The Development and Use of Chatbots in Public Health: Scoping Review PMC

For example, chatbots can schedule appointments, answer common questions, provide medication reminders, and even offer mental health support. These chatbots also streamline internal support by giving these professionals quick access to information, such as patient history and treatment plans. Trust AI assumes a critical role in navigating complexities, particularly in AI-powered chatbots. Serving as a link between theoretical analytical expressions and the numerical models derived through Machine Learning, Trust AI addresses the challenge of explainability. The nuanced nature of human-machine interactions demands a delicate balance between analytical rigor and user-friendly outcomes. We need the multifaceted Trust AI approach to augment transparency and interpretability, fostering trust in AI-driven communication systems.

• Rasa stack provides you with an open-source framework to build highly intelligent contextual models giving you full control over the process flow.
• If certain classes are overrepresented or underrepresented, the resultant chatbot model may be skewed towards predicting the overrepresented classes, thereby leading to unfair outcomes for the underrepresented classes (22).
• A chatbot further eases the process by allowing patients to know available slots and schedule or delete meetings at a glance.
• One example of a task-oriented chatbot is a medical chatbot called Omaolo developed by the Finnish Institute for Health and Welfare (THL), which is an online symptom assessment tool (e-questionnaire) (Atique et al. 2020, p. 2464; THL 2020).

The role of a medical professional is far more multifaceted than simply diagnosing illnesses or recommending treatments. Physicians and nurses provide comfort, reassurance, and empathy during what can be stressful and vulnerable times for patients [6]. This doctor-patient relationship, built on trust, rapport, and understanding, is not something that can be automated or substituted with AI chatbots. Additionally, while chatbots can provide general health information and manage routine tasks, their current capabilities do not extend to answering complex medical queries. These queries often require deep medical knowledge, critical thinking, and years of clinical experience that chatbots do not possess at this point in time [7]. Thus, the intricate medical questions and the nuanced patient interactions underscore the indispensable role of medical professionals in healthcare.

Start by defining specific objectives for the chatbot, such as appointment scheduling or symptom checking, aligning with existing workflows. Identify the target audience and potential user scenarios to tailor the chatbot’s functionalities. Integration with electronic health record (EHR) systems streamlines access to relevant patient data, enhancing personalized assistance.

As we move towards the future, the editorial underscores the importance of a collaborative model, wherein AI chatbots and medical professionals work together to optimize patient outcomes. Despite the potential for AI advancements, the likelihood of chatbots completely replacing medical professionals remains low, as the complexity of healthcare necessitates human involvement. The ultimate aim should be to use technology like AI chatbots to enhance patient care and outcomes, not to replace the irreplaceable human elements of healthcare. With the increasing popularity of conversational agents in healthcare spaces involving the COVID-19 pandemic, medical experts (e.g. McGreevey et al. 2020) have become concerned about the consequences of these emerging technologies on clinical practices.

They use AI algorithms to analyze symptoms reported by patients and suggest possible causes or conditions. The chatbot’s NLP capabilities analyze the user’s chatbot in healthcare input to understand their intent and desired outcome. This involves identifying keywords, phrases, and context to interpret the user’s query or request.

The Development and Use of Chatbots in Public Health: Scoping Review

Our industry-leading expertise with app development across healthcare, fintech, and ecommerce is why so many innovative companies choose us as their technology partner. We recommend using ready-made SDKs, libraries, and APIs to keep the chatbot development budget under control. This practice lowers the cost of building the app, but it also speeds up the time to market significantly.

Open up the NLU training file and modify the default data appropriately for your chatbot. In this article, we shall focus on the NLU component and how you can use Rasa NLU to build contextual chatbots. However, humans rate a process not only by the outcome but also by how easy and straightforward the process is.

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. Apps were also excluded if they were specific to an event (i.e., apps for conferences or marches). Dr. Rachel Goodman and colleagues at Vanderbilt University investigated chatbox responses in a recent study in Jama.

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Although apps like Hinge have added new features such as conversation-starting prompts on profiles and voice memos, dating apps mostly have stuck to the basic swiping method invented by Tinder more than a decade ago. A 2022 survey found that nearly 80 percent of people across different age groups reported feeling burned out or emotionally fatigued when using dating apps. On Volar, people create dating profiles by messaging with a chatbot instead of filling out a profile. They answer questions about what they do for work or fun and what they’re looking for in a partner, including preferences about age, gender, and personal qualities.

• Ms Brackley, who herself has dyslexia, ADHD and autism, says AI chatbots allow her to “outsource my challenge without having to overly explain why [to another human]”.
• This area holds tremendous potential, as an estimated ≥50% of all patients with cancer have used radiotherapy during the course of their treatment.
• These categories are not exclusive, as chatbots may possess multiple characteristics, making the process more variable.
• Claude is a noteworthy chatbot to reference because of its unique characteristics.

The American Medical Association has also adopted the Augmented Intelligence in Health Care policy for the appropriate integration of AI into health care by emphasizing the design approach and enhancement of human intelligence [109]. An area of concern is that chatbots are not covered under the Health Insurance Portability and Accountability Act; therefore, users’ data may be unknowingly sold, traded, and marketed by companies [110]. On the other hand, overregulation may diminish the value of chatbots and decrease the freedom for innovators. Consequently, balancing these opposing aspects is essential to promote benefits and reduce harm to the health care system and society.

Additionally, focus areas including anesthesiology, cancer, cardiology, dermatology, endocrinology, genetics, medical claims, neurology, nutrition, pathology, and sexual health were assessed. As apps could fall within one or both of the major domains and/or be included in multiple focus areas, each individual domain and focus area was assigned a numerical value. 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. Further, it is unclear how the performance of NLP-driven chatbots should be assessed.

Apps were assessed using an evaluation framework addressing chatbot characteristics and natural language processing features. Most healthbots are patient-facing, available on a mobile interface and provide a range of functions including health education and counselling support, assessment of symptoms, and assistance with tasks such as scheduling. 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.

This results in a frustrating user experience and often leads the chatbot to transfer the user to a live support agent. In some cases, transfer to a human agent isn’t enabled, causing the chatbot to act as a gatekeeper and further frustrating the user. 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].

You can foun additiona information about ai customer service and artificial intelligence and NLP. This finding may reflect both the degree to which conversational technologies lend themselves to the kinds of interactive methodologies used in mental health and the necessity for greater scrutiny of the methods that are used by health practitioners in field. Our inclusion criteria were for the studies that used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact. We included experimental studies where chatbots were trialed and showed health impacts. We chose not to distinguish between embodied conversational agents and text-based agents, including both these modalities, as well as chatbots with cartoon-based interfaces. In the light of the huge growth in the deployment of chatbots to support public health provision, there is pressing need for research to help guide their strategic development and application [13].

This paper complements this research and addresses a gap in the literature by assessing the breadth and scope of research evidence for the use of chatbots across the domain of public health. Chatbots have the potential to address many of the current concerns regarding cancer care mentioned above. This includes the triple aim of health care that encompasses improving the experience of care, improving the health of populations, and reducing per capita costs [21].

For those interested in this unique service, we have a complete guide on how to use Miscrosfot’s Copilot chatbot. Claude is free to use with a $20 per month Pro Plan, which increases limits and provides early access to new features. They also appreciate its larger context window to understand the entire conversation at hand better. It helps summarize content and find specific information better than other tools like ChatGPT because it can remember more. Copy.ai has undergone an identity shift, making its product more compelling beyond simple AI-generated writing. Many of those debating the pros and cons of AI agree that its awesome potential cannot be left only in the hands of those who want to manipulate it for power or for profit.

Top Health Categories

The integration of chatbots stands out as a revolutionary force, reshaping the dynamics of patient engagement and information dissemination. Here, we explore the distinctive advantages that medical chatbots offer, underscoring their pivotal role in the healthcare landscape. It is critical to incorporate multilingual support and guarantee accessibility in order to serve a varied patient population. By taking this step, the chatbot’s reach is increased and it can effectively communicate with users who might prefer a different language or who need accessibility features. Chatbot becomes a vital point of communication and information gathering at unforeseeable times like a pandemic as it limits human interaction while still retaining patient engagement.

Cancer has become a major health crisis and is the second leading cause of death in the United States [18]. The exponentially increasing number of patients with cancer each year may be because of a combination of carcinogens in the environment and improved quality of care. The latter aspect could explain why cancer is slowly becoming a chronic disease that is manageable over time [19].

Regularly update the chatbot’s knowledge base to incorporate advancements in remote monitoring technologies. By prioritizing real-time data collection and continuous learning, the chatbot facilitates remote patient monitoring without compromising accuracy. Seamless integration of chatbots into EHR systems involves compliance with healthcare standards like HL7 and FHIR. Develop interfaces that enable the chatbot to access and retrieve relevant information from EHRs. Prioritize interoperability to ensure compatibility with diverse healthcare applications. Implement encryption protocols for secure data transmission and stringent access controls to regulate data access.

If you wish to know anything about a particular disease, a healthcare chatbot can gather correct information from public sources and instantly help you. Furthermore, if there was a long wait time to connect with an agent, 62% of consumers feel more at ease when a chatbot handles their queries, according to Tidio. As we’ll read further, a healthcare chatbot might seem like a simple addition, but it can substantially impact and benefit many sectors of your institution. Healthcare Chatbot is an AI-powered software that uses machine learning algorithms or computer programs to interact with leads in auditory or textual modes.

That chatbot helps customers maintain emotional health and improve their decision-making and goal-setting. Users add their emotions daily through chatbot interactions, answer a set of questions, and vote up or down on suggested articles, quotes, and other content. Doctors also have a virtual assistant chatbot that supplies them with necessary info – Safedrugbot. The bot offers healthcare providers data the right information on drug dosage, adverse drug effects, and the right therapeutic option for various diseases.

The app then spins up a chatbot that tries to mimic not only a person’s interests but also their conversational style. The free version should be for anyone who is starting and is interested in the AI industry and what the technology can do. Many other AI chatbots are built on the technologies that OpenAI has developed, which means they’re often behind the curve with new features and innovation. The following AI chatbots have been carefully selected based on various factors, including ease of use, features, functionality, pros and cons, and customer reviews.

One of the authors screened the titles and abstracts of the studies identified through the database search, selecting the studies deemed to match the eligibility criteria. The second author then screened 50% of the same set of identified studies at random to validate the first author’s selection. The papers meeting the criteria for inclusion at the title- and abstract-screening stage were retrieved and reviewed independently by both authors, with subsequent discussion about discrepancies and resolution to end with an agreed upon list of included studies.

We’ll briefly show you how to create a healthcare chatbot for Instagram, but you can do the same for WhatsApp, Facebook, or Telegram. BetterHelp has a healthcare chatbot on Facebook responsible for welcoming new visitors and helping them get started. The chatbot simplifies the whole onboarding process and makes the online therapy portal even more intuitive and approachable. But the healthcare industry is quickly catching up, and even small clinics are trying to automate their most repetitive processes and offer 24/7 availability to their visitors.

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, Chat GPT critical thinking, and ambiguity [93]. Chatbots create added complexity that must be identified, addressed, and mitigated before their universal adoption in health care. Design intuitive interfaces for seamless interactions, reducing the risk of frustration. Map out user journeys for different scenarios, ensuring the chatbot’s adaptability.

The dominos fall when chatbots push patients from traditional clinical face-to-face practice to more complicated automated systems. However, despite certain disadvantages of chatbots in healthcare, they add value where it really counts. They can significantly augment the efforts of healthcare professionals, offering time-saving support and contributing meaningfully in crucial areas. Such types of chatbots are specifically developed to provide mental health support.

Studies have shown that the interpretation of medical images for the diagnosis of tumors performs equally well or better with AI compared with experts [53-56]. In addition, automated diagnosis may be useful when there are not enough specialists to review the images. This was made possible through deep learning algorithms in combination with the increasing availability of databases for the tasks of detection, segmentation, and classification [57]. For example, Medical Sieve (IBM Corp) is a chatbot that examines radiological images to aid and communicate with cardiologists and radiologists to identify issues quickly and reliably [24]. Similarly, InnerEye (Microsoft Corp) is a computer-assisted image diagnostic chatbot that recognizes cancers and diseases within the eye but does not directly interact with the user like a chatbot [42]. Even with the rapid advancements of AI in cancer imaging, a major issue is the lack of a gold standard [58].

Family history collection is a proven way of easily accessing the genetic disposition of developing cancer to inform risk-stratified decision-making, clinical decisions, and cancer prevention [63]. The web-based chatbot ItRuns (ItRunsInMyFamily) gathers family history information at the population level to determine the risk of hereditary cancer [29]. We have yet to find a chatbot that incorporates deep learning to process large and complex data sets at a cellular level.

Patients can receive support and care remotely, reducing the need for in-person visits and improving access to healthcare services. Within the realm of telemedicine, chatbots equipped with AI capabilities excel at preliminary patient assessments, assisting in case prioritization, and providing valuable decision support for healthcare providers. A noteworthy example is TytoCare’s telehealth platform, where AI-driven chatbots guide patients through self-examination procedures during telemedicine consultations, ensuring the integrity of collected data (9). Chatbots can be connected with electronic health records, systems that manage medical practices, and other healthcare-related platforms. This allows them to access and utilize patient data to provide personalized care and recommendations. Integration also streamlines workflows for healthcare providers by automating routine tasks and providing real-time patient information.

Despite the obvious pros of using healthcare chatbots, they also have major drawbacks. Chatbots are well equipped to help patients get their healthcare insurance claims approved speedily and without hassle since they have been with the patient throughout the illness. Not only can they recommend the most useful insurance policies for the patient’s medical condition, but they can save time and money by streamlining the process of claiming insurance and simplifying the payment process. Chatbots can help patients manage their health more effectively, leading to better outcomes and a higher quality of life. These bots can help patients stay on track with their healthcare goals and manage chronic conditions more effectively by providing personalized support and assistance. The challenge of explainability in AI-powered communication intertwines with establishing trust, amplified in dynamic chatbot interactions.

Chatbots drive cost savings in healthcare delivery, with experts estimating that cost savings by healthcare chatbots will reach $3.6 billion globally by 2022. More research is needed to fully understand the effectiveness of using chatbots in public health. Concerns with the clinical, legal, and ethical aspects of the use of chatbots for health care are well founded given the speed with which they have been adopted in practice. Future research on their use should address these concerns through the development of expertise and best practices specific to public health, including a greater focus on user experience.

Finally, you have to ensure that they enter the correct data, don’t misspell your name, etc. Chatbots are also great for conducting feedback surveys to assess patient satisfaction. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

And in 39 percent of more than 1,000 recorded responses from the chatbot, it either refused to answer or deflected the question. The researchers said that although the refusal to answer questions in such situations is likely the result of preprogrammed https://chat.openai.com/ safeguards, they appeared to be unevenly applied. The report further claims that in addition to bogus information on polling numbers, election dates, candidates, and controversies, Copilot also created answers using flawed data-gathering methodologies.

The new app is designed to do an array of tasks, including serving as a personal tutor, helping computer programmers with coding tasks and even preparing job hunters for interviews, Google said. First, there were talking digital assistants like Siri, Alexa and Google Assistant. AI chatbots cannot perform surgeries or invasive procedures, which require the expertise, skill, and precision of human surgeons. It’s perfect for people creating content for the internet that needs to be optimized for SEO. Some people say there is a specific culture on the platform that might not appeal to everyone. Claude 3 Sonnet is able to recognize aspects of images so it can talk to you about them (as well as create images like GPT-4).

No-show appointments result in a considerable loss of revenue and underutilize the physician’s time. The healthcare chatbot tackles this issue by closely monitoring the cancellation of appointments and reports it to the hospital staff immediately. The idea of a digital personal assistant is tempting, but a healthcare chatbot goes a mile beyond that. From patient care to intelligent use of finances, its benefits are wide-ranging and make it a top priority in the Healthcare industry. Healthcare chatbots enable you to turn all these ideas into a reality by acting as AI-enabled digital assistants. It revolutionizes the quality of patient experience by attending to your patient’s needs instantly.

With the continuous progression of technology, we are likely to witness the emergence of increasingly innovative chatbots. These advancements will significantly shape and transform the future landscape of healthcare delivery. Even though most types of chatbots in healthcare do similar things, they have some differences we should talk about. The ability for chatbots to facilitate appointment scheduling and provide automated patient reminders can help ease the administrative burden and help to minimize the number of people who forget and do not show up for their appointments. 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.

Another chatbot that reduces the burden on clinicians and decreases wait time is Careskore (CareShore, Inc), which tracks vitals and anticipates the need for hospital admissions [42]. Chatbots have also been proposed to autonomize patient encounters through several advanced eHealth services. In addition to collecting data and providing bookings, Health OnLine Medical Suggestions or HOLMES (Wipro, Inc) interacts with patients to support diagnosis, choose the proper treatment pathway, and provide prevention check-ups [44].

Since it can access live data on the web, it can be used to personalize marketing materials and sales outreach. It also has a growing automation and workflow platform that makes creating new marketing and sales collateral easier when needed. Instead of building a general-purpose chatbot, they used revolutionary AI to help sales teams sell. It has all the integrations with CRMs that make it a meaningful addition to a sales toolset. It is also powered by its “Infobase,” which brings brand voice, personality, and workflow functionality to the chat. It offers quick actions to modify responses (shorten, sound more professional, etc.).

For example, IBM’s Watson for Oncology examines data from records and medical notes to generate an evidence-based treatment plan for oncologists [34]. Studies have shown that Watson for Oncology still cannot replace experts at this moment, as quite a few cases are not consistent with experts (approximately 73% concordant) [67,68]. Nonetheless, this could be an effective decision-making tool for cancer therapy to standardize treatments. Although not specifically an oncology app, another chatbot example for clinicians’ use is the chatbot Safedrugbot (Safe In Breastfeeding) [69]. This is a chat messaging service for health professionals offering assistance with appropriate drug use information during breastfeeding.

healthcare-chatbot

Artificial intelligence (AI) is at the forefront of transforming numerous aspects of our lives by modifying the way we analyze information and improving decision-making through problem solving, reasoning, and learning. Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches [1]. 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.

Consider diverse user preferences, language preferences, and accessibility needs. Implement multilingual support and inclusive design features, such as compatibility with assistive technologies. Iteratively refine the chatbot based on user feedback to address potential disparities in user experience. By embracing inclusivity in design and continuous refinement, healthcare chatbots become versatile and cater to diverse user demographics effectively. Medical chatbots are especially useful since they can answer questions that definitely should not be ignored, questions asked by anxious patients or their caregivers, but which do not need highly trained medical professionals to answer.

Details on the number of downloads and app across the 33 countries are available in Appendix 2. Only ten apps (12%) stated that they were HIPAA compliant, and three (4%) were Child Online Privacy and Protection Act (COPPA)-compliant. To test and evaluate the accuracy and completeness of GPT-4 as compared to GPT-3.5, researchers asked both systems 44 questions regarding melanoma and immunotherapy guidelines. The mean score for accuracy improved from 5.2 to 5.7, while the mean score for completeness improved from 2.6 to 2.8, as medians for both systems were 6.0 and 3.0, respectively. Of the 180 questions asked for GPT-3.5, 71 (39.4%) were completely accurate, and another 33 (18.3%) were nearly accurate.

Healthcare chatbots are AI-powered virtual assistants that provide personalized support to patients and healthcare providers. They are designed to simulate human-like conversation, enabling patients to interact with them as they would with a real person. These chatbots are trained on healthcare-related data and can respond to many patient inquiries, including appointment scheduling, prescription refills, and symptom checking.

In this article, we will explore how chatbots in healthcare can improve patient engagement and experience and streamline internal and external support. 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. For the output modality, or how the chatbot interacts with the user, all accessible apps had a text-based interface (98%), with five apps (6%) also allowing spoken/verbal output, and six apps (8%) supporting visual output. Visual output, in this case, included the use of an embodied avatar with modified expressions in response to user input. Eighty-two percent of apps had a specific task for the user to focus on (i.e., entering symptoms).

In fact, the survey findings reveal that more than 82 percent of people keep their messaging notifications on. Patients can naturally interact with the bot using text or voice to find medical services and providers, schedule an appointment, check their eligibility, and troubleshoot common issues using FAQ for fast and accurate resolution. The higher the intelligence of a chatbot, the more personal responses one can expect, and therefore, better customer assistance. Informative chatbots provide helpful information for users, often in the form of pop-ups, notifications, and breaking stories. After reading this blog, you will hopefully walk away with a solid understanding that chatbots and healthcare are a perfect match for each other. And there are many more chatbots in medicine developed today to transform patient care.

Most (21/32, 65%) of the included studies established that the chatbots were usable but with some differences in the user experience and that they can provide some positive support across the different health domains. The results show a substantial increase in the interest of chatbots in the past few years, shortly before the pandemic. Half (16/32, 50%) of the research evaluated chatbots applied to mental health or COVID-19. The studies suggest promise in the application of chatbots, especially to easily automated and repetitive tasks, but overall, the evidence for the efficacy of chatbots for prevention and intervention across all domains is limited at present. In conclusion, it is paramount that we remain steadfast in our ultimate goal of improving patient outcomes and quality of care in this digital frontier.

We examined the evidence for the development and use of chatbots in public health to assess the current state of the field, the application domains in which chatbot uptake is the most prolific, and the ways in which chatbots are being evaluated. Reviewing current evidence, we identified some of the gaps in current knowledge and possible next steps for the development and use of chatbots for public health provision. While AI chatbots can provide general recommendations, developing personalized treatment plans based on a patient’s unique circumstances, medical history, and preferences often requires the judgment and expertise of human healthcare providers. AI chatbots have been increasingly integrated into the healthcare system to streamline processes and improve patient care. While they can perform several tasks, there are limitations to their abilities, and they cannot replace human medical professionals in complex scenarios. Here, we discuss specific examples of tasks that AI chatbots can undertake and scenarios where human medical professionals are still required.

Service-provided classification is dependent on sentimental proximity to the user and the amount of intimate interaction dependent on the task performed. 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.