a major source for collection and quick dissemination of some health information. Health organizations, individuals, news organizations, businesses, and a host of other entities discuss health issues on Twitter. However, the enormous number of tweets presents challenges to those who seek to improve their knowledge of health issues. For instance, it is difficult to understand the overall sentiment on a health issue or the central message of the discourse. For Twitter to be an effective tool for health promotion, stakeholders need to be able to understand, analyze, and appraise health information and discussions on this platform. The purpose of this paper is to examine how a visual analytic study can provide insight into a variety of health issues on Twitter. Visual analytics enhances the understanding of data by combining computational models with interactive visualizations. Our study demonstrates how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on Twitter. In this paper, we report on the process of data collection, analysis of data, and representation of results. We present our findings and discuss the implications of this work to support the use of Twitter for health promotion.

VINCENT: A visual analytics system for investigating the online vaccine debate.

This paper reports and describes VINCENT, a visual analytics system that is designed to help public health stakeholders (i.e., users) make sense of data from websites involved in the online debate about vaccines. VINCENT allows users to explore visualizations of data from a group of 37 vaccine-focused websites. These websites differ in their position on vaccines, topics of focus about vaccines, geographic location, and sentiment towards the efficacy and morality of vaccines, specific and general ones. By integrating webometrics, natural language processing of website text, data visualization, and human-data interaction, VINCENT helps users explore complex data that would be difficult to understand, and, if at all possible, to analyze without the aid of computational tools. The objectives of this paper are to explore A) the feasibility of developing a visual analytics system that integrates webometrics, natural language processing of website text, data visualization, and human-data interaction in a seamless manner; B) how a visual analytics system can help with the investigation of the online vaccine debate; and C) what needs to be taken into consideration when developing such a system. This paper demonstrates that visual analytics systems can integrate different computational techniques; that such systems can help with the exploration of online public health debates that are distributed across a set of websites; and that care should go into the design of the different components of such systems.

A Qualitative Study of the Theory Behind the Chairs: Balancing Lean-Accelerated Patient Flow With the Need for Privacy and Confidentiality in an Emergency Medicine Setting.

BACKGROUND: Many emergency departments (EDs) have used the Lean methodology to guide the restructuring of their practice environments and patient care processes. Despite research cautioning that the layout and design of treatment areas can increase patients' vulnerability to privacy breaches, evaluations of Lean interventions have ignored the potential impact of these on patients' informational and physical privacy. If professional regulatory organizations are going to require that nurses and physicians interact with their patients privately and confidentially, we need to examine the degrees to which their practice environment supports them to do so. OBJECTIVE: This study explored how a Lean intervention impacted the ability of emergency medicine physicians and nurses to optimize conditions of privacy and confidentiality for patients under their care. METHODS: From July to December 2017, semistructured interviews were iteratively conducted with health care professionals practicing emergency medicine at a single teaching hospital in Ontario, Canada. The hospital has 1000 beds, and approximately 128,000 patients visit its 2 EDs annually. In response to poor wait times, in 2013, the hospital's 2 EDs underwent a Lean redesign. As the interviews proceeded, information from their transcripts was first coded into topics and then organized into themes. Data collection continued to theoretical sufficiency. RESULTS: Overall, 15 nurses and 5 physicians were interviewed. A major component of the Lean intervention was the construction of a three-zone front cell at both sites. Each zone was outfitted with a set of chairs in an open concept configuration. Although, in theory, professionals perceived value in having the chairs, in practice, these served multiple, and often, competing uses by patients, family members, and visitors. In an attempt to work around limitations they encountered and keep patients flowing, professionals often needed to move a patient out from a front chair and actively search for another location that better protected individuals' informational and physical privacy. CONCLUSIONS: To our knowledge, this is the first qualitative study of the impact of a Lean intervention on patient privacy and confidentiality. The physical configuration of the front cell often intensified the clinical work of professionals because they needed to actively search for spaces better affording privacy and confidentiality for patient encounters. These searches likely increased clinical time and added to these patients' length of stay. We advocate that the physical structure and configuration of the front cell should be re-examined under the lens of Lean's principle of value-added activities. Future exploration of the perspectives of patients, family members, and visitors regarding the relative importance of privacy and confidentiality during emergency care is warranted.

Easier and Faster Is Not Always Better: Grounded Theory of the Impact of Large-Scale System Transformation on the Clinical Work of Emergency Medicine Nurses and Physicians.

BACKGROUND: The effectiveness of Lean Thinking as a quality improvement method for health care has been contested due, in part, to our limited contextual understanding of how it affects the working conditions and clinical workflow of nurses and physicians. Although there are some initial indications, arising from prevalence surveys and interviews, that Lean may intensify work performed within medical environments, the evidence base still requires detailed descriptions of the changes that were actually introduced to individuals' clinical workflow and how these changes impacted health care professionals. OBJECTIVE: The aim of this study was to explore ways in which a Lean intervention may impact the clinical work of emergency medicine nurses and physicians. METHODS: We used a realist grounded theory approach to explore the clinical work of nurses and physicians practicing in 2 emergency medicine departments from a single teaching hospital in Canada. The hospital has 1000 beds with 128,000 emergency department (ED) visits annually. In 2013, both sites began a large-scale, Lean-driven system transformation of their practice environments. In-person interviews were iteratively conducted with health care professionals from July to December 2017. Information from transcripts was coded into categories and compared with existing codes. With repeated review of transcripts and evolving coding, we organized categories into themes. Data collection continued to theoretical sufficiency. RESULTS: A total of 15 emergency medicine nurses and 5 physicians were interviewed. Of these, 18 individuals had practiced for at least 10 years. Our grounded theory involved 3 themes: (1) organization of our clinical work, (2) pushed pace in the front cell, and (3) the toll this all takes on us. Although the intervention was supposed to make the EDs work easier, faster, and better, the participants in our study indicated that the changes made had the opposite impact. Nurses and physicians described ways in which the reconfigured EDs disrupted their established practice routines and resulted in the intensification of their work. Participants also identified indications of deskilling of nurses' work and how the new push-forward model of patient care had detrimental impacts on their physical, cognitive, and emotional well-being. CONCLUSIONS: To our knowledge, this is the first study to describe the impact of Lean health care on the working conditions and actual work of emergency medicine nurses and physicians. We theorize that rather than support health care professionals in their management of the complexities that characterize emergency medicine, the physical and process-based changes introduced by the Lean intervention acted to further complicate their working environment. We have illuminated some unintended consequences associated with accelerating patient flow on the clinical workflow and perceived well-being of health care professionals. We identify some areas for reconsideration by the departments and put forward ideas for future research.

Interactive visualization of public health indicators to support policymaking: An exploratory study.

The purpose of this study is to examine the use of interactive visualizations to represent data/information related to social determinants of health and public health indicators, and to investigate the benefits of such visualizations for health policymaking. METHODS: The study developed a prototype for an online interactive visualization tool that represents the social determinants of health. The study participants explored and used the tool. The tool was evaluated using the informal user experience evaluation method. This method involves the prospective users of a tool to use and play with it and their feedback to be collected through interviews. RESULTS: Using visualizations to represent and interact with health indicators has advantages over traditional representation techniques that do not allow users to interact with the information. Communicating healthcare indicators to policymakers is a complex task because of the complexity of the indicators, diversity of audiences, and different audience needs. This complexity can lead to information misinterpretation, which occurs when users of the health data ignore or do not know why, where, and how the data has been produced, or where and how it can be used. CONCLUSIONS: Public health policymaking is a complex process, and data is only one element among others needed in this complex process. Researchers and healthcare organizations should conduct a strategic evaluation to assess the usability of interactive visualizations and decision support tools before investing in these tools. Such evaluation should take into consideration the cost, ease of use, learnability, and efficiency of those tools, and the factors that influence policymaking.

Ontology-Driven Search and Triage: Design of a Web-Based Visual Interface for MEDLINE.

BACKGROUND: Diverse users need to search health and medical literature to satisfy open-ended goals such as making evidence-based decisions and updating their knowledge. However, doing so is challenging due to at least two major difficulties: (1) articulating information needs using accurate vocabulary and (2) dealing with large document sets returned from searches. Common search interfaces such as PubMed do not provide adequate support for exploratory search tasks. OBJECTIVE: Our objective was to improve support for exploratory search tasks by combining two strategies in the design of an interactive visual interface by (1) using a formal ontology to help users build domain-specific knowledge and vocabulary and (2) providing multi-stage triaging support to help mitigate the information overload problem. METHODS: We developed a Web-based tool, Ontology-Driven Visual Search and Triage Interface for MEDLINE (OVERT-MED), to test our design ideas. We implemented a custom searchable index of MEDLINE, which comprises approximately 25 million document citations. We chose a popular biomedical ontology, the Human Phenotype Ontology (HPO), to test our solution to the vocabulary problem. We implemented multistage triaging support in OVERT-MED, with the aid of interactive visualization techniques, to help users deal with large document sets returned from searches. RESULTS: Formative evaluation suggests that the design features in OVERT-MED are helpful in addressing the two major difficulties described above. Using a formal ontology seems to help users articulate their information needs with more accurate vocabulary. In addition, multistage triaging combined with interactive visualizations shows promise in mitigating the information overload problem. CONCLUSIONS: Our strategies appear to be valuable in addressing the two major problems in exploratory search. Although we tested OVERT-MED with a particular ontology and document collection, we anticipate that our strategies can be transferred successfully to other contexts.

Beyond simple charts: Design of visualizations for big health data.

Health data is often big data due to its high volume, low veracity, great variety, and high velocity. Big health data has the potential to improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. Interactive visualizations have the potential to amplify big data's utilization. Visualizations can be used to support a variety of tasks, such as tracking the geographic distribution of diseases, analyzing the prevalence of disease, triaging medical records, predicting outbreaks, and discovering at-risk populations. Currently, many health visualization tools use simple charts, such as bar charts and scatter plots, that only represent few facets of data. These tools, while beneficial for simple perceptual and cognitive tasks, are ineffective when dealing with more complex sensemaking tasks that involve exploration of various facets and elements of big data simultaneously. There is need for sophisticated and elaborate visualizations that encode many facets of data and support human-data interaction with big data and more complex tasks. When not approached systematically, design of such visualizations is labor-intensive, and the resulting designs may not facilitate big-data-driven tasks. Conceptual frameworks that guide the design of visualizations for big data can make the design process more manageable and result in more effective visualizations. In this paper, we demonstrate how a framework-based approach can help designers create novel, elaborate, non-trivial visualizations for big health data. We present four visualizations that are components of a larger tool for making sense of large-scale public health data.

Reconsidering healthcare evidence as dynamic and distributed: the role of information and cognition.

AIM: The basic thrust of evidence-based healthcare is that current best evidence should be used explicitly and judiciously for diagnosis, management, and other activities in healthcare settings. For this to be possible, researchers, practitioners, and other stakeholders must have a clear and accurate conceptualization of what constitutes 'evidence' in healthcare environments, and the manner in which it is used in decision-making and other activities. Currently, the dominant conceptualization of evidence is that of a body of information that can be retrieved by stakeholders for use in healthcare practice. The aim of this article is to critically examine the concept of evidence, particularly in light of recent models of human cognition and information use in decision-making and other cognitive activities. METHODS: In this theoretical article, we employ both analytical and synthetic methods to critically examine the concepts under investigation. Key concepts, such as evidence and information, and the essential relationships between them are analyzed from the vantage point of cognitive science, information science, and other relevant disciplines to explicate a conceptualization of evidence that moves past static and objectivist accounts. RESULTS: We demonstrate that evidence is fundamentally information that takes various forms-i.e., artifacts, mental structures, or communication processes. Specific forms and manifestations of evidence can thus be described in the context of information use in dynamic information environments. Furthermore, evidence-based healthcare activities are shown to be fundamentally cognitive in nature. For any given evidence-based healthcare activity, its quality and outcome can be understood in the context of how different sources of evidence are coordinated within a distributed cognitive system. In this sense, evidence based health care activity becomes more a matter of understanding the movement of information and knowledge within a distributed and dynamic cognitive system than mere access to or translation of a ready-at-hand resource. CONCLUSIONS: The conceptualization of evidence presented in this article has a number of implications for evidence-based healthcare-in terms of where attention is focused, the direction of future research efforts, how evidence generation, use, and practice are conceptualized and discussed, and how healthcare technologies are designed and evaluated. Furthermore, the conceptualization presented in this article has implications for the manner in which evidence 'hierarchies' are developed. Such hierarchies do not provide a complete picture of evidence and the way it is used in healthcare activities. Understanding the dynamic nature of evidence and its role in distributed cognitive activities may lead to more robust and multi-faceted taxonomies, frameworks, and hierarchies related to evidence-based healthcare.

The challenge of big data in public health: an opportunity for visual analytics.

Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data's volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research.

Beyond information access: Support for complex cognitive activities in public health informatics tools.

Public health professionals work with a variety of information sources to carry out their everyday activities. In recent years, interactive computational tools have become deeply embedded in such activities. Unlike the early days of computational tool use, the potential of tools nowadays is not limited to simply providing access to information; rather, they can act as powerful mediators of human-information discourse, enabling rich interaction with public health information. If public health informatics tools are designed and used properly, they can facilitate, enhance, and support the performance of complex cognitive activities that are essential to public health informatics, such as problem solving, forecasting, sense-making, and planning. However, the effective design and evaluation of public health informatics tools requires an understanding of the cognitive and perceptual issues pertaining to how humans work and think with information to perform such activities. This paper draws on research that has examined some of the relevant issues, including interaction design, complex cognition, and visual representations, to offer some human-centered design and evaluation considerations for public health informatics tools.