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.

[Chapter 2. Internet of Things help to collect Big Data]. / Chapitre 2. Utilisation des Big Data en santé : le cas des objets connectés.

According to the report ?The Internet of Things Market? the number of connected devices will reach 68 billion in 2020. In 2012, the total amount of data was 500 petabytes. So, after the race to increase power computation, now the stake is in the capacity to store all these data in the cloud, to open their access and to analyze these data properly. The use of these data is a major challenge for medical research and public health.

Cross-Sectional and longitudinal associations of objectively-measured physical activity on blood pressure: evaluation in 37 countries.

Background: We examined the cross-sectional and longitudinal associations of objectively-measured physical activity (step counts) and blood pressure (BP) among adults spanning 37 countries. Methods: Across 37 countries, we used data from a pool of 9238 adult owners of Withings' Pulse activity trackers, which measures steps taken each day, and Wireless Blood Pressure Monitor, which measures BP. Analyses were adjusted on age, sex, number of days where the tracker was worn, and number of BP measurements. Data was collected from 2009 to 2013. Results: Subjects had a mean ± standard deviation (SD) age of 51.6 ± 11.3 years and a body mass index (BMI) of 28.7±5.5 kg/m2. A 1-month increase of more than 3000 steps per day was associated with a decrease of systolic BP (SBP) and diastolic BP (DBP) among the obese (1.57mm Hg and 1.29 mm Hg respectively, both P<0.001) and the overweight population (0.79 mm Hg and 0.84 mm Hg respectively, both P≤0.001), but not in the normal weight population (P=0.60 and P=0.36 respectively). Conclusion: One-month increases in daily step counts was associated with a decrease of SBP and DBP in a large obese and overweight free living population.

Mobile applications in oncology: is it possible for patients and healthcare professionals to easily identify relevant tools?

AIM: Mobile applications represent promising tools in management of chronic diseases, both for patients and healthcare professionals, and especially in oncology. Among the large number of mobile health (mhealth) applications available in mobile stores, it could be difficult for users to identify the most relevant ones. This study evaluated the business model and the scientific validation for mobile applications related to oncology. METHODS: A systematic review was performed over the two major marketplaces. Purpose, scientific validation, and source of funding were evaluated according to the description of applications in stores. Results were stratified according to targeted audience (general population/patients/healthcare professionals). RESULTS: Five hundred and thirty-nine applications related to oncology were identified: 46.8% dedicated to healthcare professionals, 31.5% to general population, and 21.7% to patients. A lack of information about healthcare professionals' involvement in the development process was noted since only 36.5% of applications mentioned an obvious scientific validation. Most apps were free (72.2%) and without explicit support by industry (94.2%). CONCLUSIONS: There is a need to enforce independent review of mhealth applications in oncology. The economic model could be questioned and the source of funding should be clarified. Meanwhile, patients and healthcare professionals should remain cautious about applications' contents. Key messages A systematic review was performed to describe the mobile applications related to oncology and it revealed a lack of information on scientific validation and funding. Independent scientific review and the reporting of conflicts of interest should be encouraged. Users, and all health professionals, should be aware that health applications, whatever the quality of their content, do not actually embrace such an approach.

[eHealth and mHealth: current developments in 2014 and perspectives in oncology]. / E-santé et m-santé : état des lieux en 2014 et apports potentiels en oncologie.

New information technologies and communication in health or "eHealth" is a way of improvement for management of chronic diseases. EHealth can improve patient care and care coordination especially in cancer patients who require a multidisciplinary approach. Treatments in oncology are complex and can result in new toxicities. Information of patients and of caregivers is a crucial issue. The patients require to be monitored and the caregivers need up-to-date information. The mobile component of eHealth: the mobile health or "mHealth" could provide to this need. This paper proposes to expose the principles of eHealth and its mobile component mHealth then to discuss their place in the management of cancer, for patients and caregivers.

[Smartphone application for blood gas interpretation]. / Interprétation biologique des gaz du sang par une application pour smartphone.

Ninety four per cent of health professionals use their smartphone for business purposes and more than 50% has medical applications. The «Blood Gas¼ application was created to be part of this dynamic and participate to e-health development in France. The «Blood Gas¼ application facilitates interpretation of the results of blood gas analysis using an algorithm developed with reference to a medical bibliography. It can detect some complex or intricate acid-base disorders in evaluating the effectiveness of the secondary response. The application also studied the respiratory status of the patient by calculating the PaO2/FiO2 ratio and the alveol-arterial gradient. It also indicates the presence of a shunt effect. Finally, a specific module to calculate the SID (strong ion difference) depending on the model of Stewart can detect complex acid-base disorders.