Healthcare Chatbots: Benefits, Future, Use Cases, Development

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. We identified 78 healthbot apps commercially available on the Google Play and Apple iOS stores.

My work is driven by a belief that as AI becomes an even more integral part of our world, it’s imperative to build systems that are transparent, trustworthy, and beneficial. I’m honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity. In this comprehensive guide, we‘ll explore six high-impact chatbot applications in healthcare, real-world examples, implementation best practices, evaluations of leading solutions, and predictions for the future.

Three of the apps were not fully assessed because their healthbots were non-functional. Studies that detailed any user-centered design methodology applied to the development of the chatbot were among the minority (3/32, 9%) [16-18]. About 14 percent of Earth’s people speak Mandarin, and Spanish and Hindi are used by about 8 percent each, making these the three most commonly spoken languages after English. Create user interfaces for the chatbot if you plan to use it as a distinctive application.

By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. The HIPAA Security Rule requires that you identify all the sources of PHI, including external sources, and all human, technical, and environmental threats to the safety of PHI in your company. The Rule requires that your company design a mechanism that encrypts all electronic PHI when necessary, both at rest or in transit over electronic communication tools such as the internet. Furthermore, the Security Rule allows flexibility in the type of encryption that covered entities may use. The Security Rule describes the physical safeguards as the physical measures, policies, and processes you have to protect a covered entity’s electronic PHI from security violations. Rasa is also available in Docker containers, so it is easy for you to integrate it into your infrastructure.

That’s why they’re often the chatbot of choice for mental health support or addiction rehabilitation services. Artificial Intelligence (AI) and automation have rapidly become popular in many industries, including healthcare. One of the most fascinating applications of AI and automation in healthcare is using chatbots. Chatbots in healthcare are computer programs designed to simulate conversation with human users, providing personalized assistance and support.

Of course, no algorithm can compare to the experience of a doctor that’s earned in the field or the level of care a trained nurse can provide. However, chatbot solutions for the healthcare industry can effectively complement the work of medical professionals, saving time and adding value where it really counts. Once again, answering these and many other questions concerning the backend of your software requires a certain level of expertise. Make sure you have access to professional healthcare chatbot development services and related IT outsourcing experts. Chatbots must be designed with the user in mind, providing patients a seamless and intuitive experience.

Concerns and limitations of chatbots in healthcare industry

Chatbots are software programs that use artificial intelligence and natural language processing to have personalized conversations with human users, either by text or voice. In healthcare, chatbots are being applied to automate conversations with patients for numerous uses – we‘ll cover the major ones shortly. Chatbots enable healthcare providers to collect this information seamlessly by asking relevant questions and recording patients’ responses. This automated approach eliminates the need for manual data entry, reducing errors and saving time for both patients and healthcare professionals. One of the primary use of chatbots in healthcare is their ability to assist in triaging patients at the hospital based on their symptoms, ensuring timely care.

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.

From patient care to intelligent use of finances, its benefits are wide-ranging and make it a top priority in the Healthcare industry. Most chatbots (we are not talking about AI-based ones) are rather simple and their main goal is to answer common questions. Hence, when a patient starts asking about a rare condition or names symptoms that a bot was not trained to recognize, it leads to frustration on both sides.

The Physician Compensation Report states that, on average, doctors have to dedicate 15.5 hours weekly to paperwork and administrative tasks. With this in mind, customized AI chatbots are becoming a necessity for today’s healthcare https://chat.openai.com/ businesses. The technology takes on the routine work, allowing physicians to focus more on severe medical cases. Healthcare providers can handle medical bills, insurance dealings, and claims automatically using AI-powered chatbots.

And since not everyone can receive sufficient help for their mental health, chatbots have become a truly invaluable asset. First, chatbots provide a high level of personalization due to the analysis of patient’s data. In this way, a bot suggests relevant recommendations and guidance and receive advice, tailored specifically to their needs and/or condition.

The ability of chatbots to ensure privacy is especially important, as vast amounts of personal and medical information are often collected without users being aware, including voice recognition and geographical tracking. The public’s lack of confidence is not surprising, given the increased frequency and magnitude of high-profile security breaches and inappropriate use of data [95]. Unlike financial data that becomes obsolete after being stolen, medical data are particularly valuable, as they are not perishable.

By collecting relevant information from users who may have been exposed to the virus, these bots assist in identifying potential hotspots and preventing further spread. Users can report their symptoms or any recent close contacts they may have had through the chatbot interface, enabling health authorities to take swift action. One of the key advantages of chatbots is their ability to offer up-to-date information about testing centers, vaccination sites, and updated pandemic guidelines. With the constantly evolving nature of the virus, having access to accurate and timely information is crucial. Chatbots can provide users with a list of nearby testing centers or vaccination sites based on their location, ensuring they have easy access to these important resources.

For example, CoachAI and Smart Wireless Interactive Health System used chatbot technology to track patients’ progress, provide insight to physicians, and suggest suitable activities [45,46]. Another app is Weight Mentor, which provides self-help motivation for weight loss maintenance and allows for open conversation without being affected by emotions [47]. Health Hero (Health Hero, Inc), Tasteful Bot (Facebook, Inc), Forksy (Facebook, Inc), and SLOWbot (iaso heath, Inc) guide users to make informed decisions on food choices to change unhealthy eating habits [48,49]. The effectiveness of these apps cannot be concluded, as a more rigorous analysis of the development, evaluation, and implementation is required. Nevertheless, chatbots are emerging as a solution for healthy lifestyle promotion through access and human-like communication while maintaining anonymity. A medical bot is created with the help of machine learning and large language models (LLMs).

Health-focused apps with chatbots (“healthbots”) have a critical role in addressing gaps in quality healthcare. There is limited evidence on how such healthbots are developed and applied in practice. Our review of healthbots aims to classify types of healthbots, contexts of use, and their natural language processing capabilities. Eligible apps were those that were health-related, had an embedded text-based conversational agent, available in English, and were available for free download through the Google Play or Apple iOS store. 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.

The second author then screened 50% of the same set of identified studies at random to validate the first author’s selection. If you think of a custom chatbot solution, you need one that is easy to use and understand. This can be anything from nearby facilities or pharmacies for prescription refills to their business hours. Gathering user feedback is essential to understand how well your chatbot is performing and whether it meets user demands.

Assess symptoms and suggest a diagnosis

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 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. 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).

• Moreover, integrating RPA or other automation solutions with chatbots allows for automating insurance claims processing and healthcare billing.
• By collecting relevant information from users who may have been exposed to the virus, these bots assist in identifying potential hotspots and preventing further spread.
• These are the tech measures, policies, and procedures that protect and control access to electronic health data.
• Through conversational interfaces, they create an environment where individuals feel comfortable sharing their thoughts, concerns, and suggestions.

Chatbots used for psychological support hold great potential, as individuals are more comfortable disclosing personal information when no judgments are formed, even if users could still discriminate their responses from that of humans [82,85]. From the patient’s perspective, various chatbots have been designed for symptom screening and self-diagnosis. The ability of patients to be directed to urgent referral pathways through early warning signs has been a promising market. Decreased wait times in accessing health care services have been found to correlate with improved patient outcomes and satisfaction [59-61]. The automated chatbot, Quro (Quro Medical, Inc), provides presynopsis based on symptoms and history to predict user conditions (average precision approximately 0.82) without a form-based data entry system [25]. In addition to diagnosis, Buoy Health (Buoy Health, Inc) assists users in identifying the cause of their illness and provides medical advice [26].

The prevalence of cancer is increasing along with the number of survivors of cancer, partly because of improved treatment techniques and early detection [77]. A number of these individuals require support after hospitalization or treatment periods. Maintaining autonomy and living in a self-sustaining way within their home environment is especially important for older populations [79].

How do we deal with all these issues when developing a clinical chatbot for healthcare? The CodeIT team has solutions to tackle the major text bot drawbacks, perfect for businesses like yours. Our developers can create any conversational agent you need because that’s what custom healthcare chatbot development is all about.

Gather patient data

Finally, human-aided classification incorporates human computation, which provides more flexibility and robustness but lacks the speed to accommodate more requests [17]. 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. As conversational AI continues advancing, measurable benefits like these will accelerate chatbot adoption exponentially. By thoughtfully implementing chatbots aligned to organizational goals, healthcare providers can elevate patient experiences and clinical outcomes to new heights.

The healthcare industry incorporates chatbots in its ecosystem to streamline communication between patients and healthcare professionals, prevent unnecessary expenses and offer a smooth, around-the-clock helping station. Considering these numbers, the cybersecurity issue is acute and goes far beyond securing chatbots. In order for a healthcare provider to properly safeguard its systems, they have to implement security on all levels of an organization. And we don’t need to mention how critical a data breach is, especially in the light of such regulations as HIPAA. Hence, every healthcare services provider needs to think about ways of strengthening their digital environment, including chatbots. By using this information, a medical organization can analyze the efficiency and quality of their services and identify areas for improvement.

Chatbots can improve the quality or experience of care by providing efficient, equitable, and personalized medical services. We can think of them as intermediaries between physicians for facilitating the history taking of sensitive and intimate information before consultations. They could also be thought of as decision aids that deliver regular feedback on disease progression and treatment reactions to help clinicians Chat PG better understand individual conditions. Preventative measures of cancer have become a priority worldwide, as early detection and treatment alone have not been effective in eliminating this disease [22]. Physical, psychological, and behavioral improvements of underserved or vulnerable populations may even be possible through chatbots, as they are so readily accessible through common messaging platforms.

By having a smart bot perform these tedious tasks, medical professionals have more time to focus on more critical issues, which ultimately results in better patient care. While a chatbot in healthcare can not be considered a 100% trusted and reliable medical consultant, it can at least help patients recognize their symptoms and the urgency of their condition or answer their questions. And the best part is that these actions do not require patients to schedule an appointment or stand in line, waiting for the doctor to respond. As for the doctors, the constant availability of bots means that doctors can better manage their time since the bots will undertake some of their responsibilities and tasks. As you can see, chatbots are on the rise and both patients and doctors recognize their value.

One study that stands out is the work of Bonnevie and colleagues [16], who describe the development of Layla, a trusted source of information in contraception and sexual health among a population at higher risk of unintended pregnancy. Layla was designed and developed through community-based participatory research, where the community that would benefit from the chatbot also had a say in its design. Such approaches also raise important questions about the production of knowledge, a concern that AI more broadly is undergoing a reckoning with [19]. This result is possibly an artifact of the maturity of the research that has been conducted in mental health on the use of chatbots and the massive surge in the use of chatbots to help combat COVID-19. The graph in Figure 2 thus reflects the maturity of research in the application domains and the presence of research in these domains rather than the quantity of studies that have been conducted. The overall worse performance in non-English languages may result from the way these models were trained, the study authors say.

A cross-sectional web-based survey of 100 practicing physicians gathered the perceptions of chatbots in health care [6]. Although a wide variety of beneficial aspects were reported (ie, management of health and administration), an equal number of concerns were present. If the limitations of chatbots are better understood and mitigated, the fears of adopting this technology in health care may slowly subside.

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. The majority (83%) had a fixed-input dialogue interaction method, indicating that the healthbot led the conversation flow. This was typically done by providing “button-push” options for user-indicated responses.

As well, doctors can gain a better understanding of patients and create a more personalized treatment plan for them, which will ultimately result in better patient care. And finally, all information will be added to a system and will be stored in an organized and centralized manner, thus helping clinics avoid data silos and facilitate admission and tracking of patients’ conditions. A distinctive feature of a chatbot technology in healthcare is its ability to immediately respond to a request, and this is another big benefit. In traditional patient care, a patient might have to wait for quite some time to get an answer to their question. With smart chatbots, not only the patient receives a reply within seconds, but exactly when the information is needed the most. And one more great thing about chatbots is that one bot can process multiple requests simultaneously, while a doctor cannot do so.

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. 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. 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.

By accessing a vast pool of medical resources, chatbots can provide users with comprehensive information on various health topics. 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. Patients can request prescription refills directly through the chatbot app, saving valuable time and effort for both themselves and healthcare providers. This continuous monitoring allows healthcare providers to detect any deviations from normal values promptly.

What are the top chatbot use cases in healthcare?

However, in other domains of use, concerns over the accuracy of AI symptom checkers [22] framed the relationships with chatbot interfaces. The trustworthiness and accuracy of information were factors in people abandoning consultations with diagnostic chatbots [28], and there is a recognized need for clinical supervision of the AI algorithms [9]. Studies were included if they used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact. Scientific American asked OpenAI and the creators of MedAlpaca for comment but did not receive a response by the time of this article’s publication. For example, when a chatbot suggests a suitable recommendation, it makes patients feel genuinely cared for. Customized chat technology helps patients avoid unnecessary lab tests or expensive treatments.

Yes, many healthcare chatbots can act as symptom checkers to facilitate self-diagnosis. Users usually prefer chatbots over symptom checker apps as they can precisely describe how they feel to a bot in the form of a simple conversation and get reliable and real-time results. After we’ve looked at the main benefits and types of healthcare chatbots, let’s use of chatbots in healthcare move on to the most common healthcare chatbot use cases. We will also provide real-life examples to support each use case, so you have a better understanding of how exactly the bots deliver expected results. These bots are used after the patient received a treatment or a service, and their main goal is to collect user feedback and patient data.

How to Decide Which Features Are Crucial In Your MVP App

We have yet to find a chatbot that incorporates deep learning to process large and complex data sets at a cellular level. Although not able to directly converse with users, DeepTarget [64] and deepMirGene [65] are capable of performing miRNA and target predictions using expression data with higher accuracy compared with non–deep learning models. With the advent of phenotype–genotype predictions, chatbots for genetic screening would greatly benefit from image recognition. New screening biomarkers are also being discovered at a rapid speed, so continual integration and algorithm training are required. These findings align with studies that demonstrate that chatbots have the potential to improve user experience and accessibility and provide accurate data collection [66].

• Only ten apps (12%) stated that they were HIPAA compliant, and three (4%) were Child Online Privacy and Protection Act (COPPA)-compliant.
• We were able to determine the dialogue management system and the dialogue interaction method of the healthbot for 92% of apps.
• The use of AI for symptom checking and triage at scale has now become the norm throughout much of the world, signaling a move away from human-centered health care [9] in a remarkably short period of time.

Seventy-four (53%) apps targeted patients with specific illnesses or diseases, sixty (43%) targeted patients’ caregivers or healthy individuals, and six (4%) targeted healthcare providers. The total sample size exceeded seventy-eight as some apps had multiple target populations. 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. The search was further limited using the Interactive Advertising Bureau (IAB) categories “Medical Health” and “Healthy Living”.

Acropolium provides healthcare bot development services for telemedicine, mental health support, or insurance processing. Skilled in mHealth app building, our engineers can utilize pre-designed building blocks or create custom medical chatbots from the ground up. Furthermore, social distancing and loss of loved ones have taken a toll on people’s mental health.

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. 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.

It can provide reliable and up-to-date information to patients as notifications or stories. While being seriously impacted by the COVID-19, the healthcare industry is steadily gaining traction in terms of its digital transformation and is adopting more and more innovative technologies on a regular basis. Chatbots, being among the most affordable solutions, have become valuable assets for healthcare organizations worldwide, and their value is recognized by both medical professionals and patients.

These health chatbots are better capable of addressing the patient’s concerns since they can answer specific questions. Healthcare chatbots help patients avoid unnecessary tests and costly treatments, guiding them through the system more effectively. Only limited by network connection and server performance, bots respond to requests instantaneously. And since chatbots are often based on SaaS (software as a service) packages from major players like AWS, there’s no shortage of resources.

There is a substantial lag between the production of academic knowledge on chatbot design and health impacts and the progression of the field. Studies on the use of chatbots for mental health, in particular anxiety and depression, also seem to show potential, with users reporting positive outcomes on at least some of the measurements taken [33,34,41]. The use of chatbots in healthcare helps improve the performance of medical staff by enabling automation. In the case of Tessa, a wellness chatbot provided harmful recommendations due to errors in the development stage and poor training data. Now, let’s explore the main applications of artificial intelligence chatbots in healthcare in more detail. This way, clinical chatbots help medical workers allocate more time to focus on patient care and more important tasks.

Despite limitations in access to smartphones and 3G connectivity, our review highlights the growing use of chatbot apps in low- and middle-income countries. Additionally, such bots also play an important role in providing counselling and social support to individuals who might suffer from conditions that may be stigmatized or have a shortage of skilled healthcare providers. Many of the apps reviewed were focused on mental health, as was seen in other reviews of health chatbots9,27,30,33.

Furthermore, Rasa also allows for encryption and safeguarding all data transition between its NLU engines and dialogue management engines to optimize data security. As you build your HIPAA-compliant chatbot, it will be essential to have 3rd parties audit your setup and advise where there could be vulnerabilities from their experience. 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. For example, if a chatbot is designed for users residing in the United States, a lookup table for “location” should contain all 50 states and the District of Columbia. In this article, we shall focus on the NLU component and how you can use Rasa NLU to build contextual chatbots.

Promising progress has also been made in using AI for radiotherapy to reduce the workload of radiation staff or identify at-risk patients by collecting outcomes before and after treatment [70]. An ideal chatbot for health care professionals’ use would be able to accurately detect diseases and provide the proper course of recommendations, which are functions currently limited by time and budgetary constraints. Continual algorithm training and updates would be necessary because of the constant improvements in current standards of care. Further refinements and testing for the accuracy of algorithms are required before clinical implementation [71].

With the growing spread of the disease, there comes a surge of misinformation and diverse conspiracy theories, which could potentially cause the pandemic curve to keep rising. Therefore, it has become necessary to leverage digital tools that disseminate authoritative healthcare information to people across the globe. We built the chatbot as a progressive web app, rendering on desktop and mobile, that interacts with users, helping them identify their mental state, and recommending appropriate content.

Although the use of NLP is a new territory in the health domain [47], it is a well-studied area in computer science and HCI. The majority (28/32, 88%) of the studies contained very little description of the technical implementation of the chatbot, which made it difficult to classify the chatbots from this perspective. In such cases, we marked the chatbot as using a combination of input methods (see Figure 5).