Healthcare Chatbots Research Articles

AI Based Healthcare Chatbot

AI Based Healthcare Chatbot

Doctor Recommendation Chatbot: A research study

Healthcare is very important in our life. As the population increases, day by day the quality of communication between patients and doctors becomes less effective. So because of a load of patients in hospitals, the quality of treatment decreases day by day. So, there should be a system by which a patient can easily communicate with a chatbot. Then the chatbot will examine the patient's problem and at last, the chatbot will recommend a specific doctor for a patient. With the help of artificial intelligence, the proposed system will be developed using natural language processing that means the chatbot first diagnoses the patient's problem then recommends a doctor for consultation. During the COVID pandemic, the trends of virtual assistance increased, and people are interacting with robots and virtual assistants. In many countries, there are robots in hospital receptionists to solve problems of people and work like humans. Chatbots make it easy to find a particular doctor by telling them problems. In this research study, there are different literature reviews included making a healthcare chatbot.

Mr. Dr. Health-Assistant Chatbot

Research says 60% of visits to a doctor are for simple small-scale diseases, 80% of which can be diagnosed at home using simple check-up. These diseases mostly include common cold and cough, headache, abdominal pains etc. Whereas, chat-bots in healthcare are highly in demand, which functioning can offer various services from symptom checking and appointment scheduling. Therefore, the purpose of the research aims to design, develop and evaluate a health-assistant Chat-bot Application entitled “MR.Dr.” that helps users to ask any personal query related to healthcare without physically available to the hospital. MR.Dr. is evaluated in term of usability. 30 respondents attended the survey of usability evaluation. In the system usability scale MR.Dr. achieved 87.6 % rating which means Grade A (excellent). User's feedback level was pretty satisfying where 24/7 service is the highest one.

Chatbots to Support Mental Wellbeing of People Living in Rural Areas: Can User Groups Contribute to Co-design?

Digital technologies such as chatbots can be used in the field of mental health. In particular, chatbots can be used to support citizens living in sparsely populated areas who face problems such as poor access to mental health services, lack of 24/7 support, barriers to engagement, lack of age appropriate support and reductions in health budgets. The aim of this study was to establish if user groups can design content for a chatbot to support the mental wellbeing of individuals in rural areas. University students and staff, mental health professionals and mental health service users (N = 78 total) were recruited to workshops across Northern Ireland, Ireland, Scotland, Finland and Sweden. The findings revealed that participants wanted a positive chatbot that was able to listen, support, inform and build a rapport with users. Gamification could be used within the chatbot to increase user engagement and retention. Content within the chatbot could include validated mental health scales and appropriate response triggers, such as signposting to external resources should the user disclose potentially harmful information or suicidal intent. Overall, the workshop participants identified user needs which can be transformed into chatbot requirements. Responsible design of mental healthcare chatbots should consider what users want or need, but also what chatbot features artificial intelligence can competently facilitate and which features mental health professionals would endorse.

AI based healthcare chat bot system

The main purpose of the healthcare chatbot system is to provide services in rural areas and government hospital for those people who are not able to take appointment or medical information from the doctors. They can solve their problem with the help of chatbot. With increasing population of India, increasing birth rate and decreasing death rate due to advancement in the medical field it has been observed that number of doctors are less to serve the need of the increasing population.

Acute Diseases Prognosis using Chatbot

The role of chatbots in healthcare is to help free-up valuable physician-time by reducing or eliminating unnecessary doctor’s appointments. As the increase in cost, various healthcare organizations are looking for different ways to manage cost while improving the user’s experience. As we know there is shortage of healthcare professionals that makes it increasingly necessary for us to augment technology with health facilities in order to allow doctors to focus on more critical patient needs. Keeping this in Mind we are aiming to develop a Project that will basically ask for Symptoms from the Patient and perform the Prognosis on the basis of already developed dataset. The Machine Learning Algorithm will work on that dataset of symptoms and their prognosis to tell exactly what has happened to the Patient and will help to Reach the Desired Consultant/Doctor with respect to the Prognosis. It will also help the Patients to get Useful Information regarding different diseases that may help to deal with some Chronic Diseases at an early Stage!’

Can Chatbots Help Support a Person’s Mental Health? Perceptions and Views from Mental Healthcare Professionals and Experts

The objective of this study was to understand the attitudes of professionals who work in mental health regarding the use of conversational user interfaces, or chatbots, to support people’s mental health and wellbeing. This study involves an online survey to measure the awareness and attitudes of mental healthcare professionals and experts. The findings from this survey show that more than half of the participants in the survey agreed that there are benefits associated with mental healthcare chatbots (65%, p < 0.01). The perceived importance of chatbots was also relatively high (74%, p < 0.01), with more than three-quarters (79%, p < 0.01) of respondents agreeing that mental healthcare chatbots could help their clients better manage their own health, yet chatbots are overwhelmingly perceived as not adequately understanding or displaying human emotion (86%, p < 0.01). Even though the level of personal experience with chatbots among professionals and experts in mental health has been quite low, this study shows that where they have been used, the experience has been mostly satisfactory. This study has found that as years of experience increased, there was a corresponding increase in the belief that healthcare chatbots could help clients better manage their own mental health.

Disease Prediction Chatbot

Applications in the field of machine learning and artificial intelligence have been in great demand over the recent decade. Now it has various applications in the field of health industry. With the help of machine learning algorithm prediction of diseases has been made easier. Now doctors can concentrate only on treatment with the help of technology. Technology is accelerating innovations in the healthcare domain which has increased people’s standard of living over the years. Here in our project we are making a healthcare chatbot with help of Natural language processing and machine learning algorithm to predict disease. User interacts with the chatbot just like one interacts with his doctor and based on the symptoms provided by users and the chatbot will identify the symptom and predict the disease.

A user-centered chatbot to identify and interconnect individual, social and environmental risk factors related to overweight and obesity

ABSTRACT The objective of this study was to assess the feasibility of using a user-centered chatbotfor collecting linked data to study overweight and obesity causes ina target population. In total 980 people participated in the feasibility study organized in three studies: (1) within a group of university students (88 participants), (2) in a small town (422 participants), and (3) within a university community (470 participants). We gathered self-reported data through the Wakamola chatbot regarding participants diet, physical activity, social network, living area, obesity-associated diseases, and sociodemographic data. For each study, we calculated the mean Body Mass Index (BMI) and number of people in each BMI level. Also, we defined and calculated scores (1–100 scale) regarding global health, BMI, alimentation, physical activity and social network. Moreover, we graphically represented obesity risk for living areas and the social network with nodes colored by BMI. Students group results: Mean BMI 21.37 (SD 2.57) (normal weight), 8 people underweight, 5 overweight, 0 obesity, global health status 78.21, alimentation 63.64, physical activity 65.08 and social 26.54, 3 areas with mean BMI level of obesity, 17 with overweight level. Small town´s study results: Mean BMI 25.66 (SD 4.29) (overweight), 2 people underweight, 63 overweight, 26 obesity, global health status 69.42, alimentation 64.60, physical activity 60.61 and social 1.14, 1 area with mean BMI in normal weight; University´s study results: Mean BMI 23.63 (SD 3.7) (normal weight), 22 people underweight, 86 overweight, 28 obesity, global health status 81.03, alimentation 81.84, physical activity 70.01 and social 1.47, 3 areas in obesity level, 19 in overweight level. Wakamola is a health care chatbot useful to collect relevant data from populations in the risk of overweight and obesity. Besides, the chatbot provides individual self-assessment of BMI and general status regarding the style of living. Moreover, Wakamola connects users in a social network to help the study of O&O´s causes from an individual, social and socio-economic perspective.

MedBot: Conversational Artificial Intelligence Powered ChatBot for Health Care

Healthcare is essential for leading a happy life. However, It is exceedingly difficult to seek a doctor's appointment in the event of a health condition. The suggested solution is to create a medical chatbot that can diagnose the disease and provide basic information about it before consulting a doctor. The medical chatbot was created with the aim of lowering healthcare costs and increasing access to medical information. The medical chatbot was developed with the aim of lowering healthcare costs. This system will improve processes services and outcomes and reduce cash. In a self-service mode, users communicate with the ChatBot through text. Healthcare ChatBot was developed with the aim of maintaining internal documents. It operates as a medical reference guide, assisting patients in learning about their condition and determining the disease's status based on symptoms. The main aspect of the Healthcare ChatBot is that the user can connect with the Bot directly without any restrictions, and that the user can use the app at any time without hesitation and without having to pay any money to learn about their condition. We will add more modules in the future, such as doctors and local hospitals, so that users have a better of what they want to do next. Patients can talk to a text-to-text diagnosis bot about their health conditions, and the bot will give them a customized diagnosis based on their understanding symptoms. As a result, people will be more aware of their health, and they will be better protected.

‘K-Bot’ Knowledge Enabled Personalized Healthcare Chatbot

In the present scenario when most of the things are just a click away, people consider visiting hospitals as the most effective, reliable, and convenient way for their routine check-ups or disease diagnosis. In the past few years, Machine Learning has been playing a major role in the domain of healthcare. The proposed system or approach focuses on creating an alternate using Decision Tree Algorithm where people can interact with chatbot and it will identify other symptoms and predict the disease along with the confidence level and thus recommending a specialized doctor. Using the above framework can help people save their time and money as well.

A predictive internet-based model for COVID-19 hospitalization census

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.

Development of a practical training method for a healthcare artificial intelligence (AI) chatbot

### Summary box #### What are the new findings? #### How might it impact on healthcare in the future? The rise of healthcare chatbots using artificial intelligence (AI) to understand...

AI Health Care Chatbot

Healthcare bot is a technology that makes interaction between man and machine possible by using Artificial Intelligence with the support of dialog flow. Now a day people tend to seek knowledge or information from internet that concern with health through online healthcare services. To lead a good life healthcare is very much important. But it is very difficult to obtain the consultation with the doctor in case of any health issues. The basic aim of this system is to bridge the vocabulary gap between the doctors by giving self-diagnosis from the comfort of one’s place. The proposed idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. To reduce the healthcare costs and improve accessibility to medical knowledge the medical bot is built. Certain bots act as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the real benefit of a bot only when it can diagnose all kind of disease and provide necessary information. Hence, people will have an idea about their health and have the right protection.

Simple and Steady Interactions Win the Healthy Mentality

Although suicide among older adults is increasing in severity in the aging among South Koreans, older adult depression and anxiety, which are two crucial factors affecting suicide in this population, are not adequately addressed by the status quo. Existing products and services emphasize physical needs; however, they still lack a user-oriented approach. Moreover, offers for mental health care are costly and exclude potential users from the development process, thus inhibiting older adults from having better access to such services. To address this problem, we designed and developed a mental health care chatbot, Yeonheebot, to decrease anxiety and depression among older adults. Yeonheebot initiates conversations regarding daily care for the well-being of older adults and provides features related to their interests as initiatives for using the mental health care chatbot. To examine whether a short-term, yet repeating conversation with the agent could help reduce anxiety and depression among older adults, we conducted a 2-week field study with 25 users. The results indicate that the constant use of Yeonheebot decreased the levels of anxiety and depression by 36% and 18%, respectively. Our research implies that simple and repeated interactions could help Korean older adults cope with anxiety and depression.

The Smart Health Care Prediction using Chatbot

Our healthcare is very much important to lead a peaceful and honest life. If any health issue occurs, we need to go to the hospital and consult the doctor for the very minor problems. our healthcare chatbot is developed to help the people to predict their health issue early at home before they visit the doctor or hospital for the mi nor problems. For the minor issues we are spending lots of costs. The healthcare chatbot is design to reduce such costs and also its improves the efficiency of the medical healthcare. There were many chatbots available they act as a reference for the patient to know more about their health issue. The healthcare chatbot is something different from the other chatbots which predicts the diseases by using symptoms and gives the doctor details to consult the doctor. The healthcare chatbot is developed by using AI in the text to text conversation mode. The user who knows only to write and read can use this chatbot for their minor issue. In this healthcare chatbot, the system predicts the diseases based on the symptom given by the user using the pattern concept in AIML algorithm. The system also predicts the prescription and also give the doctor details to the user based on the diseases predicted for their symptom given. By using this healthcare chat bot people will know the minor diseases at early stage with no costs. Whenever the patient or user gets the time they will consult doctor for their health issue. This will make people to know more about their health issue anywhere at any time.

Smart Health Care Chatbot for Prognosis of Treatments and Disease Diagnosis Using Machine Learning

In this present world there are various diseases for which treatments and remedies are available abundantly. It is impossible for human to remember all the precautions and remedies to cure the disease. There is no relevant platform that could exhibit all the diseases and their respective remedies. Health professionals are not always available to users on all the time. Hence, the necessity of health care Chatbot plays a major role in this current world. In the proposed idea, we have created a HealthCare Chatbot with Artificial Intelligence techniques which can process the text input and predict the diseases associated with the symptoms given by the user. The HealthCare Chatbot implemented here is a user friendly platform which predicts the probable diseases and the home remedies, we can imply to cure based on the symptoms observed by the user in their knowledge.

Visual presentation of mental healthcare chatbots for user experience

Today the digital healthcare market and interests in mental health chatbots are growing. People get easily accessed to chatbot interactions, and chatbots can be used to support people’s emotional stability and psychological well-being. Consequently, many mental healthcare chatbots have been designed to reduce the likelihood of the occurrence of mental health issues by improving self-metacognition through early interventions. However, only few studies have discussed specific factors, which mental healthcare chatbot design may affect user experience. Besides, although visual presentations throughout a chatbot system influence users’ positive/negative experiences, most chatbot studies so far have focused on identity design rather than graphical interfaces and non-verbal visual communication tools. While it is important to examine specific visual design elements, it is also important to examine overall visual design requirements. Therefore, this study explored the user experience of mental health chatbots in terms of identity design, chatbot interface design, and visual communication tools. In this study, participants’ data were collected by pre/post preference evaluations, the system usability scale questionnaire, and semi-structured interview related to selected chatbot systems (i.e. Replika, Youper, Sayana, Woebot). The collected data were qualitatively analysed, and consequently, considerations were suggested for designing mental healthcare chatbots.

How to Evaluate Health Applications with Conversational User Interface?

Application of conversational user interfaces (CUI) or chatbots to healthcare is gaining interest fueled by the rising power of artificial intelligence, increasing popularity of mobile health applications and the desire for engagement and usability. While their use is mainly justified by increasing adherence to mobile health applications and facilitating interactions with the system, the question arises: How can such systems be evaluated in a reliable manner? This paper introduces an evaluation framework for health systems whose core interaction principle is a CUI. We derive quality dimensions and attributes by collecting relevant evaluation aspects from applications that have been developed in previous work and from literature on health chatbots. The collected aspects are aggregated into six thematic categories for chatbot quality, including user experience, linguistic, task-oriented and artificial intelligence perspectives, but also healthcare quality and system quality perspectives. The framework is intended to support developers and researchers in the domain of chatbots in healthcare in selecting relevant quality attributes to be assessed before their systems are distributed to patients.

Technical Metrics Used to Evaluate Health Care Chatbots: Scoping Review.

BackgroundDialog agents (chatbots) have a long history of application in health care, where they have been used for tasks such as supporting patient self-management and providing counseling. Their use is expected to grow with increasing demands on health systems and improving artificial intelligence (AI) capability. Approaches to the evaluation of health care chatbots, however, appear to be diverse and haphazard, resulting in a potential barrier to the advancement of the field.ObjectiveThis study aims to identify the technical (nonclinical) metrics used by previous studies to evaluate health care chatbots.MethodsStudies were identified by searching 7 bibliographic databases (eg, MEDLINE and PsycINFO) in addition to conducting backward and forward reference list checking of the included studies and relevant reviews. The studies were independently selected by two reviewers who then extracted data from the included studies. Extracted data were synthesized narratively by grouping the identified metrics into categories based on the aspect of chatbots that the metrics evaluated.ResultsOf the 1498 citations retrieved, 65 studies were included in this review. Chatbots were evaluated using 27 technical metrics, which were related to chatbots as a whole (eg, usability, classifier performance, speed), response generation (eg, comprehensibility, realism, repetitiveness), response understanding (eg, chatbot understanding as assessed by users, word error rate, concept error rate), and esthetics (eg, appearance of the virtual agent, background color, and content).ConclusionsThe technical metrics of health chatbot studies were diverse, with survey designs and global usability metrics dominating. The lack of standardization and paucity of objective measures make it difficult to compare the performance of health chatbots and could inhibit advancement of the field. We suggest that researchers more frequently include metrics computed from conversation logs. In addition, we recommend the development of a framework of technical metrics with recommendations for specific circumstances for their inclusion in chatbot studies.