Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications

Objective: Artificial intelligence (AI) provides people and professionals working in the field of participatory health informatics an opportunity to derive robust insights from a variety of online sources. The objective of this paper is to identify current state of the art and application areas of AI in the context of participatory health. Methods: A search was conducted across seven databases (PubMed, Embase, CINAHL, PsychInfo, ACM Digital Library, IEEExplore, and SCOPUS), covering articles published since 2013. Additionally, clinical trials involving AI in participatory health contexts registered at clinicaltrials.gov were collected and analyzed. Results: Twenty-two articles and 12 trials were selected for review. The most common application of AI in participatory health was the secondary analysis of social media data: self-reported data including patient experiences with healthcare facilities, reports of adverse drug reactions, safety and efficacy concerns about over-the-counter medications, and other perspectives on medications. Other application areas included determining which online forum threads required moderator assistance, identifying users who were likely to drop out from a forum, extracting terms used in an online forum to learn its vocabulary, highlighting contextual information that is missing from online questions and answers, and paraphrasing technical medical terms for consumers. Conclusions: While AI for supporting participatory health is still in its infancy, there are a number of important research priorities that should be considered for the advancement of the field. Further research evaluating the impact of AI in participatory health informatics on the psychosocial wellbeing of individuals would help in facilitating the wider acceptance of AI into the healthcare ecosystem

The organizational implications of medical imaging in the context of Malaysian hospitals

This research investigated the implementation and use of medical imaging in the context of Malaysian hospitals. In this report medical imaging refers to PACS, RIS/HIS and imaging modalities which are linked through a computer network. The study examined how the internal context of a hospital and its external context together influenced the implementation of medical imaging, and how this in turn shaped organizational roles and relationships within the hospital itself. It further investigated how the implementation of the technology in one hospital affected its implementation in another hospital. The research used systems theory as the theoretical framework for the study. Methodologically, the study used a case-based approach and multiple methods to obtain data. The case studies included two hospital-based radiology departments in Malaysia. The outcomes of the research suggest that the implementation of medical imaging in community hospitals is shaped by the external context particularly the role played by the Ministry of Health. Furthermore, influences from both the internal and external contexts have a substantial impact on the process of implementing medical imaging and the extent of the benefits that the organization can gain. In the context of roles and social relationships, the findings revealed that the routine use of medical imaging has substantially affected radiographers’ roles, and the social relationships between non clinical personnel and clinicians. This study found no change in the relationship between radiographers and radiologists. Finally, the approaches to implementation taken in the hospitals studied were found to influence those taken by other hospitals. Overall, this study makes three important contributions. Firstly, it extends Barley’s (1986, 1990) research by explicitly demonstrating that the organization’s internal and external contexts together shape the implementation and use of technology, that the processes of implementing and using technology impact upon roles, relationships and networks and that a role-based approach alone is inadequate to examine the outcomes of deploying an advanced technology. Secondly, this study contends that scalability of technology in the context of developing countries is not necessarily linear. Finally, this study offers practical contributions that can benefit healthcare organizations in Malaysia

Applications of Automated Identification Technology in EHR/EMR

Although both the electronic health record (EHR) and the electronic medical record (EMR) store an individuals computerized health information and the terminologies are often used interchangeably, there are some differences between them. Three primary approaches in Automated Identification Technology (AIT) are barcoding, radio frequency identification (RFID), and biometrics. In this paper, technology intelligence, progress, limitations, and challenges of EHR/EMR are introduced. The applications and challenges of barcoding, RFID, and biometrics in EHR/EMR are presented respectively

Nurses’ Learning and Conceptualization of Technology used in Practice

How nurses conceptualize and learn about health technology used in practice was examined in this qualitative, interpretive-descriptive study. Traditionally, conceptualizations of technology used in the nursing profession have been viewed from either socially- or technically- centric perspectives that have clouded the real nature of nurse-technology interactions. For instance, current perspectives examining nurses’ use of technology typically ignore or minimize socio-technical considerations impacting technology acceptance and adoption by nurses. A research approach that embraced the mingling of social and material (sociomaterial) actors was used to address the following research questions: (a) How do nurses conceptualize health technology used in practice?, and, (b) How do nurses learn about health technology used in practice? The theoretical lens of Actor-Network Theory (ANT) provided the overall perspective and guided elements of data collection and analysis. ANT is aligned to a relational ontology, whereby both human and non-human participants (or actors) are viewed in symmetry (or as equals) during data analysis. Privilege during the analysis was, therefore, not automatically prescribed to either the human or non-human actors. Interviews, documents, and direct observation of nurses constituted the majority of the data collected for this study. Using an iterative data analysis process, themes were generated related to nurses’ conceptualization of and learning about technology used in practice. Technology was conceptualized by nurses to possess variation in naming, roles, and also engendered notions of action or praxis. Learning technology by nurses possessed elements resembling both processes and products. From these learning processes and products, salient strategies (e.g., indispensability, semblance, habituation) were developed by nurses in order to negotiate and use various health technologies for practice. Ultimately, learning of health technology by nurses appeared to actively influence, modify, and shape the role of health technology, and its subsequent use by human actors. Therefore, how nurses learn about technology should be considered during the planning, development, and evaluation of future technologies. End-users, like nurses, will rarely use a health technology to its fullest capability unless learning is congruent with the environmental context surrounding the technological actor. In light of these findings, recommendations for nursing education and professional practice related to the role and interpretation of health technology used by nurses in 2013 is also discussed, along with implications for future research

My Virtual Colleague: A State-of-the-Art Analysis of Conversational Agents for the Workplace

Conversational interfaces at the workplace are not a new idea, but it is only the recent technological advancements that turned what was once a vision into near-future reality. Improved reliability and accuracy enable conversational systems to be used in higher stake environments, such as the workplace. In this work, we perform a literature review on concepts proposed to incorporate Conversational Agents (CA) into the workplace. We found 29 workplace CAs designed for workers that contribute to eight different application domains. Based on the studies of these CAs, we compiled a list of aspects to be considered when designing such CAs and identified starting points for further research

Conversational Agents in Health Care: Expert Interviews to Inform the Definition, Classification, and Conceptual Framework

Background Conversational agents (CAs), or chatbots, are computer programs that simulate conversations with humans. The use of CAs in health care settings is recent and rapidly increasing, which often translates to poor reporting of the CA development and evaluation processes and unreliable research findings. We developed and published a conceptual framework, designing, developing, evaluating, and implementing a smartphone-delivered, rule-based conversational agent (DISCOVER), consisting of 3 iterative stages of CA design, development, and evaluation and implementation, complemented by 2 cross-cutting themes (user-centered design and data privacy and security). Objective This study aims to perform in-depth, semistructured interviews with multidisciplinary experts in health care CAs to share their views on the definition and classification of health care CAs and evaluate and validate the DISCOVER conceptual framework. Methods We conducted one-on-one semistructured interviews via Zoom (Zoom Video Communications) with 12 multidisciplinary CA experts using an interview guide based on our framework. The interviews were audio recorded, transcribed by the research team, and analyzed using thematic analysis. Results Following participants’ input, we defined CAs as digital interfaces that use natural language to engage in a synchronous dialogue using ≥1 communication modality, such as text, voice, images, or video. CAs were classified by 13 categories: response generation method, input and output modalities, CA purpose, deployment platform, CA development modality, appearance, length of interaction, type of CA-user interaction, dialogue initiation, communication style, CA personality, human support, and type of health care intervention. Experts considered that the conceptual framework could be adapted for artificial intelligence–based CAs. However, despite recent advances in artificial intelligence, including large language models, the technology is not able to ensure safety and reliability in health care settings. Finally, aligned with participants’ feedback, we present an updated iteration of the conceptual framework for health care conversational agents (CHAT) with key considerations for CA design, development, and evaluation and implementation, complemented by 3 cross-cutting themes: ethics, user involvement, and data privacy and security. Conclusions We present an expanded, validated CHAT and aim at guiding researchers from a variety of backgrounds and with different levels of expertise in the design, development, and evaluation and implementation of rule-based CAs in health care settings

Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol

Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals\u27 health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations

Multilingual Chatbots to Collect Patient-Reported Outcomes

With spoken language interfaces, chatbots, and enablers, the conversational intelligence became an emerging field of research in man-machine interfaces in several target domains. In this paper, we introduce the multilingual conversational chatbot platform that integrates Open Health Connect platform and mHealth application together with multimodal services in order to deliver advanced 3D embodied conversational agents. The platform enables novel human-machine interaction with the cancer survivors in six different languages. The platform also integrates patients’ reported information as patients gather health data into digital clinical records. Further, the conversational agents have the potential to play a significant role in healthcare, from assistants during clinical consultations, to supporting positive behavior changes, or as assistants in living environments helping with daily tasks and activities

The Medical Authority of AI: A Study of AI-enabled Consumer-facing Health Technology

Recently, consumer-facing health technologies such as Artificial Intelligence (AI)-based symptom checkers (AISCs) have sprung up in everyday healthcare practice. AISCs solicit symptom information from users and provide medical suggestions and possible diagnoses, a responsibility that people usually entrust with real-person authorities such as physicians and expert patients. Thus, the advent of AISCs begs a question of whether and how they transform the notion of medical authority in everyday healthcare practice. To answer this question, we conducted an interview study with thirty AISC users. We found that users assess the medical authority of AISCs using various factors including automated decisions and interaction design patterns of AISC apps, associations with established medical authorities like hospitals, and comparisons with other health technologies. We reveal how AISCs are used in healthcare delivery, discuss how AI transforms conventional understandings of medical authority, and derive implications for designing AI-enabled health technology