Sahiti Myneni

Recovery at the edge of error: Debunking the myth of the infallible expert

The notion that human error should not be tolerated is prevalent in both the public and personal perception of the performance of clinicians. However, researchers in other safety-critical domains have long since abandoned the quest for zero defects as an impractical goal, choosing to focus instead on the development of strategies to enhance the ability to recover from error. This paper presents a cognitive framework for the study of error recovery, and the results of our empirical research into error detection and recovery in the critical care domain, using both laboratory-based and naturalistic approaches. Both attending physicians and residents were prone to commit, detect and recover from errors, but the nature of these errors was different. Experts corrected the errors as soon as they detected them and were better able to detect errors requiring integration of multiple elements in the case. Residents were more cautious in making decisions showing a slower error recovery pattern, and the detected errors were more procedural in nature with specific patient outcomes. Error detection and correction are shown to be dependent on expertise, and on the nature of the everyday tasks of the clinicians concerned. Understanding the limits and failures of human decision-making is important if we are to build robust decision-support systems to manage the boundaries of risk of error in decision-making. Detection and correction of potential error is an integral part of cognitive work in the complex, critical care workplace.

Content-Driven Analysis of an Online Community for Smoking Cessation: Integration of Qualitative Techniques, Automated Text Analysis, and Affiliation Networks

OBJECTIVES We identified content-specific patterns of network diffusion underlying smoking cessation in the context of online platforms, with the aim of generating targeted intervention strategies. METHODS QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated text analysis, and affiliation network analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior. RESULTS Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence. CONCLUSIONS Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion.

Finding meaning in social media: content-based social network analysis of QuitNet to identify new opportunities for health promotion.

Unhealthy behaviors increase individual health risks and are a socioeconomic burden. Harnessing social influence is perceived as fundamental for interventions to influence health-related behaviors. However, the mechanisms through which social influence occurs are poorly understood. Online social networks provide the opportunity to understand these mechanisms as they digitally archive communication between members. In this paper, we present a methodology for content-based social network analysis, combining qualitative coding, automated text analysis, and formal network analysis such that network structure is determined by the content of messages exchanged between members. We apply this approach to characterize the communication between members of QuitNet, an online social network for smoking cessation. Results indicate that the method identifies meaningful theme-based social sub-networks. Modeling social network data using this method can provide us with theme-specific insights such as the identities of opinion leaders and sub-community clusters. Implications for design of targeted social interventions are discussed.

In Pursuit of Theoretical Ground in Behavior Change Support Systems: Analysis of Peer-to-Peer Communication in a Health-Related Online Community

Background Research studies involving health-related online communities have focused on examining network structure to understand mechanisms underlying behavior change. Content analysis of the messages exchanged in these communities has been limited to the “social support” perspective. However, existing behavior change theories suggest that message content plays a prominent role reflecting several sociocognitive factors that affect an individual’s efforts to make a lifestyle change. An understanding of these factors is imperative to identify and harness the mechanisms of behavior change in the Health 2.0 era. Objective The objective of this work is two-fold: (1) to harness digital communication data to capture essential meaning of communication and factors affecting a desired behavior change, and (2) to understand the applicability of existing behavior change theories to characterize peer-to-peer communication in online platforms. Methods In this paper, we describe grounded theory–based qualitative analysis of digital communication in QuitNet, an online community promoting smoking cessation. A database of 16,492 de-identified public messages from 1456 users from March 1-April 30, 2007, was used in our study. We analyzed 795 messages using grounded theory techniques to ensure thematic saturation. This analysis enabled identification of key concepts contained in the messages exchanged by QuitNet members, allowing us to understand the sociobehavioral intricacies underlying an individual’s efforts to cease smoking in a group setting. We further ascertained the relevance of the identified themes to theoretical constructs in existing behavior change theories (eg, Health Belief Model) and theoretically linked techniques of behavior change taxonomy. Results We identified 43 different concepts, which were then grouped under 12 themes based on analysis of 795 messages. Examples of concepts include “sleepiness,” “pledge,” “patch,” “spouse,” and “slip.” Examples of themes include “traditions,” “social support,” “obstacles,” “relapse,” and “cravings.” Results indicate that themes consisting of member-generated strategies such as “virtual bonfires” and “pledges” were related to the highest number of theoretical constructs from the existing behavior change theories. In addition, results indicate that the member-generated communication content supports sociocognitive constructs from more than one behavior change model, unlike the majority of the existing theory-driven interventions. Conclusions With the onset of mobile phones and ubiquitous Internet connectivity, online social network data reflect the intricacies of human health behavior as experienced by health consumers in real time. This study offers methodological insights for qualitative investigations that examine the various kinds of behavioral constructs prevalent in the messages exchanged among users of online communities. Theoretically, this study establishes the manifestation of existing behavior change theories in QuitNet-like online health communities. Pragmatically, it sets the stage for real-time, data-driven sociobehavioral interventions promoting healthy lifestyle modifications by allowing us to understand the emergent user needs to sustain a desired behavior change.

Socially Influencing Technologies for Health Promotion: Translating Social Media Analytics into Consumer-facing Health Solutions

Health-related online communities are increasingly popular platforms on which consumers engage in peer-to-peer communication while seeking and providing health-related information. These platforms provide an empirical account of user communications related to health behaviors. Several advanced analytical approaches have been developed to unearth and characterize social influence mechanisms embedded in these platforms. However, translating these insights into design features of consumer-facing health promotion information systems presents very significant challenges. In this paper, we present a design methodology that utilizes persuasive principles and lessons learned from large-scale analysis of an online community for smoking cessation, to harness social influence and implement as technological features of a behavior support intervention. We transformed observed social diffusion patterns underlying user communication events to user-participatory interactions that can potentially lead to superior user engagement through meaningful network affiliations and thence to sustained healthy behavior change. Preliminary evaluation study and future steps are discussed.

Towards an Ontology-driven Framework to Enable Development of Personalized mHealth Solutions for Cancer Survivors' Engagement in Healthy Living

Adolescent and Young Adult (AYA) cancer survivors manage an array of health-related issues. Survivorship Care Plans (SCPs) have the potential to empower these young survivors by providing information regarding treatment summary, late-effects of cancer therapies, healthy lifestyle guidance, coping with work-life-health balance, and follow-up care. However, current mHealth infrastructure used to deliver SCPs has been limited in terms of flexibility, engagement, and reusability. The objective of this study is to develop an ontology-driven survivor engagement framework to facilitate rapid development of mobile apps that are targeted, extensible, and engaging. The major components include ontology models, patient engagement features, and behavioral intervention technologies. We apply the proposed framework to characterize individual building blocks (“survivor digilegos”), which form the basis for mHealth tools that address user needs across the cancer care continuum. Results indicate that the framework (a) allows identification of AYA survivorship components, (b) facilitates infusion of engagement elements, and (c) integrates behavior change constructs into the design architecture of survivorship applications. Implications for design of patient-engaging chronic disease management solutions are discussed.

Leveraging Social Media for Health Promotion and Behavior Change: Methods of Analysis and Opportunities for Intervention

This chapter describes methodologies used to describe, model, and predict user communication patterns in social media interactions, with the shared goal of facilitating understanding of health-related behavior change. To set the stage, the chapter presents an overview of the documented effects of social relationships on health behavior change. Investigators from a variety of disciplines have attempted to understand and harness these social ties for health promotion. Online communities, which digitize peer-to-peer communication, provide a unique opportunity to researchers to understand the mechanisms underlying human behavior change. Through transdisciplinary methods that draw upon socio-behavioral theories, and information and network sciences, analysis of communication patterns underlying social media user interactions is possible at scale. Such methods can provide insight into development of “healthier life” technologies that harness the power of social connections. Examples of such translational projects and implications for public health practice are discussed to conclude the chapter.

Resolving complex research data management issues in biomedical laboratories

This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to (1) characterize specific problems faced by biomedical researchers with traditional information management practices, (2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to (3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity.

High-content screening data management for drug discovery in a small- to medium-size laboratory: results of a collaborative pilot study focused on user expectations as indicators of effectiveness.

High-content screening (HCS) technology provides a powerful vantage point to approach biological problems; it allows analysis of cell parameters, including changes in cell or protein movement, shape, or texture. As part of a collaborative pilot research project to improve bioscience research data integration, we identified HCS data management as an area ripe for advancement. A primary goal was to develop an integrated data management and analysis system suitable for small- to medium-size HCS programs that would improve research productivity and increase work satisfaction. A system was developed that uses Labmatrix, a Web-based research data management platform, to integrate and query data derived from a Cellomics STORE database. Focusing on user expectations, several barriers to HCS productivity were identified and reduced or eliminated. The impact of the project on HCS research productivity was tested through a series of 18 lab-requested integrated data queries, 7 of which were fully enabled, 7 partially enabled, and 4 enabled through data export to standalone data analysis tools. The results are limited to one laboratory, but this pilot suggests that through an “implementation research” approach, a network of small- to medium-size laboratories involved in HCS projects could achieve greater productivity and satisfaction in drug discovery research.

The Portrayal of Quit Emotions: Content-Sensitive Analysis of Peer Interactions in an Online Community for Smoking Cessation

Tobacco use causes serious emotional harm among smokers and it manifests in the form of mood disorders such as depression and anxiety. The effects of smoking cessation on quality of life are well documented. However, our understanding of emotional well-being of an individual in the window of quit and relapse period to provide just in time support is quite limited. In this study, we focus on social engagement, communication attributes, and emotional landscape of successful quitters as manifested in peer interactions of an online health community for smoking cessation. Further, we employed Word Embedding techniques to analyze the content-specific communication attributes in a given quit episode at scale. Results indicate users were highly engaged after a quit. The emotional index of successful quitters highlighted the fragile and complex nature of sentiments associated with a quit episode. The behavior change techniques popular before quit were ‘goals and planning’ and ‘self-belief’ and after quit were ‘feedback and monitoring’ and ‘goals and planning’. Communication genres popular before quit were ‘family and friends’ and ‘quit readiness’, whereas focus on ‘traditions’, ‘quit progress’ and ‘quit obstacles’ was high after quit. Implications for development of real-time interventions that are mindful of emotional and informational support are discussed.