A chat-bot in rheumatoid arthritis treatment control

BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune rheumatic disease with joint damage and systemic manifestations, which worsens the quality of life and life expectancy, leading to disability in the absence of effective therapy at a young age. The goal of RA treatment is to achieve remission/low disease activity. Frequent monitoring of the disease is needed (every 13 months until the goal is reached, then every 36 months), which is not always possible due to remoteness of residence, financial capabilities of patients, and epidemic situation. Remote monitoring appears to be a possible solution to the problem; however, the effectiveness of telemedical technologies in the treatment of patients with RA is not sufficiently studied. AIM: To investigate the effectiveness of remote-control treatment of patients with RA of high and moderate activity using a chat-bot. METHODS: An algorithm for remote monitoring and communication with RA patients was developed. The chat-bot performs a survey every month and out-of-schedule when the patients condition worsens and provides the data to the doctor in a convenient form. Regular assessments of RA activity, functional impairment, and quality of life and correction of recommendations if necessary are made. All participants are coded, with only the doctor having access to personal data. In remote (60 patients) and traditional (30 patients) control groups, the time to remission/low disease activity will be compared. Adherence to the chat-bot and cost-effectiveness analysis will be studied. RESULTS: Twenty patients were trained on how to use the chat-bot and have been using the program for 2 months. The condition is monitored and online counseling is provided if necessary. Nineteen patients had no difficulties when working with the chat-bot. One patient needed a second consultation on how to use the chat-bot. Half of the patients were over 60 years old. Most patients prefer remote counseling to a face-to-face appointment. Patients report an improved understanding of the disease, treatment principles, and methods of self-assessment of the joint condition. Remote monitoring is planned to achieve stable control of RA activity by timely detection of exacerbations and therapy correction and assessment of the need for hospitalization, which will help to reduce the period of remission/low RA activity. The economic cost of treating RA is expected to be reduced. CONCLUSIONS: Remote monitoring using a chat-bot to improve the effectiveness of RA treatment is an important aspect of current rheumatology and a potential method for increasing the availability of medical care. The results may serve as a basis for further research on telemedical technologies and the development and application of personalized algorithms for monitoring, prevention, and treatment of patients with rheumatic diseases

Quality of life and adherence to therapy in patients with chronic heart failure who were remotely monitored by chatbot compared to the standard follow-up group for 3 months

BACKGROUND: Chronic heart failure (CHF) is one of the leading causes of death. Telemedicine and remote monitoring (RM) are a way to increase life expectancy and quality of life in patients with CHF. Methods based on messengers familiar to patients promote adherence and do not require additional training. AIM: To compare quality of life and adherence to therapy in patients with CHF who were on RM using a chatbot compared to the standard follow-up (SFU) group for 3 months. METHODS: Patients with CHF on optimal drug therapy discharged from the hospital were included in the study. Comparison groups were formed according to the method of observation, particularly, RM and SFU. A chatbot was set up for patients in the RM group. Monitoring was done using a seven-question survey sent daily. The signs of decompensation (red flags [RF]) were increased edema, dyspnea, body weight 2 kg per week, and changes in individual parameters of heart rate and blood pressure. If a RF was detected, telephone contact was made, and the therapy was corrected if necessary. Quality of life was assessed according to the Minnesota Quality of Life Questionnaire for patients with CHF (highest, 0 points; lowest, 105 points), and adherence was assessed using the Adherence Scale of the National Society for Evidence-based Pharmacotherapy. RESULTS: A total of 60 patients were included in the study; 37 patients completed a 3-month follow-up. The RM group (n=17, 13 men, 76.5%; median age 61 [51; 62]) and comparison group (n=20, 14 men, 70%; mean age 64.98.9) were comparable according to the functional class (New York Heart Association), but differed in ejection fraction (42.813% versus 53.210.4% [p 0.05]). Adherence to the chat-bot was 67.2%. Adherence to therapy was not significantly different between the RM and SFU groups accounting for (17 [100%]) and (18 [90%], respectively, (p=0.62). In the RM group, RF was detected in 7 (41%) patients. Only one patient required correction of therapy. Patients in the RM group required no referral to a medical facility, whereas 2 patients in the SFU group required medical care. Quality of life was statistically significantly higher in the RM group, reaching 28.713.9 points compared to 37.717.9 points in the SFU group (p=0.04). CONCLUSIONS: After 3 months, patients in the RM group were committed to the chatbot, with adherence to therapy comparable to the SFU group. Quality of life was statistically significantly higher in the RM group. Patients in the RM group did not go to medical facilities, in contrast to the SFU group. The limitations of the study were the small sample size and short follow-up period. The results require further research to obtain additional data