A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions

This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details

Productivity of incident management with conversational bots-a review

The use of conversational agents (bots) in information systems managed by company’s increases productivity in the development of activities focused on processes such as customer service, healthcare, and presentation. The present work is a systematic literature review that collects articles from 2019 to 2022 in the databases Scopus, Springer, Willey, Indexes-Csic, Taylor & Francis, Pubmed, and Ebsco Host. PRISMA methodology was used to systematize 47 relevant articles. As a result of the analysis, 2/19 very important benefits were obtained, which are: helping to obtain information and facilitating customer service; as for the types of conversational bots, a total of 9 types were found, of which conversational agents and chatbots with artificial intelligence (AI) are the most common; in the case of processes, 3/5 processes that optimize conversational bots were found, where the most prominent are: teaching process, health processes, and customer service processes. An architecture model for conversational bots in incident management is also proposed

Feasibility of a Text Messaging-Integrated and Chatbot-Interfaced Self-Management Program for Symptom Control in Patients With Gastrointestinal Cancer Undergoing Chemotherapy: Pilot Mixed Methods Study

BACKGROUND: Outpatient chemotherapy often leaves patients to grapple with a range of complex side effects at home. Leveraging tailored evidence-based content to monitor and manage these symptoms remains an untapped potential among patients with gastrointestinal (GI) cancer. OBJECTIVE: This study aims to bridge the gap in outpatient chemotherapy care by integrating a cutting-edge text messaging system with a chatbot interface. This approach seeks to enable real-time monitoring and proactive management of side effects in patients with GI cancer undergoing intravenous chemotherapy. METHODS: Real-Time Chemotherapy-Associated Side Effects Monitoring Supportive System (RT-CAMSS) was developed iteratively, incorporating patient-centered inputs and evidence-based information. It synthesizes chemotherapy knowledge, self-care symptom management skills, emotional support, and healthy lifestyle recommendations. In a single-arm 2-month pilot study, patients with GI cancer undergoing chemotherapy received tailored intervention messages thrice a week and a weekly Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events-based symptom assessment via a chatbot interface. Baseline and postintervention patient surveys and interviews were conducted. RESULTS: Out of 45 eligible patients, 34 were enrolled (76% consent rate). The mean age was 61 (SD 12) years, with 19 (56%) being females and 21 (62%) non-Hispanic White. The most common cancer type was pancreatic (n=18, 53%), followed by colon (n=12, 35%) and stomach (n=4, 12%). In total, 27 (79% retention rate) participants completed the postintervention follow-up. In total, 20 patients texted back at least once to seek additional information, with the keyword chemo or support texted the most. Among those who used the chatbot system checker, fatigue emerged as the most frequently reported symptom (n=15), followed by neuropathy (n=7). Adjusted for multiple comparisons, patients engaging with the platform exhibited significantly improved Patient Activation Measure (3.70, 95% CI -6.919 to -0.499; P=.02). Postintervention interviews and satisfaction surveys revealed that participants found the intervention was user-friendly and were provided with valuable information. CONCLUSIONS: Capitalizing on mobile technology communication holds tremendous scalability for enhancing health care services. This study presents initial evidence of the engagement and acceptability of RT-CAMSS, warranting further evaluation in a controlled clinical trial setting

Disruptive innovation in the healthcare sector : the advent of AI chatbots

Over the last several decades, the healthcare sector has faced many challenges. These include a shortage of doctors, especially in rural areas, high clinical costs, and an increasing number of diseases needing to be treated. This thesis focuses on the potential and the limitations of an innovative way to solve problems in healthcare – use of AI chatbots. We highlight the user’s perspective concerning AI healthcare chatbot technology. Based on qualitative and quantitative research, we conclude that this novel technology offers new opportunities for diagnostics, enables work to be carried out more efficiently, and gives the patient the power to “self-diagnose”. AI chatbots have not yet reached their full potential due to legal restrictions, insufficient data, and the lack of capacity to integrate them into different systems. Even though the number of AI chatbot users is increasing, people trust chatbots less than doctors. To enhance user engagement and create a higher level of trust, credible entities such as doctors and the government could recommend the use of AI chatbots. The general acceptance of chatbots has to be analyzed per country since it is explained by socio-economic factors (education, age, income), personality-related factors (attitude to new things, curiosity) and communication behavior factors.Nas últimas décadas, o setor da saúde enfrentou muitos desafios. Nestes podem destacar-se a escassez de médicos, especialmente nas zonas rurais, custos de tratamento elevados e um número crescente de doenças a precisarem de ser tratadas. Esta tese foca-se no potencial e nas limitações de uma forma revolucionária de resolver problemas na área da saúde – o uso de chatbots de IA. Destacamos a perspetiva do utilizador em relação à assistência médica através da tecnologia de chatbot de IA. Com base em pesquisas qualitativas e quantitativas, concluímos que esta tecnologia inovadora oferece novas oportunidades para diagnósticos, permite que o trabalho seja realizado com mais eficiência e oferece ao paciente a capacidade de se autodiagnosticar. Os chatbots de IA ainda não atingiram todo o seu potencial devido a restrições legais, dados insuficientes e à falta de capacidade de integrá-los em diferentes sistemas. Ainda que o número de utilizadores de chatbot de IA esteja a aumentar, as pessoas confiam menos nos chatbots do que nos médicos. Para encorajar um maior envolvimento do utilizador e criar um nível mais alto de confiança, entidades credíveis como médicos e o governo podem recomendar o uso de chatbots de IA. A aceitação generalizada dos chatbots deve ser analisada por país, uma vez que é explicada por fatores socioeconómicos (educação, idade, rendimento), fatores relacionados com a personalidade (atitude perante coisas novas, curiosidade) e fatores de comportamento na comunicação

Conversational Agents and Connected Devices to Support Chronic Disease Self-Management

This dissertation focused on designing, developing, and evaluating the usability of a conversational agent for hypertension self-management. The objectives were to: 1) assess patient needs and preferences of a conversational agent; 2) design, develop, and evaluate a conversational agent prototype; and 3) identify physical activity clusters from wearable devices and evaluate the association between physical activity and health status, which could be used to facilitate future contextually aware dialogues as physical activity can be used to improve hypertension control. Leveraging a user-centered design process, patients with hypertension (n=15) participated in semi-structured interviews to elicit needs and perceptions towards using conversational agents to assist with managing blood pressure and medications. Based on these needs, a functional prototype was iteratively designed and developed. Another sample of patients with hypertension (n=10) participated in task-based usability testing to assess the usability and acceptability for assisting with self-management tasks. Cluster analysis of wearable device data from patients (n=430) was conducted to identify physical activity patterns that could inform tailored coaching strategies. We examined the relationship between physical activity clusters and health status using cross-sectional and longitudinal analyses. Usability testing revealed that patients demonstrated curiosity towards interacting with conversational agents for hypertension self-management behaviors for managing medications and refills, communicating with the care team, and maintaining healthy lifestyles. Patients expressed concerns about conversational agents being intrusive and providing too much information. Usability testing showed high rates of task completion and acceptability. Conversational user experience could be improved with additional navigational features of menu and back buttons, contextual error messages, and a health professional persona. Cluster analysis revealed three activity phenotypes of low, moderate, and high physical activity. Patients in the low activity cluster reported significantly worse patient-reported outcomes compared to those with moderate and high physical activity (p<0.05). The majority of patients remained in their original physical activity cluster across 6-month periods. Within this emergent field, this research contributes towards improving the design, usability, and dialogues of self-management conversational agents. This research is an important step towards realizing the potential and implications of conversational agents to support chronic disease self-management and improve health outcomes.Doctor of Philosoph

Investigating resilience patterns based on within-subject changes in sleep and resting heart rate variability

Occupational stress can cause all kinds of health problems. Resilience interventions that help employees deal with and adapt to adverse events can prevent these negative consequences. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee’s own context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. The Wearables and app-based resilience Modelling in employees (WearMe) project aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented, as the basis for the WearMe project. The included concepts are operationalized and measured using sleep tracking (Fitbit Charge 2), heart rate variability measurements (Elite HRV + Polar H7) and Ecological Momentary Assessment (mobile app), administered in the morning (7 questions) and evening (12 questions). The first (ongoing) study within the WearMe project investigates the feasibility of the developed measurement cycle and explores the development of such models in social studies students that are on their first major internship. Analyses will target the development of both within-subject (n=1) models, as well as between-subjects models. The first results will be shared at the Health By Tech 2019 conference in Groningen. If successful, future work will focus on further developing these models and eventually exploring the effectiveness of the envisioned personalized resilience system

Comparing the Effectiveness of Education Versus Digital Cognitive Behavioral Therapy for Adults With Sickle Cell Disease: Protocol for the Cognitive Behavioral Therapy and Real-time Pain Management Intervention for Sickle Cell via Mobile Applications (CaRISMA) Study

BACKGROUND: Patients with sickle cell disease (SCD) experience significant medical and psychological stressors that affect their mental health, well-being, and disease outcomes. Digital cognitive behavioral therapy (CBT) has been used in other patient populations and has demonstrated clinical benefits. Although evidence-based, nonpharmacological interventions for pain management are widely used in other populations, these treatments have not been well studied in SCD. Currently, there are no adequately powered large-scale clinical trials to evaluate the effectiveness and dissemination potential of behavioral pain management for adults with SCD. Furthermore, some important details regarding behavioral therapies in SCD remain unclear—in particular, what works best for whom and when. OBJECTIVE: Our primary goal is to compare the effectiveness of two smartphone–delivered programs for reducing SCD pain symptoms: digital CBT versus pain and SCD education (Education). Our secondary goal is to assess whether baseline depression symptoms moderate the effect of interventions on pain outcomes. We hypothesize that digital CBT will confer greater benefits on pain outcomes and depressive symptoms at 6 months and a greater reduction in health care use (eg, opioid prescriptions or refills or acute care visits) over 12 months. METHODS: The CaRISMA (Cognitive Behavioral Therapy and Real-time Pain Management Intervention for Sickle Cell via Mobile Applications) study is a multisite comparative effectiveness trial funded by the Patient-Centered Outcomes Research Institute. CaRISMA is conducted at six clinical academic sites, in partnership with four community-based organizations. CaRISMA will evaluate the effectiveness of two 12-week health coach–supported digital health programs with a total of 350 participants in two groups: CBT (n=175) and Education (n=175). Participants will complete a series of questionnaires at baseline and at 3, 6, and 12 months. The primary outcome will be the change in pain interference between the study arms. We will also evaluate changes in pain intensity, depressive symptoms, other patient-reported outcomes, and health care use as secondary outcomes. We have 80% power to detect a difference of 0.37 SDs between study arms on 6-month changes in the outcomes with 15% expected attrition at 6 months. An exploratory analysis will examine whether baseline depression symptoms moderate the effect of the intervention on pain interference. RESULTS: This study will be conducted from March 2021 through February 2022, with results expected to be available in February 2023. CONCLUSIONS: Patients with SCD experience significant disease burden, psychosocial stress, and impairment of their quality of life. CaRISMA proposes to leverage digital technology and overcome barriers to the routine use of behavioral treatments for pain and depressive symptoms in the treatment of adults with SCD. The study will provide data on the comparative effectiveness of digital CBT and Education approaches and evaluate the potential for implementing evidence-based behavioral interventions to manage SCD pain. TRIAL REGISTRATION: ClinicalTrials.gov NCT04419168; https://clinicaltrials.gov/ct2/show/NCT04419168. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/2901