Chatbots in Healthcare: Improving Patient Engagement and Experience

When it comes to warning the public about potentially harmful health care, the two most popular artificial intelligence chatbots clam up. One of the main assets of chatbots in healthcare is that they can focus on knowledge gathered from large datasets, such as scans. This varies from the traditional chatbot interactions with people in real-time, using probabilistic scenarios to give the best recommendations. Patients can be sure they will get accurate and up-to-date information from chatbots, which are essential for making informed healthcare decisions. By automating routine tasks such as scheduling appointments, reminding about medications, or doing routine diagnostics, chatbots can significantly reduce healthcare services costs.

This information can be obtained by asking the patient a few questions about where they travel, their occupation, and other relevant information. The healthcare chatbot can then alert the patient when it’s time to get vaccinated and flag important vaccinations to have when traveling to certain countries. From helping a patient manage a chronic condition better to helping patients who are visually or hearing impaired access critical information, chatbots are a revolutionary way of assisting patients efficiently and effectively. They can also be used to determine whether a certain situation is an emergency or not. This allows the patient to be taken care of fast and can be helpful during future doctor’s or nurse’s appointments.

Ideally, the difference between pre-intervention and post-intervention data for each group should be used in a meta-analysis [47]. However, we used only post-intervention data in each group for the meta-analysis because studies did not report enough data (eg, change in SD or SE of the mean between the pre-intervention and post-intervention for each group). In this review, it was possible to meta-analyze pre-intervention and post-intervention data from one-group trials (ie, did not include comparison groups). However, such analysis was not carried out in this review as such trials are very vulnerable to several threats of internal validity, such as maturation threat, instrumentation threat, regression threat, and history threat [41,48]. The first was an RCT conducted in Sweden [33], and the second was a quasiexperimental study conducted in China [37].

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. Regularly update the chatbot based on advancements in medical knowledge to enhance its efficiency.

In most cases, it is just a quick install, and once done, visitors can start interacting with them. Although a few platforms can be a little complex when compared to others, it isn’t hard to set them up. The flexible structure of chatbots makes them extremely easy to integrate with other platforms, increasing customer engagement in return. Hiring new executives (who can support customers throughout the year) and appending other basic things for them can turn out to be highly expensive for the company.

Unfortunately, the current clinical workforce is insufficient in meeting these needs. There
are approximately 9 psychiatrists per 100,000 people in developed countries1 and as few as 0.1 for every 1,000,0004 in lower-income countries. This inadequacy in meeting the present or future demand
for care has led to the proposal of technology as a solution. Particularly, there is a
growing interest surrounding chatbots, also known as conversational agents or multipurpose
virtual assistants. Design intuitive interfaces for seamless interactions, reducing the risk of frustration. Implement multi-modal interaction options, such as voice commands or graphical interfaces, to cater to diverse user preferences.

Patients appreciate that using a healthcare chatbot saves time and money, as they don’t have to commute all the way to the doctor’s clinic or the hospital. For instance, the startup Sense.ly provides a chatbot specifically focused on managing care plans for chronic disease patients. Studies show they can improve outcomes by 15-20% for chronic disease management programs. 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.

Instant Access to Critical Information

A single person can handle only 1-2 people simultaneously, and if this exceeds, the process becomes hard for an employee. In the future, we’re going to see more comprehensive chatbots solutions emerge on the market. An organization can use chatbots to send files to new hires whenever needed, automatically remind new hires to complete their forms, and automate many other tasks such as requests for vacation time, maternity leave, and others. An example of such a chatbot is Florence, a personal medical system designed for people who undergo long-term medical care. Users of the bot can get extra information about clinic locations and benefit from features such as health tracking, medication reminder, and statistics. The greatest advantage of chatbots here is that they can deal with many user inquiries at the same time, and the staff won’t be overwhelmed with the number of inquiries, no matter how high it gets.

Second, we consider how the implementation of chatbots amplifies the project of rationality and automation in professional work as well as changes in decision-making based on epistemic probability. We then discuss ethical and social issues relating to health chatbots from the perspective of professional ethics by considering professional-patient relations and the changing position of these stakeholders on health and medical assessments. We stress here that our intention is not to provide empirical evidence for or against chatbots in health care; it is to advance discussions of professional ethics in the context of novel technologies. Hesitancy from physicians and poor adoption by patients is a major barrier to overcome, which could be explained by many of the factors discussed in this section. 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.

The built-in chatbots on
most smartphones were incapable of responding to mental health problems such as suicidal
ideation beyond providing simple web search or helpline information. To seamlessly implement chatbots in healthcare systems, a phased approach is crucial. 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.

The crucial question that policy-makers are faced with is what kind of health services can be automated and translated into machine readable form. The primary role of healthcare chatbots is to streamline communication between patients and healthcare benefits of chatbots in healthcare providers. They serve as round-the-clock digital assistants, capable of handling a wide array of tasks – from answering common health queries and scheduling appointments to reminding patients about medication and providing tailored health advice.

The Many Hats of Chatbots in Healthcare: Types and Their Applications

Healthcare chatbots automate the information-gathering process while boosting patient engagement. 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.

Their results suggest that the primary factor driving patient response to COVID-19 screening hotlines (human or chatbot) were users’ perceptions of the agent’s ability (Dennis et al. 2020, p. 1730). A secondary factor in persuasiveness, satisfaction, likelihood of following the agent’s advice and likelihood of use was the type of agent, with participants reporting that they viewed chatbots more positively in comparison with human agents. One of the positive aspects is that healthcare organisations struggling to meet user demand for screening services can provide new patient services.

In the aftermath of COVID-19, Omaolo was updated to include ‘Coronavirus symptoms checker’, a service that ‘gives guidance regarding exposure to and symptoms of COVID-19’ (Atique et al. 2020, p. 2464; Tiirinki et al. 2020). In September 2020, the THL released the mobile contact tracing app Koronavilkku,1 which can collaborate with Omaolo by sharing information and informing the app of positive test cases (THL 2020, p. 14). In these ethical discussions, technology use is frequently ignored, technically automated mechanical functions are prioritised over human initiatives, or tools are treated as neutral partners in facilitating human cognitive efforts. So far, there has been scant discussion on how digitalisation, including chatbots, transform medical practices, especially in the context of human capabilities in exercising practical wisdom (Bontemps-Hommen et al. 2019).

Agreement between reviewers was very good, except for the assessment of the risk of bias (which was good). When possible, findings of the included studies were meta-analyzed; thereby, we were able to increase the power of the studies and improve the estimates of the likely size of effect of chatbots on a variety of mental health outcomes. Further studies are required to draw solid conclusions about the effectiveness and safety of chatbots.

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. This article contributes to the discussion on the ethical challenges posed by chatbots from the perspective of healthcare professional ethics. Chatbots experience the Black
Box problem, which is similar to many computing systems programmed using ML that are trained on massive data sets to produce multiple layers of connections. Although they are capable of solving complex problems that are unimaginable by humans, these systems remain highly opaque, and the resulting solutions may be unintuitive. This means that the systems’ behavior is hard to explain by merely looking inside, and understanding exactly how they are programmed is nearly impossible. For both users and developers, transparency becomes an issue, as they are not able to fully understand the solution or intervene to predictably change the chatbot’s behavior [97].

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. The best way to boost response times with chatbots is to start by coming up with playbooks that answer common questions and queries. Then, to take your experience to the next level, you can roll out AI chatbots so that you can accurately respond to a broader range of customer needs. The study observed 10 mental health chatbot apps and analyzed more than 6,000 reviews of the apps. San Francisco, California-based Woebot Health partners with both companies, providing employees with access to its chatbot (distinguished by a friendly-looking robot) and with health systems by dropping into existing clinical workflows. Limbic, a London, England-based company, has been trusted by NHS and has helped more than 250,000 NHS patients enter behavioral health care.

You can foun additiona information about ai customer service and artificial intelligence and NLP. With the rapidly increasing applications of chatbots in health care, this section will explore several areas of development and innovation in cancer care. Various examples of current chatbots provided below will illustrate their ability to tackle the triple aim of health care. The specific use case of chatbots in oncology with examples of actual products and proposed designs are outlined in Table 1.

Medical chatbot aid in efficient triage, evaluating symptom severity, directing patients to appropriate levels of care, and prioritizing urgent cases. Evolving into versatile educational instruments, chatbots deliver accurate and relevant health information to patients. This empowerment enables individuals to make well-informed decisions about their health, contributing to a more health-conscious society. The chatbot has undergone extensive testing and optimization and is now prepared for use. With real-time monitoring, problems can be quickly identified, user feedback can be analyzed, and changes can be made quickly to keep the health bot working effectively in a variety of healthcare scenarios. For example, Northwell Health recently launched a chatbot to reduce the number of no-shows for the colonoscopy procedure, which is critical for diagnosing colorectal cancer.

Scheduling

Dennis et al. (2020) examined ability, integrity and benevolence as potential factors driving trust in COVID-19 screening chatbots, subsequently influencing patients’ intentions to use chatbots and comply with their recommendations. They concluded that high-quality service provided by COVID-19 screening chatbots was critical but not sufficient for widespread adoption. The key was to emphasise the chatbot’s ability and assure users that it delivers the same quality of service as human agents (Dennis et al. 2020, p. 1727).

As advancements in AI are ever evolving and ameliorating, chatbots will inevitably perform a range of complex activities and become an indispensable part of many industries, mainly, healthcare. Chatbots are made on AI technology and are programmed to access vast healthcare data to run diagnostics and check patients’ symptoms. It can provide reliable and up-to-date information to patients as notifications or stories. According to an MGMA Stat poll, about 49% of medical groups said that the rates of ‘no-shows‘ soared since 2021.

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 businesses. The technology takes on the routine work, allowing physicians to focus more on severe medical cases.

Scheduling appointments and reminders

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. Another point to consider is whether your medical AI chatbot will be integrated with existing software systems and applications like EHR, telemedicine platforms, etc. 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. The Health Insurance and Portability and Accountability Act (HIPAA) of 1996 is United States regulation that sets the standards for using, handling, and storing sensitive healthcare data.

Not only do these responses defeat the purpose of the conversation, but they also make the conversation one-sided and unnatural.

Our Experience in Healthcare Chatbot Development

They can also assist doctors by giving them a medical history and other important details about a patient with a medical emergency. Our results indicate that the risk of harm from the use of chatbots is extremely low, with
a total adverse event incidence of 1 in 759 recruited participants. The single adverse
event, reported in Bickmore et al.,21 involved a participant who developed paranoia and withdrew from study. Another
participant in the same study almost withdrew due to concerns of personal data theft until
reorientation and counsel from the nurse on-call, suggesting a possible benefit to available
clinician support. A few studies examined the effectiveness of conversational agents in the diagnosis and
treatment of psychiatric disorders.

This research was internally funded and received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. This safeguard includes designating people, either by job title or job description, who are authorized to access this data, as well as electronic access control systems, video monitoring, and door locks restricting access to the data. 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. That sums up our module on training a conversational model for classifying intent and extracting entities using Rasa NLU.

This constant availability not only enhances patient engagement but also significantly reduces the workload on healthcare professionals. By automating responses to repetitive questions and routine administrative tasks, healthcare chatbots free up valuable time for healthcare staff, allowing them to focus more on critical care and patient interaction. Chatbots, also known as conversational agents, interactive agents, virtual agents, virtual humans, or virtual assistants, are artificial intelligence programs designed to simulate human conversation via text or speech. 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.

Regularly update security protocols to align with evolving regulations and standards. Conduct regular audits to identify and patch vulnerabilities, ensuring the chatbot’s adherence to legal requirements. Proactively monitor regulation changes and update the chatbot accordingly to avoid legal challenges for clients.

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. Conversational chatbots can be trained on large datasets, including the symptoms, mode of transmission, natural course, prognostic factors, and treatment of the coronavirus infection.

• Healthcare providers can overcome this challenge by investing in a dedicated team to manage bots and ensure they are up-to-date with the latest healthcare information.
• This psychiatric counseling chatbot was effective in engaging users and reducing anxiety in young adults after cancer treatment [40].
• HCPs and patients lack trust in the ability of chatbots, which may lead to concerns about their clinical care risks, accountability and an increase in the clinical workload rather than a reduction.
• The chatbot enables healthcare providers to receive the amount due for the treatment they offer to their patients.
• Implementation of chatbots may address some of these concerns, such as reducing the burden on the health care system and supporting independent living.

Chatbots in healthcare can walk the patient through the specified procedures, give information about what to expect, and send notifications about the need for contact with the doctor. They also remember conversations and can personalise answers to the nature of the patient’s medical history. Multi-Lingual SupportOne of the biggest benefits of chatbots is they can be programmed to support multiple languages. It allows you to give a personalized customer experience, by allowing them to converse in the language they are most comfortable with. Whether you have an international customer base, or your target audience group prefers native language support, the right vendor can help you meet customer expectations in their native language. Fitzpatrick et al.,18 Gardiner et al.,19 and Bickmore et al.22 highlight the effect of establishing appropriate rapport or therapeutic alliance on
patient interactions.

While much remains unknown about chatbots, it is clear that some patients are already
willing to engage and interact with them today. Unlike in-person visits to a clinician,
where patient privacy and confidentiality are both assumed and protected,25 chatbots today often do not offer users such benefits. For example, in the United
States, most chatbots are not currently covered under the Health Insurance and Portability
and Accountability Act (HIPAA), meaning users’ data may be sold, traded, and marketed by
companies owning the chatbot. As most chatbots are connected to the Internet and sometimes
even social media, users may unknowingly be offering a large amount of personal
information in exchange for use. Informed decision making around chatbot remains as
nascent as the evidence for their effectiveness. As previously mentioned, it is also
important to consider the potential relationships that may be formed with chatbots.