Health-focused conversational agents in person-centered care: a review of apps npj Digital Medicine

As such models are formal (and have already been accepted and in use), it is relatively easy to turn them into algorithmic form. The rationality in the case of models and algorithms is instrumental, and one can say that an algorithm is ‘the conceptual embodiment of instrumental rationality within’ (Goffey 2008, p. 19) machines. Thus, algorithms are an actualisation of reason in the digital domain (e.g. Finn 2017; Golumbia 2009). However, it is worth noting that formal models, such as game-theoretical models, do not completely describe reality or the phenomenon in question and its processes; they grasp only a slice of the phenomenon. Twenty of these apps (25.6%) had faulty elements such as providing irrelevant responses, frozen chats, and messages, or broken/unintelligible English.

Patients can book appointments directly from the chatbot, which can be programmed to assign a doctor, send an email to the doctor with patient information, and create a slot in both the patient’s and the doctor’s calendar. Chatbots provide quick and helpful information that is crucial, especially in emergency situations. Health crises can occur unexpectedly, and patients may require urgent medical attention at any time, from identifying symptoms to scheduling surgeries. Leveraging high-open-rate channels like WhatsApp, Voiceoc ensures patients never miss their scheduled appointments, optimizing clinic workflow and patient attendance. Voiceoc’s AI engine simplifies the appointment scheduling process, enabling patients to book OPD visits, lab tests, and more within seconds.

Conversational AI serves as the cornerstone of interactive healthcare experiences, enabling natural and intuitive communication between patients and virtual assistants. And if there is a short gap in a conversation, the chatbot cannot pick up the thread where it fell, instead having to start all over again. This may not be possible or agreeable for all users, and may be counterproductive for patients with mental illness. That happens with chatbots that strive to help on all fronts and lack access to consolidated, specialized databases. In the wake of stay-at-home orders issued in many countries and the cancellation of elective procedures and consultations, users and healthcare professionals can meet only in a virtual office. Recently, Google Cloud launched an AI chatbot called Rapid Response Virtual Agent Program to provide information to users and answer their questions about coronavirus symptoms.

Telemedicine uses technology to provide healthcare services remotely, while chatbots are AI-powered virtual assistants that provide personalized patient support. They offer a powerful combination to improve patient outcomes and streamline healthcare delivery. UK health authorities have recommended apps, such as Woebot, for those suffering from depression and anxiety (Jesus 2019).

For all their apparent understanding of how a patient feels, they are machines and cannot show empathy. They also cannot assess how different people prefer to talk, whether seriously or lightly, keeping the same tone for all conversations. use of chatbots in healthcare “The answers not only have to be correct, but they also need to adequately fulfill the users’ needs and expectations for a good answer.” More importantly, errors in answers from automated systems destroy trust more than errors by humans.

This will allow doctors and healthcare professionals to focus on more complex tasks while chatbots handle lower-level tasks. So, healthcare providers can use a chatbot dedicated to answering their patient’s most commonly asked questions. Questions about insurance, like covers, claims, documents, symptoms, business hours, and quick fixes, can be communicated to patients through the chatbot. Lastly one of the benefits of healthcare chatbots is that it provide reliable and consistent healthcare advice and treatment, reducing the chances of errors or inconsistencies. However, with the use of a healthcare chatbot, patients can receive personalized information and recommendations, guidance through their symptoms, predictions for potential diagnoses, and even book an appointment directly with you.

Most of the 78 apps reviewed focus on primary care and mental health, only 6 (7.59%) had a theoretical underpinning, and 10 (12.35%) complied with health information privacy regulations. Our assessment indicated that only a few apps use machine learning and natural language processing approaches, despite such marketing claims. Most apps allowed for a finite-state input, where the dialogue is led by the system and follows a predetermined algorithm.

What are the benefits of healthcare chatbots?

Second, we consider how the implementation of chatbots amplifies the project of rationality and automation in professional work as well as changes in decision-making based on epistemic probability. We then discuss ethical and social issues relating to health chatbots from the perspective of professional ethics by considering professional-patient relations and the changing position of these stakeholders on health and medical assessments. Finally, to ground our analysis, we employ the perspective of HCPs and list critical aspects and challenges relating to how chatbots may transform clinical capabilities and change patient-clinician relationships in clinical practices in the long run. We stress here that our intention is not to provide empirical evidence for or against chatbots in health care; it is to advance discussions of professional ethics in the context of novel technologies. The design principles of most health technologies are based on the idea that technologies should mimic human decision-making capacity. These systems are computer programmes that are ‘programmed to try and mimic a human expert’s decision-making ability’ (Fischer and Lam 2016, p. 23).

Advancements in ML have provided benefits in terms of accuracy, decision-making, quick processing, cost-effectiveness, and handling of complex data [2]. Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. The Oxford dictionary defines a chatbot as “a computer program that can hold a conversation with a person, usually over the internet.” They can also be physical entities designed to socially interact with humans or other robots. Predetermined responses are then generated by analyzing user input, on text or spoken ground, and accessing relevant knowledge [3].

Do patients prefer interacting with chatbots over humans?

By integrating with wearable devices or smart home technologies, these chatbots collect real-time data on metrics like heart rate, blood pressure, or glucose levels. Personalization was defined based on whether the healthbot app as a whole has tailored its content, interface, and functionality to users, including individual user-based or user category-based accommodations. Personalization features were only identified in 47 apps (60%), of which all required information drawn from users’ active participation. Forty-three of these (90%) apps personalized the content, and five (10%) personalized the user interface of the app.

Through conversation-based interactions, these chatbots can offer mindfulness exercises, stress management techniques, or even connect users with licensed therapists when necessary. The availability of such mental health support tools helps reduce barriers to accessing professional help while promoting emotional well-being in the medical procedure field. Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication.

The widespread use of chatbots can transform the relationship between healthcare professionals and customers, and may fail to take the process of diagnostic reasoning into account. This process is inherently uncertain, and the diagnosis may evolve over time as new findings present themselves. The development of more reliable algorithms for healthcare chatbots requires programming experts who require payment. Moreover, backup systems must be designed for failsafe operations, involving practices that make it more costly, and which may introduce unexpected problems. The app helps people with addictions  by sending daily challenges designed around a particular stage of recovery and teaching them how to get rid of drugs and alcohol.

A text-to-text chatbot by Divya et al [32] engages patients regarding their medical symptoms to provide a personalized diagnosis and connects the user with the appropriate physician if major diseases are detected. Rarhi et al [33] proposed a similar design that provides a diagnosis based on symptoms, measures the seriousness, and connects users with a physician if needed [33]. In general, these systems may greatly help individuals in conducting daily check-ups, increase awareness of their health status, and encourage users to seek medical assistance for early intervention. Healthbots are computer programs that mimic conversation with users using text or spoken language9. The underlying technology that supports such healthbots may include a set of rule-based algorithms, or employ machine learning techniques such as natural language processing (NLP) to automate some portions of the conversation.

How to Develop a Medical Chatbot App?

Forksy is the go-to digital nutritionist that helps you track your eating habits by giving recommendations about diet and caloric intake. This chatbot tracks your diet and provides automated feedback to improve your diet choices; plus, it offers useful information about every food you eat – including the number of calories it contains, and its benefits and risks to health. Informative chatbots provide helpful information for users, often in the form of pop-ups, notifications, and breaking stories.

Imagine having a knowledgeable assistant available round the clock to address your medical queries and concerns promptly. Click now to understand everything about AI Healthcare Chatbots and How they are a game-changer in  the industry. Dr. Liji Thomas is an OB-GYN, who graduated from the Government Medical College, University of Calicut, Kerala, in 2001. Liji practiced as a full-time consultant in obstetrics/gynecology in a private hospital for a few years following her graduation. She has counseled hundreds of patients facing issues from pregnancy-related problems and infertility, and has been in charge of over 2,000 deliveries, striving always to achieve a normal delivery rather than operative. “What doctors often need is wisdom rather than intelligence, and we are a long way away from a science of artificial wisdom.” Chatbots lack both wisdom and the flexibility to correct their errors and change their decisions.

These virtual assistants are trained using vast amounts of data from medical professionals, enabling them to provide accurate information and guidance to patients. Moreover, chatbots act as valuable resources for patients who require assistance but may not have immediate access to healthcare professionals. In cases where individuals face geographical barriers or limited availability of doctors, chatbots bridge the gap by offering accessible support and guidance.

The Ethics of Using Chatbots in Healthcare

For companies like QliqSOFT, which has focused its solutions on enhancing patient engagement and satisfaction, this comes as little surprise. According to the global tech market advisory firm ABI Research, AI spending in the healthcare and pharmaceutical industries is expected to increase from $463 million in 2019 to more than $2 billion over the next 5 years. And while these tools’ rise in popularity can be accredited to the very nature of the COVID-19 pandemic, AI’s role in healthcare has been growing steadily on its own for years — and that’s anticipated to continue. That provides an easy way to reach potentially infected people and reduce the spread of the infection. Using these safeguards, the HIPAA regulation requires that chatbot developers incorporate these models in a HIPAA-complaint environment. This requires that the AI conversations, entities, and patient personal identifiers are encrypted and stored in a safe environment.

In practice, however, clinicians make diagnoses in a more complex manner, which they are rarely able to analyse logically (Banerjee et al. 2009). Unlike artificial systems, experienced doctors recognise the fact that diagnoses and prognoses are always marked by varying degrees of uncertainty. They are aware that some diagnoses may turn out to be wrong or that some of their treatments may not lead to the cures expected. Thus, medical diagnosis and decision-making require ‘prudence’, that is, ‘a mode of reasoning about contingent matters in order to select the best course of action’ (Hariman 2003, p. 5). Yes, implementing healthcare chatbots can lead to cost savings by automating routine administrative tasks and reducing manual labor expenses within healthcare organizations. By streamlining workflows across different departments within hospitals or clinics, chatbots contribute significantly to cost savings for healthcare organizations.

Patients can receive immediate assistance on a wide range of topics such as medication information or general health advice. In addition to answering general health-related questions, chatbots also assist users with issues related to insurance coverage and making appointments. Patients can inquire about their insurance policies, coverage details, and any other concerns they may have regarding their healthcare plans.

This allows them to provide relevant responses tailored to the specific needs of each individual. One of the key advantages of chatbots is their ability to offer reliable and up-to-date information sourced from trusted medical databases or institutions. By accessing a vast pool of medical resources, chatbots can provide users with comprehensive Chat PG information on various health topics. This continuous monitoring allows healthcare providers to detect any deviations from normal values promptly. In case of alarming changes, the chatbot can trigger alerts to both patients and healthcare professionals, ensuring timely intervention and reducing the risk of complications.

With just a few clicks or taps, individuals can modify their appointment timing according to their needs or unexpected circumstances. This feature not only empowers patients but also reduces the burden on healthcare staff who would otherwise https://chat.openai.com/ need to handle these requests manually. Input modality, or how the user interacts with the chatbot, was primarily text-based (96%), with seven apps (9%) allowing for spoken/verbal input, and three (4%) allowing for visual input.

The perfect blend of human assistance and chatbot technology will enable healthcare centers to run efficiently and provide better patient care. With regard to health concerns, individuals often have a plethora of questions, both minor and major, that need immediate clarification. A healthcare chatbot can act as a personal health specialist, offering assistance beyond just answering basic questions. Here are five types of healthcare chatbots that are frequently used, along with their templates. Chatbots gather user information by asking questions, which can be stored for future reference to personalize the patient’s experience. With this approach, chatbots not only provide helpful information but also build a relationship of trust with patients.

Babylon Health offers AI-driven consultations with a virtual doctor, a patient chatbot, and a real doctor. Any chatbot you develop that aims to give medical advice should deeply consider the regulations that govern it. Navigating yourself through this environment will require legal counsel to guide you as you build this portion of your bot to address these different chatbot use cases in healthcare. Chatbot developers should employ a variety of chatbots to engage and provide value to their audience.

These categories are not exclusive, as chatbots may possess multiple characteristics, making the process more variable. Textbox 1 describes some examples of the recommended apps for each type of chatbot but are not limited to the ones specified. This global experience will impact the healthcare industry’s dependence on chatbots, and might provide broad and new chatbot implementation opportunities in the future. Chatbots can extract patient information by asking simple questions such as their name, address, symptoms, current doctor, and insurance details. The chatbots then, through EDI, store this information in the medical facility database to facilitate patient admission, symptom tracking, doctor-patient communication, and medical record keeping.

This integration ensures that patients are promptly assigned to an available doctor without any delays or confusion. Gone are the days of endless phone calls and waiting on hold while staff members manually check schedules. First, we used IAB categories, classification parameters utilized by 42Matters; this relied on the correct classification of apps by 42Matters and might have resulted in the potential exclusion of relevant apps. Additionally, the use of healthbots in healthcare is a nascent field, and there is a limited amount of literature to compare our results. Furthermore, we were unable to extract data regarding the number of app downloads for the Apple iOS store, only the number of ratings.

The use of chatbots in healthcare has become increasingly prevalent, particularly in addressing public health concerns, including COVID-19 pandemic during previous years. These AI-powered tools have proven to be invaluable in screening individuals for COVID-19 symptoms and providing guidance on necessary precautions. Imagine a scenario where a patient requires prescription refills but is unable to visit the clinic physically due to various reasons such as distance or time constraints. Chatbots come to the rescue by offering an efficient solution through their user-friendly interfaces.

Rapid diagnoses by chatbots can erode diagnostic practice, which requires practical wisdom and collaboration between different specialists as well as close communication with patients. HCP expertise relies on the intersubjective circulation of knowledge, that is, a pool of dynamic knowledge and the intersubjective criticism of data, knowledge and processes. Our review suggests that healthbots, while potentially transformative in centering care around the user, are in a nascent state of development and require further research on development, automation, and adoption for a population-level health impact. The study focused on health-related apps that had an embedded text-based conversational agent and were available for free public download through the Google Play or Apple iOS store, and available in English. A healthbot was defined as a health-related conversational agent that facilitated a bidirectional (two-way) conversation. Applications that only sent in-app text reminders and did not receive any text input from the user were excluded.

The search was further limited using the Interactive Advertising Bureau (IAB) categories “Medical Health” and “Healthy Living”. The IAB develops industry standards to support categorization in the digital advertising industry; 42Matters labeled apps using these standards40. Relevant apps on the iOS Apple store were identified; then, the Google Play store was searched with the exclusion of any apps that were also available on iOS, to eliminate duplicates. With the chatbot remembering individual patient details, patients can skip the need to re-enter their information each time they want an update. This feature enables patients to check symptoms, measure their severity, and receive personalized advice without any hassle. World-renowned healthcare companies like Pfizer, the UK NHS, Mayo Clinic, and others are all using Healthcare Chatbots to meet the demands of their patients more easily.

As conversational agents have gained popularity during the COVID-19 pandemic, medical experts have been required to respond more quickly to the legal and ethical aspects of chatbots. To fully leverage the potential of healthcare chatbots in the future, it is crucial for organizations to prioritize accuracy in data collection and feedback mechanisms. By ensuring that these virtual assistants collect precise patient information and provide reliable guidance based on medical best practices, trust between patients and technology can be established. AI Chatbots in healthcare have revolutionized the way patients receive support, providing round-the-clock assistance from virtual assistants.

AI Chatbots have revolutionized the healthcare industry by offering a multitude of benefits that contribute to improving efficiency and reducing costs. These intelligent virtual assistants automate various administrative tasks, allowing health systems, hospitals, and medical professionals to focus more on providing quality care to patients. Hesitancy from physicians and poor adoption by patients is a major barrier to overcome, which could be explained by many of the factors discussed in this section.

This can be further divided into interpersonal for providing services to transmit information, intrapersonal for companionship or personal support to humans, and interagent to communicate with other chatbots [14]. The next classification is based on goals with the aim of achievement, subdivided into informative, conversational, and task based. Response generation chatbots, further classified as rule based, retrieval based, and generative, account for the process of analyzing inputs and generating responses [16].

Another chatbot designed by Harshitha et al [27] uses dialog flow to provide an initial analysis of breast cancer symptoms. A study of 3 mobile app–based chatbot symptom checkers, Babylon (Babylon Health, Inc), Your.md (Healthily, Inc), and Ada (Ada, Inc), indicated that sensitivity remained low at 33% for the detection of head and neck cancer [28]. The number of studies assessing the development, implementation, and effectiveness are still relatively limited compared with the diversity of chatbots currently available. Further studies are required to establish the efficacy across various conditions and populations. Nonetheless, chatbots for self-diagnosis are an effective way of advising patients as the first point of contact if accuracy and sensitivity requirements can be satisfied.

In September 2020, the THL released the mobile contact tracing app Koronavilkku,Footnote 1 which can collaborate with Omaolo by sharing information and informing the app of positive test cases (THL 2020, p. 14). The most famous chatbots currently in use are Siri, Alexa, Google Assistant, Cordana and XiaoIce. Two of the most popular chatbots used in health care are the mental health assistant Woebot and Omaolo, which is used in Finland. From the emergence of the first chatbot, ELIZA, developed by Joseph Weizenbaum (1966), chatbots have been trying to ‘mimic human behaviour in a text-based conversation’ (Shum et al. 2018, p. 10; Abd-Alrazaq et al. 2020). Thus, their key feature is language and speech recognition, that is, natural language processing (NLP), which enables them to understand, to a certain extent, the language of the user (Gentner et al. 2020, p. 2).

No studies have been found to assess the effectiveness of chatbots for smoking cessation in terms of ethnic, racial, geographic, or socioeconomic status differences. Creating chatbots with prespecified answers is simple; however, the problem becomes more complex when answers are open. Bella, one of the most advanced text-based chatbots on the market advertised as a coach for adults, gets stuck when responses are not prompted [51]. Given all the uncertainties, chatbots hold potential for those looking to quit smoking, as they prove to be more acceptable for users when dealing with stigmatized health issues compared with general practitioners [7]. Inherited factors are present in 5% to 10% of cancers, including breast, colorectal, prostate, and rare tumor syndromes [62].

• Moreover, chatbots simplify appointment scheduling by allowing patients to book appointments online or through messaging platforms.
• Here are five types of healthcare chatbots that are frequently used, along with their templates.
• Chatbots called virtual assistants or virtual humans can handle the initial contact with patients, asking and answering the routine questions that inevitably come up.
• Pasquale (2020, p. 46) pondered, ironically, that cheap mental health apps are a godsend for health systems pressed by austerity cuts, such as Britain’s National Health Service.
• For example, many patients now require extended at-home support and monitoring, whereas health care workers deal with an increased workload.

We were able to determine the dialogue management system and the dialogue interaction method of the healthbot for 92% of apps. Dialogue management is the high-level design of how the healthbot will maintain the entire conversation while the dialogue interaction method is the way in which the user interacts with the system. While these choices are often tied together, e.g., finite-state and fixed input, we do see examples of finite-state dialogue management with the semantic parser interaction method. Ninety-six percent of apps employed a finite-state conversational design, indicating that users are taken through a flow of predetermined steps then provided with a response. The majority (83%) had a fixed-input dialogue interaction method, indicating that the healthbot led the conversation flow.

Healthcare chatbots facilitate continuous and personalized communication with patients, fostering a deeper level of engagement. Therefore, any AI experience built for healthcare must adhere to stringent regulatory standards and industry best practices. Compliance with regulations such as the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and Health Information Trust Alliance (HITRUST) is non-negotiable. Moreover, these tools facilitate seamless handoffs from virtual assistants to healthcare professionals, ensuring continuity of care and enhancing patient satisfaction. Equipped with comprehensive medical knowledge bases and sophisticated language models, these tools empower users to articulate their concerns and receive accurate responses in real-time.

Patients can request prescription refills directly through the chatbot app, saving valuable time and effort for both themselves and healthcare providers. AI Chatbots also play a crucial role in the healthcare industry by offering mental health support. They provide resources and guide users through coping strategies, creating a safe space for individuals to discuss their emotional well-being anonymously. Survivors of cancer, particularly those who underwent treatment during childhood, are more susceptible to adverse health risks and medical complications. Consequently, promoting a healthy lifestyle early on is imperative to maintain quality of life, reduce mortality, and decrease the risk of secondary cancers [87]. According to the analysis from the web directory, health promotion chatbots are the most commonly available; however, most of them are only available on a single platform.

To our knowledge, no review has been published examining the landscape of commercially available and consumer-facing healthbots across all health domains and characterized the NLP system design of such apps. This review aims to classify the types of healthbots available on the app store (Apple iOS and Google Play app stores), their contexts of use, as well as their NLP capabilities. AI and ML have advanced at an impressive rate and have revealed the potential of chatbots in health care and clinical settings. AI technology outperforms humans in terms of image recognition, risk stratification, improved processing, and 24/7 assistance with data and analysis. However, there is no machine substitute for higher-level interactions, critical thinking, and ambiguity [93]. Chatbots create added complexity that must be identified, addressed, and mitigated before their universal adoption in health care.

The search initially yielded 2293 apps from both the Apple iOS and Google Play stores (see Fig. 1). In the second round of screening, 48 apps were removed as they lacked a chatbot feature and 103 apps were also excluded, as they were not available for full download, required a medical records number or institutional login, or required payment to use. We conducted iOS and Google Play application store searches in June and July 2020 using the 42Matters software. A team of two researchers (PP, JR) used the relevant search terms in the “Title” and “Description” categories of the apps. The language was restricted to “English” for the iOS store and “English” and “English (UK)” for the Google Play store.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Similarly, a picture of a doctor wearing a stethoscope may fit best for a symptom checker chatbot. Hyro is an adaptive communications platform that replaces common-place intent-based AI chatbots with language-based conversational AI, built from NLU, knowledge graphs, and computational linguistics. Once the fastest-growing health app in Europe, Ada Health has attracted more than 1.5 million users, who use it as a standard diagnostic tool to provide a detailed assessment of their health based on the symptoms they input. Conversational chatbots are built to be contextual tools that respond based on the user’s intent.

Whether it’s a minor health issue or a crisis situation, chatbots are available 24/7 to address user concerns promptly. One of the key advantages of using chatbots for scheduling appointments is their ability to integrate with existing systems. These intelligent bots can instantly check doctors’ availability in real-time before confirming appointments.

While there were 78 apps in the review, accounting for the multiple categorizations, this multi-select characterization yielded a total of 83 (55%) counts for one or more of the focus areas. To facilitate this assessment, we develop and present an evaluative framework that classifies the key characteristics of healthbots. Concerns over the unknown and unintelligible “black boxes” of ML have limited the adoption of NLP-driven chatbot interventions by the medical community, despite the potential they have in increasing and improving access to healthcare.

In addition to educating patients, AI chatbots also play a crucial role in promoting preventive care. By using AI to offer personalized recommendations for healthy habits, such as exercise routines or dietary guidelines, they encourage patients to adopt healthier lifestyles. This proactive approach not only improves patient outcomes but also reduces the burden on healthcare systems by preventing the onset of chronic diseases. With their ability to offer tailored assistance, chatbots enhance patient satisfaction and improve outcomes. They alleviate the burden on hospital staff by handling routine queries, allowing physicians and nurses to dedicate more time to critical cases. Moreover, as artificial intelligence continues to advance, chatbots are becoming increasingly intelligent, capable of addressing complex medical questions with accuracy.