A Chatbot Versus Physicians to Provide Information for Patients With Breast Cancer: Blind, Randomized Controlled Noninferiority Trial.

BACKGROUND: The data regarding the use of conversational agents in oncology are scarce. OBJECTIVE: The aim of this study was to verify whether an artificial conversational agent was able to provide answers to patients with breast cancer with a level of satisfaction similar to the answers given by a group of physicians. METHODS: This study is a blind, noninferiority randomized controlled trial that compared the information given by the chatbot, Vik, with that given by a multidisciplinary group of physicians to patients with breast cancer. Patients were women with breast cancer in treatment or in remission. The European Organisation for Research and Treatment of Cancer Quality of Life Group information questionnaire (EORTC QLQ-INFO25) was adapted and used to compare the quality of the information provided to patients by the physician or the chatbot. The primary outcome was to show that the answers given by the Vik chatbot to common questions asked by patients with breast cancer about their therapy management are at least as satisfying as answers given by a multidisciplinary medical committee by comparing the success rate in each group (defined by a score above 3). The secondary objective was to compare the average scores obtained by the chatbot and physicians for each INFO25 item. RESULTS: A total of 142 patients were included and randomized into two groups of 71. They were all female with a mean age of 42 years (SD 19). The success rates (as defined by a score >3) was 69% (49/71) in the chatbot group versus 64% (46/71) in the physicians group. The binomial test showed the noninferiority (P<.001) of the chatbot's answers. CONCLUSIONS: This is the first study that assessed an artificial conversational agent used to inform patients with cancer. The EORTC INFO25 scores from the chatbot were found to be noninferior to the scores of the physicians. Artificial conversational agents may save patients with minor health concerns from a visit to the doctor. This could allow clinicians to spend more time to treat patients who need a consultation the most. TRIAL REGISTRATION: Clinicaltrials.gov NCT03556813, https://tinyurl.com/rgtlehq.

Using a Chatbot to Study Medication Overuse Among Patients Suffering From Headaches.

According to the World Health Organization, half the adult population around the world suffers from headaches. Even though this condition remains in most cases innocuous, it can have a major impact on the patient's quality of life but also on public health expenditure. Moreover, most patients manage their headaches on their own, without consulting a doctor. Therefore, self-medication can eventually lead to drug overuse, and consequently the emergence of a secondary disease called medication-overuse headache (MOH). The detection and follow-up of these unconventional patients represent a major challenge. Some of the latest technology advancements seem to be tailored and fitting for this context. The goal of this study is to investigate medication overuse in French patients suffering from headaches using the chatbot Vik Migraine. Data collection and analysis were assembled from answers to a questionnaire of 28 questions divided into three parts: socio-demographic profile, drug consumption, and medical follow-up. The study showed that medication overuse was often linked to increased headache frequency. Prescription drugs like triptans and opioids, were the most overused drugs among the cohort. This suggests that healthcare professionals could play a critical role in targeting these drugs in prevention of overuse.

Assessing the performances of a chatbot to collect real-life data of patients suffering from primary headache disorders.

Background: There are many scales for screening the impact of a disease. These scales are generally used to diagnose or assess the type and severity of a disease and are carried out by doctors. The chatbot helps patients suffering from primary headache disorders through personalized text messages. It could be used to collect patient-reported outcomes. Objective: The aims of this study were (1) to study whether the collection and analysis of remote scores, without prior medical intervention, are possible by a chatbot, (2) to perform suggested diagnosis and define the type of headaches, and (3) to assess the patient satisfaction and engagement with the chatbot. Method: Voluntary users of the chatbot were recruited online. They had to be over 18 and have a personal history of headaches. A questionnaire was presented (1) by text messages to the participants to evaluate migraines (2) based on the criteria of the International Headache Society. Then, the Likert scale (3) was used to assess overall satisfaction with the use of the chatbot. Results: We included 610 participants with primary headache disorders. A total of 89.94% (572/610) participants had fully completed the questionnaire (eight items), 4.72% (30/610) had partially completed it, and 5.41% (33) had refused to complete it. Statistical analysis was performed on 86.01% (547/610) of participants. Auto diagnostic showed that 14.26% (78/547) participants had a tension headache, and 85.74% (469/547) had a probable migraine. In this population, 15.78% (74/469) suffered from migraine without probable aura, and 84.22% (395/469) had migraine without aura. The patient's age had a significant incidence regarding the auto diagnosis (P = .008<.05). The evaluation of overall satisfaction shows that a total of 93.9% (599/610) of users were satisfied or very satisfied regarding the timeliness of responses the chatbot provides. Conclusion: The study confirmed that it was possible to obtain such a collection remotely, and quickly (average time of 3.24 min) with a high success rate (89.67% (547/610) participants who had fully completed the IHS questionnaire). Users were strongly engaged through chatbot: out of the total number of participants, we observed a very low number of uncompleted questionnaires (6.23% (38/610)). Conversational agents can be used to remotely collect data on the nature of the symptoms of patients suffering from primary headache disorders. These results are promising regarding patient engagement and trust in the chatbot.

Psychological distress during the COVID-19 pandemic in France: a national assessment of at-risk populations.

BACKGROUND: Lockdowns were implemented to limit the spread of COVID-19. Peritraumatic distress (PD) and post-traumatic stress disorder have been reported after traumatic events, but the specific effect of the pandemic is not well known. AIM: The aim of this study was to assess PD in France, a country where COVID-19 had such a dramatic impact that it required a country-wide lockdown. METHODS: We recruited patients in four groups of chatbot users followed for breast cancer, asthma, depression and migraine. We used the Psychological Distress Inventory (PDI), a validated scale to measure PD during traumatic events, and correlated PD risk with patients' characteristics in order to better identify the ones who were the most at risk. RESULTS: The study included 1771 participants. 91.25% (n=1616) were female with a mean age of 32.8 (13.71) years and 7.96% (n=141) were male with a mean age of 28.0 (8.14) years. In total, 38.06% (n=674) of the respondents had psychological distress (PDI ≥14). An analysis of variance showed that unemployment and depression were significantly associated with a higher PDI score. Patients using their smartphones or computers for more than 1 hour a day also had a higher PDI score (p=0.026). CONCLUSION: Prevalence of PD in at-risk patients is high. These patients are also at an increased risk of developing post-traumatic stress disorder. Specific steps should be implemented to monitor and prevent PD through dedicated mental health policies if we want to limit the public health impact of COVID-19 in time. TRIAL REGISTRATION NUMBER: NCT04337047.

Healthcare ex Machina: Are conversational agents ready for prime time in oncology?

Chatbots, also known as conversational agents or digital assistants, are artificial intelligence-driven software programs designed to interact with people in a conversational manner. They are often used for user-friendly customer-service triaging. In healthcare, chatbots can create bidirectional information exchange with patients, which could be leveraged for follow-up, screening, treatment adherence or data-collection. They can be deployed over various modalities, such as text-based services (text messaging, mobile applications, chat rooms) on any website or mobile applications, or audio services, such as Siri, Alexa, Cortana or Google Assistant. Potential applications are very promising, particularly in the field of oncology. In this review, we discuss the available publications and applications and the ongoing trials in that setting.

When Chatbots Meet Patients: One-Year Prospective Study of Conversations Between Patients With Breast Cancer and a Chatbot.

BACKGROUND: A chatbot is a software that interacts with users by simulating a human conversation through text or voice via smartphones or computers. It could be a solution to follow up with patients during their disease while saving time for health care providers. OBJECTIVE: The aim of this study was to evaluate one year of conversations between patients with breast cancer and a chatbot. METHODS: Wefight Inc designed a chatbot (Vik) to empower patients with breast cancer and their relatives. Vik responds to the fears and concerns of patients with breast cancer using personalized insights through text messages. We conducted a prospective study by analyzing the users' and patients' data, their usage duration, their interest in the various educational contents proposed, and their level of interactivity. Patients were women with breast cancer or under remission. RESULTS: A total of 4737 patients were included. Results showed that an average of 132,970 messages exchanged per month was observed between patients and the chatbot, Vik. Thus, we calculated the average medication adherence rate over 4 weeks by using a prescription reminder function, and we showed that the more the patients used the chatbot, the more adherent they were. Patients regularly left positive comments and recommended Vik to their friends. The overall satisfaction was 93.95% (900/958). When asked what Vik meant to them and what Vik brought them, 88.00% (943/958) said that Vik provided them with support and helped them track their treatment effectively. CONCLUSIONS: We demonstrated that it is possible to obtain support through a chatbot since Vik improved the medication adherence rate of patients with breast cancer.