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

Despite AI’s promising future in healthcare, adoption of the technology will still come down to patient experience and — more important — patient preference. These influencers and health IT leaders are change-makers, paving the way toward health equity and transforming healthcare’s approach to data. Chatbots were effective in decreasing the severity of acrophobia according to one RCT. The effect size of chatbots on acrophobia in this RCT [38] was substantially higher than the total effect size of therapist-assisted exposure treatment on phobias reported by a meta-analysis (2.0 versus 1.1) [46]. This indicates that chatbots may be equivalent to, if not better, exposure treatment delivered by a therapist in treating phobias. The influence of using chatbots on psychological distress was examined by 2 studies, conducted in Japan and Australia [35,36].

Thus, new technologies require system-level assessment of their effects in the design and implementation phase. Through chatbots (and their technical functions), we can have only a very limited view of medical knowledge. The ‘rigid’ and formal systems of chatbots, even with the ML bend, are locked in certain a priori models of calculation. Expertise generally requires the intersubjective circulation of knowledge, that is, a pool of dynamic knowledge and intersubjective criticism of data, knowledge and processes (e.g. Prior 2003; Collins and Evans 2007).

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. We acknowledge the difficulty in identifying the nature of systemic change and looking at its complex network-like structure in the functioning of health organisations. Nonetheless, we consider it important to raise this point when talking about chatbots and their potential breakthrough in health care. We suggest that new ethico-political approaches are required in professional ethics because chatbots can become entangled with clinical practices in complex ways. It is difficult to assess the legitimacy of particular applications and their underlying business interests using concepts drawn from universal AI ethics or traditional professional ethics inherited from bioethics.

Healthcare Chatbots: Benefits, Use Cases, and Top Tools

First, patients required more interaction with healthcare organizations at the height of the COVID-19 pandemic. Therefore, it is essential to ensure that the chatbot solution protects sensitive consumer data, encrypts messages, and securely transmits identifiable patient information to other secure systems (e.g., electronic health record software). The goals you set now will establish the very essence of your new product and the technology on which your artificial intelligence healthcare chatbot system or project will be based. Health chatbots can quickly offer this information to patients, including information about nearby medical facilities, hours of operation, and nearby pharmacies where prescription drugs can be filled. They can also be programmed to answer questions about a particular condition, such as a health problem or a medical procedure. Medical (social) chatbots can interact with patients who are prone to anxiety, depression and loneliness, allowing them to share their emotional issues without fear of being judged, and providing good advice as well as simple company.

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. The use of chatbots in health care presents a novel set of moral and ethical challenges that must be addressed for the public to fully embrace this technology.

How Does AI in a Chatbot for Healthcare Work?

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. Also, it’s required to maintain the infrastructure to ensure the large language model has the necessary amount of computing power to process user requests.

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. However, healthcare data is often stored in disparate systems that are not integrated. Healthcare providers can overcome this challenge by investing in data integration technologies that allow chatbots to access patient data in real-time. 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.

Through this, the system can extract the intended meaning and generate appropriate responses. When a patient does require human intervention, watsonx Assistant uses intelligent human agent handoff capabilities to ensure patients are accurately routed to the right medical professional. With watsonx Assistant, patients arrive at that human interaction with the relevant patient data necessary to facilitate rapid resolution. That means patients get what they need faster and more effectively, without the inefficiency of long wait times and incorrect call routing. Watsonx Assistant is the key to improving the customer experience with automated self-service answers and actions.

A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. 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.

Similarly, a graph-based chatbot has been proposed to identify the mood of users through sentimental analysis and provide human-like responses to comfort patients [84]. Vivobot (HopeLab, Inc) provides cognitive and behavioral interventions to deliver positive psychology skills and promote well-being. This psychiatric counseling chatbot was effective in engaging users and reducing anxiety in young adults after cancer treatment [40]. The limitation to the abovementioned studies chatbot in healthcare was that most participants were young adults, most likely because of the platform on which the chatbots were available. In addition, longer follow-up periods with larger and more diverse sample sizes are needed for future studies. 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].

• There are countless cases where a digital personal assistant or chatbot can help doctors, patients, or their families.
• Healthcare chatbots significantly cut unnecessary spending by allowing patients to perform minor treatments or procedures without visiting the doctor.
• We found that services that used this digital solution identified substantially increased referrals (15% increase versus 6% increase in control services).

The severity of anxiety was measured using the Generalized Anxiety Disorder scale [28,29] and Overall Anxiety Severity and Impairment Scale [32]. While 2 studies were RCTs [28,29], the third study was a pretest-posttest quasiexperiment [32]. Half of the included studies (6/12) examined the effect of using chatbots on the severity of depression [27-32].

Chatbot in UAE: Top 4 Vendors & Use Cases in 2024

Given chatbots’ diverse applications in numerous aspects of health care, further research and interdisciplinary collaboration to advance this technology could revolutionize the practice of medicine. Many health professionals and experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace health professional assessments (Palanica et al. 2019). Although some applications can provide assistance in terms of real-time information on prognosis and treatment effectiveness in some areas of health care, health experts have been concerned about patient safety (McGreevey et al. 2020).

The world witnessed its first psychotherapist chatbot in 1966 when Joseph Weizenbaum created ELIZA, a natural language processing program. It used pattern matching and substitution methodology to give responses, but limited communication abilities led to its downfall. Most patients prefer to book appointments online instead of making phone calls or sending messages. A chatbot further eases the process by allowing patients to know available slots and schedule or delete meetings at a glance. It also increases revenue as the reduction in the consultation periods and hospital waiting lines leads healthcare institutions to take in and manage more patients. Healthcare Chatbot is an AI-powered software that uses machine learning algorithms or computer programs to interact with leads in auditory or textual modes.

Improve patient satisfaction

Chatbots can reply to scheduling questions and send meeting and referral reminders (usually via text message or SMS) to help limit no-shows. A medical chatbot is a software program developed to engage in a conversation with a user through text or voice to provide real-time assistance. This technology allows healthcare companies to deliver client service without compelling additional resources (like human staff).

The COVID-19 pandemic has accelerated the digitization of healthcare services, making this technology more relevant than ever before. In simple terms, conversational AI is a category of AI-driven solutions that automate human-like conversations with users. It utilizes techniques like natural language processing and machine learning to tap into their learnings and deliver clear answers to varied questions in a conversational tone. Sophisticated AI-based chatbots require a great deal of human resources, for instance, experts of data analytics, whose work also needs to be publicly funded. More simple solutions can lead to new costs and workload when the usage of new technology creates unexpected problems in practice.

• While many patients appreciate the help of a human assistant, many others prefer to hold their information private.
• This way, appointment-scheduling chatbots in the healthcare industry streamline communication and scheduling processes.
• Despite the obvious pros of using healthcare chatbots, they also have major drawbacks.
• Chatbots can facilitate interactions with those who are reluctant to seek mental health advice due to stigmatization [5] and allow more conversational flexibility [16].

In combination with wearable technology and affordable software, chatbots have great potential to affect patient monitoring solutions. 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]. Added life expectancy poses new challenges for both patients and the health care team. For example, many patients now require extended at-home support and monitoring, whereas health care workers deal with an increased workload.

There are risks involved when patients are expected to self-diagnose, such as a misdiagnosis provided by the chatbot or patients potentially lacking an understanding of the diagnosis. If experts lean on the false ideals of chatbot capability, this can also lead to patient overconfidence and, furthermore, ethical problems. Chatbots can provide insurance services and healthcare resources to patients and insurance plan members. Moreover, integrating RPA or other automation solutions with chatbots allows for automating insurance claims processing and healthcare billing. Today, chatbots offer diagnosis of symptoms, mental healthcare consultation, nutrition facts and tracking, and more. For example, in 2020 WhatsApp collaborated with the World Health Organization (WHO) to make a chatbot service that answers users’ questions on COVID-19.

Instances of chatbots providing false or misleading information pose significant risks to users’ health. Chatbots in the healthcare industry provide support by recommending coping strategies for various mental health problems. Such an interactive AI technology can automate various healthcare-related activities. A medical bot is created with the help of machine learning and large language models (LLMs).

Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot

Medical chatbots respond to prompts and data shared by users about their health to offer relevant information, guidance, and advice. As healthcare systems grapple with staffing shortages and overburdened resources, medical chatbots could offer a digital lifeline. They can automate bothersome and time-consuming tasks, like appointment scheduling or consultation. An AI chatbot can be integrated with third-party software, enabling them to deliver proper functionality.

Although

clinicians’ knowledge base in the use of scientific evidence to guide decision-making has expanded, there are still many other facets to the quality of care that has yet to catch up. Key areas of focus are safety, effectiveness, timeliness, efficiency, equitability, and patient-centered care [20]. Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. There were 47 (31%) apps that were developed for a primary care domain area and 22 (14%) for a mental health domain.

How Is Zero Trust Working in Healthcare Today?

A complete system also requires a ‘back-up system’ or practices that imply increased costs and the emergence of new problems. The crucial question that policy-makers are faced with is what kind of health services can be automated and translated into machine readable form. Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence clinical practices and expert–client relationships. We suggest the need for new approaches in professional ethics as the large-scale deployment of artificial intelligence may revolutionise professional decision-making and client–expert interaction in healthcare organisations. We argue that the implementation of chatbots amplifies the project of rationality and automation in clinical practice and alters traditional decision-making practices based on epistemic probability and prudence.

Chatbots and other types of automation in health care are a top priority for Northwell Health. CEO of Conversa Health Murray Brozinsky suggests automated support will become increasingly necessary in the future of health care. Chatbots became more popular in healthcare out of necessity — there needed to be more healthcare workers to meet increased patient demand and expectations. Although chatbots are extremely helpful for many organizations, they cannot replace a real doctor’s visit or emergency procedure.

Results of Studies

Better organization of patient routes, drug management, emergency or first aid, and offering simpler solutions to medical problems are all possible situations in which chatbots can intervene and ease the burden on medical professionals. These chatbots employ artificial intelligence (AI) to quickly determine intent and context, engage in more complex and detailed conversations, and create the feeling of talking to a real person. The best part of AI chatbots is that they have self-learning models, which means there is no need for frequent training. Developers can create algorithmic models combined with linguistic processing to provide intelligent and complex conversational solutions. As an important component of proactive healthcare services, chatbots are already used in hospitals, pharmacies, laboratories, and even care facilities. The ubiquitous use of smartphones, IoT, telehealth, and other related technologies fosters the market’s expansion.

AI text bots helped detect and guide high-risk individuals toward self-isolation. The technology helped the University Hospitals system used by healthcare providers to screen 29,000 employees for COVID-19 symptoms daily. This enabled swift response to potential cases and eased the burden on clinicians. Following Pasquale (2020), we can divide the use of algorithmic systems, such as chatbots, into two strands.

Improve patient satisfaction

There were only six (8%) apps that utilized a theoretical or therapeutic framework underpinning their approach, including Cognitive Behavioral Therapy (CBT)43, Dialectic Behavioral Therapy (DBT)44, and Stages of Change/Transtheoretical Model45. With state and local restrictions, people remained inside their homes more frequently to reduce the risk of spreading the coronavirus. However, the onset of the pandemic made more people reflect on their health, so doctor appointments started filling up fast.

• From generic tips to research-backed cures, Healthily gives patients control over improving their health while sitting at home.
• In this interview, we speak with Jack O'Meara, co-founder and CEO of Ochre Bio, a pioneering force in liver disease treatment.
• Healthcare organizations struggle to find workers and manage unfilled positions in hospitals and other facilities.
• Further, in order to ensure the responsible and effective use of the novel and still-developing technology, ethical concerns and data privacy must be thoroughly addressed.
• These chatbots are data-driven, meaning they learn from patterns, conversations, and previous experiences to improve the quality of their responses.
• This lack of resources is especially evident in low-income and middle-income countries where there are 0.1 psychiatrists per 1,000,000 people [7], compared to 90 psychiatrists per 1,000,000 people in high-income countries [8].

These findings align with studies that demonstrate that chatbots have the potential to improve user experience and accessibility and provide accurate data collection [66]. Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes.

Healthcare Chatbots Overview: Role of AI, Benefits, Examples

Inequality in treatment access is a pressing issue in most healthcare systems across many medical disciplines. In mental healthcare, reduced treatment access for minorities is ubiquitous but remedies are sparse. Here we demonstrate that digital tools can reduce the accessibility gap by addressing several key barriers. We found that services that used this digital solution identified substantially increased referrals (15% increase versus 6% increase in control services). Critically, this increase was particularly pronounced in minorities, such as nonbinary (179% increase) and ethnic minority individuals (29% increase). This provides strong evidence that digital tools may help overcome the pervasive inequality in mental healthcare.

While numerous studies have assessed the effectiveness and safety of using chatbots in mental health, no reviews have pooled the results of those studies. Healthcare chatbots are not only reasonable solutions for your patients but your doctors as well. Imagine how many more patients you can connect with if you save time and effort by automating responses to repetitive questions of patients and basic activities like appointment scheduling or providing health facts. Like falling dominoes, the large-scale deployment of chatbots can push HCPs and patients into novel forms of healthcare delivery, which can affect patients’ access to care and drive some to new provider options. Due to partly automated systems, patient frustration can reach boiling point when patients feel that they must first communicate with chatbots before they can schedule an appointment.

Latest Articles

A study performed on Woebot, developed based on cognitive behavioral therapy, showed that depressive symptoms were significantly reduced, and participants were more receptive than in traditional therapies [41]. This agreed with the Shim results, also using the same type of therapy, which showed that the intervention was highly engaging, improved well-being, and reduced stress [82]. When another chatbot was developed based on the structured association technique counseling method, the user’s motivation was enhanced, and stress was reduced [83].

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 chatbot in healthcare and assistance. When using chatbots in healthcare, it is essential to ensure that patients understand how their data will be used and are allowed to opt out if they choose. Sensely’s Molly is another example of a healthcare chatbot that acts as a personal assistant.

DISADVANTAGES OF CHATBOTS IN HEALTHCARE

In practice, ‘chatbot expertise’ has to do with, for example, giving a correct answer (provision of accurate and relevant information). The importance of providing correct answers has been found in previous studies (Nordheim et al. 2019, p. 25), which have ‘identified the perceived ability of software agents as a strong predictor of trust’. Conversely, automation errors have a negative effect on trust—‘more so than do similar errors from human experts’ (p. 25). However, the details of experiencing chatbots and their expertise as trustworthy are a complex matter.

While 1 study was conducted in the United States [30], the other study was conducted in the United Kingdom [38]. The former study concluded that the chatbot was safe because users did not report any harm, distress, adverse events, or worsening of depressive symptoms resulting from using the chatbot during the study [30]. Similarly, Freeman et al [38] concluded that the chatbot was safe because no serious adverse events (eg, suicide attempts, death, serious violent incidents) or discomfort caused by the chatbot were reported. When there was a statistically significant difference between groups, we assessed how this difference was clinically important. A minimal clinically important difference refers to the smallest change in a measured outcome that a patient would deem as worthy and significant and which mandates a change in a patient’s treatment [25]. Boundaries of a minimal clinically important difference for each outcome were calculated as ±0.5 times the SD of the control arms of the studies at baseline.

This system also informs the user of the composition and prescribed use of medications to help select the best course of action. The diagnosis and course of treatment for cancer are complex, so a more realistic system would be a chatbot used to connect users with appropriate specialists or resources. 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.

Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot

Medical chatbots respond to prompts and data shared by users about their health to offer relevant information, guidance, and advice. As healthcare systems grapple with staffing shortages and overburdened resources, medical chatbots could offer a digital lifeline. They can automate bothersome and time-consuming tasks, like appointment scheduling or consultation. An AI chatbot can be integrated with third-party software, enabling them to deliver proper functionality.

Although clinicians’ knowledge base in the use of scientific evidence to guide decision-making has expanded, there are still many other facets to the quality of care that has yet to catch up. Key areas of focus are safety, effectiveness, timeliness, efficiency, equitability, and patient-centered care [20]. Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. There were 47 (31%) apps that were developed for a primary care domain area and 22 (14%) for a mental health domain.

How Is Zero Trust Working in Healthcare Today?

A complete system also requires a ‘back-up system’ or practices that imply increased costs and the emergence of new problems. The crucial question that policy-makers are faced with is what kind of health services can be automated and translated into machine readable form. Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence clinical practices and expert–client relationships. We suggest the need for new approaches in professional ethics as the large-scale deployment of artificial intelligence may revolutionise professional decision-making and client–expert interaction in healthcare organisations. We argue that the implementation of chatbots amplifies the project of rationality and automation in clinical practice and alters traditional decision-making practices based on epistemic probability and prudence.

Chatbots and other types of automation in health care are a top priority for Northwell Health. CEO of Conversa Health Murray Brozinsky suggests automated support will become increasingly necessary in the future of health care. Chatbots became more popular in healthcare out of necessity — there needed to be more healthcare workers to meet increased patient demand and expectations. Although chatbots are extremely helpful for many organizations, they cannot replace a real doctor’s visit or emergency procedure.

Results of Studies

Better organization of patient routes, drug management, emergency or first aid, and offering simpler solutions to medical problems are all possible situations in which chatbots can intervene and ease the burden on medical professionals. These chatbots employ artificial intelligence (AI) to quickly determine intent and context, engage in more complex and detailed conversations, and create the feeling of talking to a real person. The best part of AI chatbots is that they have self-learning models, which means there is no need for frequent training. Developers can create algorithmic models combined with linguistic processing to provide intelligent and complex conversational solutions. As an important component of proactive healthcare services, chatbots are already used in hospitals, pharmacies, laboratories, and even care facilities. The ubiquitous use of smartphones, IoT, telehealth, and other related technologies fosters the market’s expansion.

AI text bots helped detect and guide high-risk individuals toward self-isolation. The technology helped the University Hospitals system used by healthcare providers to screen 29,000 employees for COVID-19 symptoms daily. This enabled swift response to potential cases and eased the burden on clinicians. Following Pasquale (2020), we can divide the use of algorithmic systems, such as chatbots, into two strands.

Improve patient satisfaction

There were only six (8%) apps that utilized a theoretical or therapeutic framework underpinning their approach, including Cognitive Behavioral Therapy (CBT)43, Dialectic Behavioral Therapy (DBT)44, and Stages of Change/Transtheoretical Model45. With state and local restrictions, people remained inside their homes more frequently to reduce the risk of spreading the coronavirus. However, the onset of the pandemic made more people reflect on their health, so doctor appointments started filling up fast.

• From generic tips to research-backed cures, Healthily gives patients control over improving their health while sitting at home.
• In this interview, we speak with Jack O’Meara, co-founder and CEO of Ochre Bio, a pioneering force in liver disease treatment.
• Healthcare organizations struggle to find workers and manage unfilled positions in hospitals and other facilities.
• Further, in order to ensure the responsible and effective use of the novel and still-developing technology, ethical concerns and data privacy must be thoroughly addressed.
• These chatbots are data-driven, meaning they learn from patterns, conversations, and previous experiences to improve the quality of their responses.
• This lack of resources is especially evident in low-income and middle-income countries where there are 0.1 psychiatrists per 1,000,000 people [7], compared to 90 psychiatrists per 1,000,000 people in high-income countries [8].

These findings align with studies that demonstrate that chatbots have the potential to improve user experience and accessibility and provide accurate data collection [66]. Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes.

Healthcare Chatbots Overview: Role of AI, Benefits, Examples

Inequality in treatment access is a pressing issue in most healthcare systems across many medical disciplines. In mental healthcare, reduced treatment access for minorities is ubiquitous but remedies are sparse. Here we demonstrate that digital tools can reduce the accessibility gap by addressing several key barriers. We found that services that used this digital solution identified substantially increased referrals (15% increase versus 6% increase in control services). Critically, this increase was particularly pronounced in minorities, such as nonbinary (179% increase) and ethnic minority individuals (29% increase). This provides strong evidence that digital tools may help overcome the pervasive inequality in mental healthcare.

While numerous studies have assessed the effectiveness and safety of using chatbots in mental health, no reviews have pooled the results of those studies. Healthcare chatbots are not only reasonable solutions for your patients but your doctors as well. Imagine how many more patients you can connect with if you save time and effort by automating responses to repetitive questions of patients and basic activities like appointment scheduling or providing health facts. Like falling dominoes, the large-scale deployment of chatbots can push HCPs and patients into novel forms of healthcare delivery, which can affect patients’ access to care and drive some to new provider options. Due to partly automated systems, patient frustration can reach boiling point when patients feel that they must first communicate with chatbots before they can schedule an appointment.

Latest Articles

A study performed on Woebot, developed based on cognitive behavioral therapy, showed that depressive symptoms were significantly reduced, and participants were more receptive than in traditional therapies [41]. This agreed with the Shim results, also using the same type of therapy, which showed that the intervention was highly engaging, improved well-being, and reduced stress [82]. When another chatbot was developed based on the structured association technique counseling method, the user’s motivation was enhanced, and stress was reduced [83].

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 chatbot in healthcare and assistance. When using chatbots in healthcare, it is essential to ensure that patients understand how their data will be used and are allowed to opt out if they choose. Sensely’s Molly is another example of a healthcare chatbot that acts as a personal assistant.

DISADVANTAGES OF CHATBOTS IN HEALTHCARE

In practice, ‘chatbot expertise’ has to do with, for example, giving a correct answer (provision of accurate and relevant information). The importance of providing correct answers has been found in previous studies (Nordheim et al. 2019, p. 25), which have ‘identified the perceived ability of software agents as a strong predictor of trust’. Conversely, automation errors have a negative effect on trust—‘more so than do similar errors from human experts’ (p. 25). However, the details of experiencing chatbots and their expertise as trustworthy are a complex matter.

While 1 study was conducted in the United States [30], the other study was conducted in the United Kingdom [38]. The former study concluded that the chatbot was safe because users did not report any harm, distress, adverse events, or worsening of depressive symptoms resulting from using the chatbot during the study [30]. Similarly, Freeman et al [38] concluded that the chatbot was safe because no serious adverse events (eg, suicide attempts, death, serious violent incidents) or discomfort caused by the chatbot were reported. When there was a statistically significant difference between groups, we assessed how this difference was clinically important. A minimal clinically important difference refers to the smallest change in a measured outcome that a patient would deem as worthy and significant and which mandates a change in a patient’s treatment [25]. Boundaries of a minimal clinically important difference for each outcome were calculated as ±0.5 times the SD of the control arms of the studies at baseline.

This system also informs the user of the composition and prescribed use of medications to help select the best course of action. The diagnosis and course of treatment for cancer are complex, so a more realistic system would be a chatbot used to connect users with appropriate specialists or resources. 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.