Towards a Personal Health Management Assistant

Ferguson, George; Quinn, Jill R.; Horwitz, Carol D.; Swift, Mary; Allen, James F.; Galescu, Lucian

doi:10.1016/j.jbi.2010.05.014

Cited by 37 publications

(42 citation statements)

References 24 publications

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“…Four studies were conducted in the United States [ 50 - 53 ], 2 in Spain [ 54 , 55 ], and 1 each in Australia [ 56 ], Canada [ 57 ], United Kingdom [ 58 ], and Korea [ 59 ]. Most studies were quasi-experimental and involved users testing and evaluating the conversational agents [ 50 , 51 , 54 , 56 - 59 ]. Two studies were randomized controlled trials (RCTs) [ 52 , 53 ], and 1 was a proof-of-concept study [ 55 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Schachner¹,

Keller²,

Wangenheim³

2020

J Med Internet Res

158

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Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Schachner¹,

Keller²,

Wangenheim³

2020

J Med Internet Res

158

View full text Add to dashboard Cite

show abstract

“…Information chaos has been defined in the context of healthcare delivery as information overload, information underload, information scatter, information conflict, and erroneous information [43]. Merely storing and presenting stored information is inadequate to support CHIM [44]. More is needed in terms of summary visualization, sorted or filtered information retrieval, and situationappropriate, just-in-time decision support.…”

Section: Caregiver Health Information Management (Chim) Implicationsmentioning

confidence: 99%

Health information management practices in informal caregiving: An artifacts analysis and implications for IT design

Holden

Karanam

Cavalcanti

et al. 2018

International Journal of Medical Informatics

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Introduction: Unpaid informal caregivers of adult care recipients, including persons with dementia, experience multiple unmet information needs and information management challenges. Objectives: To understand the current personal health information management (PHIM) practices in informal caregiving for adults with and without dementia. Methods: Semi-structured interviews were performed with ten informal caregivers-half of whom were caring for persons with dementia-and four formal caregivers at an adult day service. Interviews centered on a paper-based tool distributed by the day service, the CARE Kit, permitting an artifacts analysis of the tools used by participants for PHIM. Qualitative thematic analysis was applied to interview data. Results: Caregivers' PHIM practices aimed to support daily care management and decisionmaking on behalf of care recipients, through: 1) information acquisition and integration across multiple sources and records; 2) information maintenance, updating, and use over time; and 3) information sharing and communication with healthcare professionals and other family caregivers. Participants reported advantages and challenges of their PHIM practices and tools, including fitting PHIM into their daily lives, managing PHIM-related cognitive workload, the functionality of PHIM tools, and the dynamic, longitudinal nature of PHIM. Conclusion: The study produced a number of implications for caregiver health information management information technology (CHIM IT), based on findings about the nature of caregivers' practices for managing information for adult care recipients. We present CHIM IT requirements related to privacy and security, customization and flexibility, ease of use, credibility and sensitivity, situation awareness, information integration, delegation and shared use, updating and maintenance, archiving and versioning, communication, agency and information access, and validation.

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“…For example, Chester [ 14 ] is a medication advisor that uses unconstrained speech input but does not appear to have progressed beyond the prototype stage. Project Health Design developed a prototype speech-based counseling system for congestive heart failure self-care management but was not evaluated [ 15 ]. MyCoach is a voice-driven exercise advisor for overweight cancer survivors developed using the Amazon Alexa conversational assistant framework [ 16 ] and provides a range of functions including advice and coaching.…”

Section: Introductionmentioning

confidence: 99%

Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant

Bickmore¹,

Trinh²,

Ólafsson³

et al. 2018

J Med Internet Res

211

145

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BackgroundConversational assistants, such as Siri, Alexa, and Google Assistant, are ubiquitous and are beginning to be used as portals for medical services. However, the potential safety issues of using conversational assistants for medical information by patients and consumers are not understood.ObjectiveTo determine the prevalence and nature of the harm that could result from patients or consumers using conversational assistants for medical information.MethodsParticipants were given medical problems to pose to Siri, Alexa, or Google Assistant, and asked to determine an action to take based on information from the system. Assignment of tasks and systems were randomized across participants, and participants queried the conversational assistants in their own words, making as many attempts as needed until they either reported an action to take or gave up. Participant-reported actions for each medical task were rated for patient harm using an Agency for Healthcare Research and Quality harm scale.ResultsFifty-four subjects completed the study with a mean age of 42 years (SD 18). Twenty-nine (54%) were female, 31 (57%) Caucasian, and 26 (50%) were college educated. Only 8 (15%) reported using a conversational assistant regularly, while 22 (41%) had never used one, and 24 (44%) had tried one “a few times.“ Forty-four (82%) used computers regularly. Subjects were only able to complete 168 (43%) of their 394 tasks. Of these, 49 (29%) reported actions that could have resulted in some degree of patient harm, including 27 (16%) that could have resulted in death.ConclusionsReliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Patients should be cautioned to not use these technologies for answers to medical questions they intend to act on without further consultation from a health care provider.

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Towards a Personal Health Management Assistant

Cited by 37 publications

References 24 publications

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Health information management practices in informal caregiving: An artifacts analysis and implications for IT design

Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant

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