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TitleTrust Me, I'm a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test.
AuthorsPowell, J
JournalJournal of medical Internet research
Publication Date28 Oct 2019
Date Added to PubMed30 Oct 2019
AbstractOver the next decade, one issue which will dominate sociotechnical studies in health informatics is the extent to which the promise of artificial intelligence in health care will be realized, along with the social and ethical issues which accompany it. A useful thought experiment is the application of the Turing test to user-facing artificial intelligence systems in health care (such as chatbots or conversational agents). In this paper I argue that many medical decisions require value judgements and the doctor-patient relationship requires empathy and understanding to arrive at a shared decision, often handling large areas of uncertainty and balancing competing risks. Arguably, medicine requires wisdom more than intelligence, artificial or otherwise. Artificial intelligence therefore needs to supplement rather than replace medical professionals, and identifying the complementary positioning of artificial intelligence in medical consultation is a key challenge for the future. In health care, artificial intelligence needs to pass the implementation game, not the imitation game.
TitlePreliminary Evaluation of a mHealth Coaching Conversational Artificial Intelligence for the Self-Care Management of People with Sickle-Cell Disease.
AuthorsIssom, DZ; Rochat, J; Hartvigsen, G; Lovis, C
JournalStudies in health technology and informatics
Publication Date16 Jun 2020
Date Added to PubMed24 Jun 2020
AbstractAdherence to the complex set of recommended self-care practices among people with Sickle-Cell Disease (SCD) positively impacts health outcomes. However, few patients possess the required skills (i.e. disease-specific knowledge, adequate levels of self-efficacy). Consequently, adherence rates remain low and only 1% of patients are empowered enough to master the self-care practices. Health coaching and therapeutic patient education have emerged as new approaches to enhance patients' self-management and support health behavior changes. This preliminary feasibility study examined patients' perceived usefulness of the information provided by a chatbot we developed following patient-important requirements collected during our preliminary studies. Participants tested the chatbot and completed a post-test survey. A total of 19 patients were enrolled and 2 withdrew. 15 respondents (15/17, 88%) gave a score of at least 3/4 to the question "The chatbot contains all the information I need". Results suggest that mHealth coaching apps could be used to promote the knowledge acquisition of recommended health behaviors related to the prevention of SCD main symptoms.
TitleAssessing the feasibility of a chatbot after ureteroscopy.
AuthorsGoldenthal, SB; Portney, D; Steppe, E; Ghani, K; Ellimoottil, C
Publication Date1 Dec 2019
Date Added to PubMed26 Apr 2019
AbstractPatients experience common symptoms and/or complications after undergoing ureteroscopy, which is a common procedure used to treat kidney stones. Current methods for patient education regarding these complications include counseling and discharge materials. This study aimed to assess chatbot usage and their ability to deliver information to patient post-ureteroscopy. Patients who underwent ureteroscopy at the University of Michigan were given instructions to activate the chatbot. Within one to four weeks of their surgery, semi-structured interviews were conducted regarding post-surgical recovery and chatbot usage. Twenty patients were interviewed, seven of whom activated the chatbot. Frequent reasons for not activating the chatbot included misplacing instructions for chatbot use (n=6), relying on follow-up with clinic or discharge materials (n=4), inability to activate chatbot (n=2), and inability to text (n=1). Perceived benefits included alleviation of concerns surrounding common symptoms and quick access to information for non-emergent issues. These results suggest that chatbots are a convenient method to address common concerns post-ureteroscopy. However, better integration in the flow of care delivery and improved usability are needed to increase patient engagement.
TitleClinical Integration of Digital Solutions in Health Care: An Overview of the Current Landscape of Digital Technologies in Cancer Care.
AuthorsGarg, S; Williams, NL; Ip, A; Dicker, AP
JournalJCO clinical cancer informatics
Publication Date1 Dec 2018
Date Added to PubMed18 Jan 2019
AbstractDigital health constitutes a merger of both software and hardware technology with health care delivery and management, and encompasses a number of domains, from wearable devices to artificial intelligence, each associated with widely disparate interaction and data collection models. In this review, we focus on the landscape of the current integration of digital health technology in cancer care by subdividing digital health technologies into the following sections: connected devices, digital patient information collection, telehealth, and digital assistants. In these sections, we give an overview of the potential clinical impact of such technologies as they pertain to key domains, including patient education, patient outcomes, quality of life, and health care value. We performed a search of PubMed ( ) and for numerous terms related to digital health technologies, including digital health, connected devices, smart devices, wearables, activity trackers, connected sensors, remote monitoring, electronic surveys, electronic patient-reported outcomes, telehealth, telemedicine, artificial intelligence, chatbot, and digital assistants. The terms health care and cancer were appended to the previously mentioned terms to filter results for cancer-specific applications. From these results, studies were included that exemplified use of the various domains of digital health technologies in oncologic care. Digital health encompasses the integration of a vast array of technologies with health care, each associated with varied methods of data collection and information flow. Integration of these technologies into clinical practice has seen applications throughout the spectrum of care, including cancer screening, on-treatment patient management, acute post-treatment follow-up, and survivorship. Implementation of these systems may serve to reduce costs and workflow inefficiencies, as well as to improve overall health care value, patient outcomes, and quality of life.
TitleDoes the addition of a supportive chatbot promote user engagement with a smoking cessation app? An experimental study.
AuthorsPerski, O; Crane, D; Beard, E; Brown, J
JournalDigital health
Publication Date1 Dec
Date Added to PubMed18 Oct 2019
AbstractThe objective of this study was to assess whether a version of the Smoke Free app with a supportive chatbot powered by artificial intelligence (versus a version without the chatbot) led to increased engagement and short-term quit success. Daily or non-daily smokers aged ≥18 years who purchased the 'pro' version of the app and set a quit date were randomly assigned (unequal allocation) to receive the app with or without the chatbot. The outcomes were engagement (i.e. total number of logins over the study period) and self-reported abstinence at a one-month follow-up. Unadjusted and adjusted negative binomial and logistic regression models were fitted to estimate incidence rate ratios (IRRs) and odds ratios (ORs) for the associations of interest. A total of 57,214 smokers were included (intervention: 9.3% (5339); control: 90.7% (51,875). The app with the chatbot compared with the standard version led to a 101% increase in engagement (IRRadj = 2.01, 95% confidence interval (CI) = 1.92-2.11, p < .001). The one-month follow-up rate was 10.6% (intervention: 19.9% (1,061/5,339); control: 9.7% (5,050/51,875). Smokers allocated to the intervention had greater odds of quit success (missing equals smoking: 844/5,339 vs. 3,704/51,875, ORadj = 2.38, 95% CI = 2.19-2.58, p < .001; follow-up only: 844/1,061 vs. 3,704/5,050, ORadj = 1.36, 95% CI = 1.16-1.61, p < .001). The addition of a supportive chatbot to a popular smoking cessation app more than doubled user engagement. In view of very low follow-up rates, there is low quality evidence that the addition also increased self-reported smoking cessation.
TitleUse of the Chatbot "Vivibot" to Deliver Positive Psychology Skills and Promote Well-Being Among Young People After Cancer Treatment: Randomized Controlled Feasibility Trial.
AuthorsGreer, S; Ramo, D; Chang, YJ; Fu, M; Moskowitz, J; Haritatos, J
JournalJMIR mHealth and uHealth
Publication Date31 Oct 2019
Date Added to PubMed2 Nov 2019
AbstractPositive psychology interventions show promise for reducing psychosocial distress associated with health adversity and have the potential to be widely disseminated to young adults through technology. This pilot randomized controlled trial examined the feasibility of delivering positive psychology skills via the Vivibot chatbot and its effects on key psychosocial well-being outcomes in young adults treated for cancer. Young adults (age 18-29 years) were recruited within 5 years of completing active cancer treatment by using the Vivibot chatbot on Facebook messenger. Participants were randomized to either immediate access to Vivibot content (experimental group) or access to only daily emotion ratings and access to full chatbot content after 4 weeks (control). Created using a human-centered design process with young adults treated for cancer, Vivibot content includes 4 weeks of positive psychology skills, daily emotion ratings, video, and other material produced by survivors, and periodic feedback check-ins. All participants were assessed for psychosocial well-being via online surveys at baseline and weeks 2, 4, and 8. Analyses examined chatbot engagement and open-ended feedback on likability and perceived helpfulness and compared experimental and control groups with regard to anxiety and depression symptoms and positive and negative emotion changes between baseline and 4 weeks. To verify the main effects, follow-up analyses compared changes in the main outcomes between 4 and 8 weeks in the control group once participants had access to all chatbot content. Data from 45 young adults (36 women; mean age: 25 [SD 2.9]; experimental group: n=25; control group: n=20) were analyzed. Participants in the experimental group spent an average of 74 minutes across an average of 12 active sessions chatting with Vivibot and rated their experience as helpful (mean 2.0/3, SD 0.72) and would recommend it to a friend (mean 6.9/10; SD 2.6). Open-ended feedback noted its nonjudgmental nature as a particular benefit of the chatbot. After 4 weeks, participants in the experimental group reported an average reduction in anxiety of 2.58 standardized t-score units, while the control group reported an increase in anxiety of 0.7 units. A mixed-effects models revealed a trend-level (P=.09) interaction between group and time, with an effect size of 0.41. Those in the experimental group also experienced greater reductions in anxiety when they engaged in more sessions (z=-1.9, P=.06). There were no significant (or trend level) effects by group on changes in depression, positive emotion, or negative emotion. The chatbot format provides a useful and acceptable way of delivering positive psychology skills to young adults who have undergone cancer treatment and supports anxiety reduction. Further analysis with a larger sample size is required to confirm this pattern.
TitleA Smartphone-Based Health Care Chatbot to Promote Self-Management of Chronic Pain (SELMA): Pilot Randomized Controlled Trial.
AuthorsHauser-Ulrich, S; Künzli, H; Meier-Peterhans, D; Kowatsch, T
JournalJMIR mHealth and uHealth
Publication Date3 Apr 2020
Date Added to PubMed4 Apr 2020
AbstractOngoing pain is one of the most common diseases and has major physical, psychological, social, and economic impacts. A mobile health intervention utilizing a fully automated text-based health care chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between a participant and the TBHC. The objectives of this study are twofold: (1) to describe the design and implementation to promote the chatbot painSELfMAnagement (SELMA), a 2-month smartphone-based cognitive behavior therapy (CBT) TBHC intervention for pain self-management in patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior, pain duration, working alliance, acceptance, and adherence were evaluated. Participants were recruited online and in collaboration with pain experts, and were randomized to interact with SELMA for 8 weeks either every day or every other day concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity, and the bond scale of working alliance were measured at baseline and postintervention. Intention to change behavior and pain duration were measured at baseline only, and acceptance postintervention was assessed via self-reporting instruments. Adherence was assessed via usage data. From May 2018 to August 2018, 311 adults downloaded the SELMA app, 102 of whom consented to participate and met the inclusion criteria. The average age of the women (88/102, 86.4%) and men (14/102, 13.6%) participating was 43.7 (SD 12.7) years. Baseline group comparison did not differ with respect to any demographic or clinical variable. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the control group postintervention. The intention to change behavior was positively related to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the TBHC SELMA was comparable to that obtained in guided internet therapies with human coaches. Participants enjoyed using the app, perceiving it as useful and easy to use. Participants of the intervention group replied with an average answer ratio of 0.71 (SD 0.20) to 200 (SD 58.45) conversations initiated by SELMA. Participants' comments revealed an appreciation of the empathic and responsible interaction with the TBHC SELMA. A main criticism was that there was no option to enter free text for the patients' own comments. SELMA is feasible, as revealed mainly by positive feedback and valuable suggestions for future revisions. For example, the participants' intention to change behavior or a more homogenous sample (eg, with a specific type of chronic pain) should be considered in further tailoring of SELMA. German Clinical Trials Register DRKS00017147;, Swiss National Clinical Trial Portal: SNCTP000002712;
TitleCan a Chatbot Increase the Motivation to Provide Personal Health Information?
AuthorsDenecke, K; May, R; Pöpel, A; Lutz Hochreutener, S
JournalStudies in health technology and informatics
Publication Date4 Sep 2020
Date Added to PubMed23 Oct 2020
AbstractIn healthcare settings, questionnaires are used to collect information from a patient. A standard method for this are paper-based questionnaires, but they are often complex to understand or long and frustrating to fill. To increase motivation, we developed a chatbot-based system Ana that asks questions that are normally asked using paper forms or in face-to-face encounters. Ana has been developed for the specific use case of collecting the music biography in the context of music therapy. In this paper, we compare user motivation, relevance of answers and time needed to answer the questions depending on the data entry method (i.e. app Ana versus paper-based questionnaire). A randomised trial was performed with 26 students of music therapy. The results show that the chatbot is more motivating and answers are given faster than on paper. No differences in answer relevance could be determined between the two means. We conclude that a chatbot could become an additional data entry method for collecting personal health information.
TitleAccuracy of a Chatbot (Ada) in the Diagnosis of Mental Disorders: Comparative Case Study With Lay and Expert Users.
AuthorsJungmann, SM; Klan, T; Kuhn, S; Jungmann, F
JournalJMIR formative research
Publication Date29 Oct 2019
Date Added to PubMed31 Oct 2019
AbstractHealth apps for the screening and diagnosis of mental disorders have emerged in recent years on various levels (eg, patients, practitioners, and public health system). However, the diagnostic quality of these apps has not been (sufficiently) tested so far. The objective of this pilot study was to investigate the diagnostic quality of a health app for a broad spectrum of mental disorders and its dependency on expert knowledge. Two psychotherapists, two psychology students, and two laypersons each read 20 case vignettes with a broad spectrum of mental disorders. They used a health app (Ada-Your Health Guide) to get a diagnosis by entering the symptoms. Interrater reliabilities were computed between the diagnoses of the case vignettes and the results of the app for each user group. Overall, there was a moderate diagnostic agreement (kappa=0.64) between the results of the app and the case vignettes for mental disorders in adulthood and a low diagnostic agreement (kappa=0.40) for mental disorders in childhood and adolescence. When psychotherapists applied the app, there was a good diagnostic agreement (kappa=0.78) regarding mental disorders in adulthood. The diagnostic agreement was moderate (kappa=0.55/0.60) for students and laypersons. For mental disorders in childhood and adolescence, a moderate diagnostic quality was found when psychotherapists (kappa=0.53) and students (kappa=0.41) used the app, whereas the quality was low for laypersons (kappa=0.29). On average, the app required 34 questions to be answered and 7 min to complete. The health app investigated here can represent an efficient diagnostic screening or help function for mental disorders in adulthood and has the potential to support especially diagnosticians in their work in various ways. The results of this pilot study provide a first indication that the diagnostic accuracy is user dependent and improvements in the app are needed especially for mental disorders in childhood and adolescence.
TitleAn Exploration Into the Use of a Chatbot for Patients With Inflammatory Bowel Diseases: Retrospective Cohort Study.
AuthorsZand, A; Sharma, A; Stokes, Z; Reynolds, C; Montilla, A; Sauk, J; Hommes, D
JournalJournal of medical Internet research
Publication Date26 May 2020
Date Added to PubMed27 May 2020
AbstractThe emergence of chatbots in health care is fast approaching. Data on the feasibility of chatbots for chronic disease management are scarce. This study aimed to explore the feasibility of utilizing natural language processing (NLP) for the categorization of electronic dialog data of patients with inflammatory bowel diseases (IBD) for use in the development of a chatbot. Electronic dialog data collected between 2013 and 2018 from a care management platform (UCLA eIBD) at a tertiary referral center for IBD at the University of California, Los Angeles, were used. Part of the data was manually reviewed, and an algorithm for categorization was created. The algorithm categorized all relevant dialogs into a set number of categories using NLP. In addition, 3 independent physicians evaluated the appropriateness of the categorization. A total of 16,453 lines of dialog were collected and analyzed. We categorized 8324 messages from 424 patients into seven categories. As there was an overlap in these categories, their frequencies were measured independently as symptoms (2033/6193, 32.83%), medications (2397/6193, 38.70%), appointments (1518/6193, 24.51%), laboratory investigations (2106/6193, 34.01%), finance or insurance (447/6193, 7.22%), communications (2161/6193, 34.89%), procedures (617/6193, 9.96%), and miscellaneous (624/6193, 10.08%). Furthermore, in 95.0% (285/300) of cases, there were minor or no differences in categorization between the algorithm and the three independent physicians. With increased adaptation of electronic health technologies, chatbots could have great potential in interacting with patients, collecting data, and increasing efficiency. Our categorization showcases the feasibility of using NLP in large amounts of electronic dialog for the development of a chatbot algorithm. Chatbots could allow for the monitoring of patients beyond consultations and potentially empower and educate patients and improve clinical outcomes.
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