How Conversational AI Is Transforming Healthcare

This is a challenging task as humans have developed languages over thousands of years to communicate information and ideas. NLP algorithms work to convert human language into a form that machines can comprehend, involving processes like converting text into binary vectors and creating a matrix representation of sentences. Through this, the system https://chat.openai.com/ can extract the intended meaning and generate appropriate responses. Summary of evaluation outcomes by the area of health care addressed by the conversational agenta. Notably, Conversational AI is significantly enhancing the high quality of communication between physicians and patients, and it’s also paving the way for remote patient treatment.

This limitation in the use of the framework for this review also highlights a limitation in many of these studies, namely, that they do not think about whole system implementation from the early stages of agent design, development, and testing. It is possible that the lack of evaluation of the implications of the agents for health care provision and resources was because of an emphasis on technology development and evaluation, rather than system integration. They must also be properly evaluated with a large sample of users, rather than be simply presented as unsubstantiated claims that the agent will reduce costs and save health care providers’ time. Eight studies noted the effectiveness of conversational agents for mental health applications [57,61,64,67,75,80,81,84].

In 12 studies, there was a mixture of self-reported and objectively assessed outcomes and outcomes were not reported in the two ongoing trials (Multimedia Appendix 4). There was an increase in the number of publications each year, from 3 in 2015 to 5 in 2016, 10 in 2017, and 23 in 2018. Some author groups were highly productive and published at least two papers within 2 years. Kowatsch et al published 3 papers between 2017 and 2018 based on their open source behavioral intervention platform MobileCoach, which allows the authors to design a text-based health care conversational agent for obesity management and behavior change [30,46,90]. Griol et al published articles on conversational agent for chronic conditions, including chronic pulmonary disease [63] and Alzheimer disease [62] in 2015 and 2016, respectively. Such productive teams reiterate the research interest in this area of conversational agents.

With careful planning, prudent vendor selection, and phased deployment focused on the highest impact areas, healthcare organizations can overcome these hurdles and realize significant value from conversational AI adoption. This technology presents an enormous opportunity to improve workflows, access, satisfaction, and care quality once implemented thoughtfully. For instance, AI-driven chatbots can provide 24/7 support, answering queries and offering medical advice. An example is the Ada Health app, which assists users in understanding their symptoms and guides them towards appropriate care. However, for this vision to become a reality, successful integration and widespread adoption of these AI-powered systems will necessitate collaborative efforts from various stakeholders. Key players such as healthcare providers, technology vendors and regulatory authorities must come together to facilitate the seamless implementation of conversational AI in the healthcare ecosystem.

AI chatbots can be integrated into existing healthcare systems through APIs (Application Programming Interfaces), SDKs (Software Development Kits), or custom development. For example, the conversational AI system records numerous instances of patients attempting to schedule appointments with podiatrists but failing to do so within a reasonable timeline. A study of the data would reveal this reoccurring pattern, and the healthcare organization may then determine that they may need to hire more podiatrists to meet patient demand.

This AI-driven guidance ensures consistent and clear instructions, reducing post-treatment complications and patient anxieties. While AI is transformative, human touch remains invaluable, especially in sensitive areas like healthcare. By analyzing patient language and sentiments during interactions, it can gauge a patient’s emotional state. With an increasing emphasis on patient-centric care, Conversational AI acts as a pivotal touchpoint between healthcare professionals and their patients.

This data includes patient symptoms, medical history, severity of conditions, and other relevant health indicators. By processing this information, the AI system can effectively prioritize patients based on urgency. These examples demonstrate the breadth of AI applications in healthcare, from predictive analytics in patient care to advancements in medical imaging and personalized medicine. The successful integration of AI with existing healthcare systems is paving the way for more efficient, accurate, and personalized patient care.

Data Availability Statement

Regular training for staff in handling and protecting patient data in the context of AI systems is also crucial. AI systems must incorporate robust cybersecurity measures to protect against data breaches and unauthorized access. This includes using advanced encryption methods for data at rest and in transit, multi-factor authentication for system access, and regular security audits and updates to guard against emerging cyber threats. For instance, AI platforms like those used in telemedicine should have end-to-end encryption to ensure that patient-provider communications are secure and private.

User-identified problems will need to be addressed if conversational agents are to have a significant impact on health care, because their impact depends on people being willing to use them and preferring to use them over alternatives. The information gathered in this review identifies the current issues with conversational agents that need to be overcome and can be used to help determine which elements of the agents are most likely to be successful and useful in various aspects of health care. As conversational agents are often touted as having the potential to reduce the burden on health care resources, evaluations of the implications of the agents for improved health care provision and reduced resource demand also need to be assessed. The most commonly used method in the included studies was quasi-experimental, which was used in almost half of the included papers. This is aligned with the findings of the previous systematic reviews of CAs in healthcare [1,27].

For instance, ecosystem stakeholders’ traditionally slow approach to adopting new technologies restricts access to training data, making it difficult to get the NLP and ML-driven systems up and running. On top of it, many even struggle with the preparation of this data and setting up dialog flow to make the conversation flow seamlessly. This can be addressed by integrating with electronic medical records and other healthcare systems and adopting tools like dbt. However, to achieve transformative results, the key lies in perfecting underlying technologies, starting natural language processing. It is a branch of AI that enables machines to analyze and understand human language data.

Considerations for building a conversational AI strategy for healthcare organizations

Very few studies in this review even discussed the cost analysis of the agent in questions, let alone provide substantive evidence about its cost-effectiveness. The evaluation of costs and outcomes of new technologies and their privacy, security, and interoperability will be necessary to advance value-based health care [60]. However, there is very little evidence to suggest that the conversational agents examined in this review considered or addressed these concerns. User feedback on 2 of the studies even noted that better interoperability between the agent and EHRs or health care providers would improve its usefulness. The characteristics of the 31 included studies are summarized in Multimedia Appendix 3 [8,9,12-15,32-56].

Although the black box effect appears to be an unavoidable consequence of the use of AI, there is some emerging research on making AI transparent and explainable [11]. However, at the moment, its use may affect the safety and accuracy of treatment and should be carefully monitored and evaluated when used in health care [9]. In this systematic review, we examined 31 studies that evaluated the effectiveness and usability of conversational agents in health care. Overall, studies reported a moderate amount of evidence supporting the effectiveness, usability, and positive user perceptions of the agents. On average, two-thirds of the studies (67%) reported positive or mixed evidence for each evaluation outcome.

The conclusions drawn in this paper were made by the authors and are not necessarily supported by the University of Oxford. The funding body had no role in the design, execution, or analysis of this systematic review. All studies retrieved from the databases were stored in the reference management software EndNote (version X9, Clarivate Analytics), which automatically eliminated duplicates. Due to time constraints, the EndNote search function was used to extract relevant studies before the screening of the citations against the inclusion and exclusion criteria by 2 independent reviewers. Where duplicates or publications from the same study were identified, the more recent publication or the one with the most detail was selected for inclusion in the review.

Twenty-six articles were considered eligible for inclusion in the systematic literature review (Figure 1). This is a paradigm shift that would be particularly useful when human resources are spread thin during a healthcare crisis. It fosters Chat PG a data-driven culture in healthcare that empowers both care providers and patients to make informed decisions. At Haptik, we’ve already witnessed the success of this tech-driven conversational approach to raising public health awareness.

Virtual assistants can even connect Net Promoter Scores (NPS) to user interactions to garner feedback that can be used to enhance customer experiences further. Learn how AI knowledge bases enhance knowledge management by enabling continuous learning, personalized support, and knowledge discovery. Otto enables leaders to share critical, time-sensitive updates right within Teams conversations. The bot also measures message engagement, handles individual follow-ups, and reports back insights. Their AI-driven tools help in analyzing medical images more accurately and quickly, aiding radiologists in diagnosing diseases such as cancer with greater precision.

In fact, the majority of today’s chatbots give straightforward replies to a specific set of questions using scripted, pre-defined responses and rule-based programming. Conversational AI-driven chatbots and virtual assistants are two remarkable applications of AI in the medical field that bring numerous benefits for both consumers and providers. Following such changes, healthcare consumers (patients and others) demand better, more relevant information that’s available quickly and in a format they can easily consume. The healthcare industry is burdened with a staff shortage, particularly in the post-COVID era, and thus struggles to provide quality care and information. Conversational AI in healthcare offers a user-friendly, automated means of sharing important information with consumers at a low cost and scale. According to Accenture, AI in healthcare can save the U.S. healthcare economy a whopping $150 billion annually by 2026.

Post-Treatment Care and Support

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”. The IAB develops industry standards to support categorization in the digital advertising industry; 42Matters labeled apps using these standards40. You can foun additiona information about ai customer service and artificial intelligence and NLP. Relevant apps on the iOS Apple store were identified; then, the Google Play store was searched with the exclusion of any apps that were also available on iOS, to eliminate duplicates.

Because it reduces many of the common issues of FAQ sections on healthcare providers’ websites, conversational AI is the best solution for self-service in healthcare. Users may struggle to identify the most appropriate response to their query using the website search tool, for example, since they aren’t using the same vocabulary as the FAQ. Alternatively, they may have a number of queries that need them to navigate to various sites. Creating a conversational AI application such as a chatbot or voice bot with the right Conversational AI platform is easy. Gupshup’s bot-builder platform is ideal for healthcare institutions and providers looking to leverage the power of Artificial Intelligence and Machine Learning to deliver better and more timely care that improves the health and lives of their patients. AI chatbots and virtual assistants are pivotal in remote and underserved areas, offering basic medical advice and emergency guidance.

Conversational Agent Evaluation

With correct implementation, conversation AI systems can have an enormous impact on the healthcare industry. If you are wondering about the potential of this technology and how it can save the beleaguered healthcare economy, this complete guide to conversation AI for the healthcare industry is meant for you. Conversational AI in healthcare communication channels must be carefully selected for successful execution. Ideal channels are ones that patients easily access and integrate seamlessly with existing systems. Voice assistants, bots, and messaging platforms are some of the most often used choices for meeting the demands of various patients.

Today’s consumers are taking a more active and authoritative role in their healthcare journeys. This is especially true of highly developed healthcare markets such as the U.S., where federal mandates like the new CMS rules proposed in December 2020 improve patient access to health information and thus empower them to make better decisions about their health. This blog for conversational AI in healthcare explores why Conversational AI is such an exciting new development in the healthcare industry. The key to meeting these goals is technology, specifically conversational AI in healthcare. Recent research revealed that 50-70% of call center activity is related to health benefits.

Using insights from Moveworks, the CIO better understands where employees are still struggling, allowing him to proactively improve their experience, whether streamlining workflows or providing new training. With Moveworks for Employee Communications, Vituity sends targeted announcements to specific groups based on location, role, and preferences. By processing thousands of detailed images, the AI can detect subtle patterns and indicators of eye diseases such as diabetic retinopathy and age-related macular degeneration, which are among the leading causes of blindness. APositive or mixed results have been coded as 1, and neutral or negative results as 0. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Conversational AI, by rule-based programming, can automate the often tedious task of appointment management, ushering in a new era of efficiency. An intelligent Conversational AI platform can swiftly schedule, reschedule, or cancel appointments, drastically reducing manual input and potential human errors. Although the studies reported accuracy, efficacy, effectiveness, and acceptability as outcomes, there were no measurements of cost, efficiency, or how the solution led to improved productivity when used instead of or to augment the work of a health professional. Therefore, it was not possible to ascertain whether the solutions developed were cost-effective compared with alternative approaches. Personality codes derived for the conversational agents included in this review, adapted from Haan et al. We condensed the descriptive terms used in individual studies to present the conversational agents into a list of 9 relevant personality traits as presented in Table 1.

In the second round of screening, 48 apps were removed as they lacked a chatbot feature and 103 apps were also excluded, as they were not available for full download, required a medical records number or institutional login, or required payment to use. The five aforementioned examples highlight how healthcare providers can leverage Conversational AI as a powerful tool for information dissemination and customer care automation. But we’ve barely started to grasp the true transformative impact of this technology on the healthcare sector. An AI Assistant can answer common queries and FAQs related to a particular disease, health condition or epidemic. It can raise awareness about a specific health-related concern or crisis by offering swift access to accurate, reliable and timely information.

Database Search

Conversational agents have been developed for many different aspects of the health sector to support health care professionals and the general public. Specific uses include screening for health conditions, triage, counseling, at-home health management support, and training for health care professionals [8,13-15]. With phone, mobile, and online platforms being widely accessible, conversational agents can support populations with limited access to health care or poor health literacy [16,17]. They also have the potential to be affordably scaled up to reach large proportions of a population [3].

Another conversational agent for well-being improvement procured positive feedback from participants who thought it was an interesting experience, pretty quick, and fun [88]. 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.

Due to the number of conversational agents in development and/or those that did not progress to the evaluation stages of development, studies that were solely descriptive were excluded. Furthermore, because of the pace at which conversational agents have developed over recent decades, studies were limited to those published during or after 2008. In 2008, the first iPhone was released, and it marks an increase in the prevalence and capabilities of digital technology.

Conversational AI in healthcare provides deeper analysis and intent recognition, allowing it to assist patients beyond contextual or grammatical errors. Conversational AI does not require patients to match specific “keywords” in order to receive a comprehensive answer or consultation. NLP enables the model to comprehend the text rather than simply scanning for a few words to get a response. This technology will soon become an indispensable tool for delivering modern healthcare. With this in mind, let’s look at some of the top use cases for conversational AI in healthcare. In summary, the impact of AI in drug discovery and research, as exemplified by Atomwise, is profound.

A study after cancer treatment clarified that the users found the chatbot nonjudgmental and helpful. Two studies examined the acceptability of conversational agents for health care service delivery [48,87]. Outcomes were reported qualitatively, including comments on ease of use, humanity of the chatbot, and users’ comfort with the input functionalities available to them as well as criticisms on technical difficulties [48]. Bickmore et al [87] more specifically compared conversational assistants Siri, Alexa, and Google Assistant on their provision of health information and found satisfaction to be lowest with Alexa and highest with Siri. Overall, there was a neutral rating for satisfaction, with a median score of 4 (IQR 1-6) [87].

The variety of specific feedback reports demonstrates the importance of examining usability for individual conversational agents and tailoring the design to the intended population. Although there were some preferences and complaints that were frequently reported, much of the feedback was agent dependent. Several studies reported user feedback that was specific to that conversational agent. This included a preference for telephone IVR over web-based pediatric care guidance [9] and a simple avatar with a computer-generated voice over a more life-like agent with a recorded voice [42]. In contrast, others found it more difficult to know how to respond so the agent would understand [14]. Recently, AI-based CAs have demonstrated multiple benefits in many domains, especially in healthcare.

Personalization was defined based on whether the healthbot app as a whole has tailored its content, interface, and functionality to users, including individual user-based or user category-based accommodations. Furthermore, methods of data collection for content personalization were evaluated41. Personalization features were only identified in 47 apps (60%), of which all required information drawn from users’ active participation. Forty-three of these (90%) apps personalized the content, and five (10%) personalized the user interface of the app. Examples of individuated content include the healthbot asking for the user’s name and addressing them by their name; or the healthbot asking for the user’s health condition and providing information pertinent to their health status. In addition to the content, some apps allowed for customization of the user interface by allowing the user to pick their preferred background color and image.

• Following such changes, healthcare consumers (patients and others) demand better, more relevant information that’s available quickly and in a format they can easily consume.
• By reducing wait times, the Cleveland Clinic’s AI system indirectly contributes to improved patient outcomes.
• For each app, data on the number of downloads were abstracted for five countries with the highest numbers of downloads over the previous 30 days.
• For example, medical staff members have to search for countless patient forms and switch between applications, resulting in loss of time and frustration.
• First, are the conversational agents investigated effective at achieving their intended health-related outcomes, and does the effectiveness vary depending on the type of agent?

Similarly, in Europe, AI systems in healthcare must comply with the General Data Protection Regulation (GDPR), which imposes strict guidelines on data privacy and consent. Cerner, another major player in healthcare IT, has been incorporating AI into its electronic health record (EHR) systems. By using predictive analytics, their AI tools help in identifying patients at risk of deteriorating health conditions, thereby enabling early intervention. Summary of the studies based on the evaluation outcomes from the synthesis framework for the assessment of health information technology differentiating between positive and mixed outcomes. In conclusion, Conversational AI is an emerging technology that has the potential to transform the healthcare industry.

Traditionally, drug discovery involves a lengthy process of screening thousands of chemical compounds, which can be both time-consuming and costly. The AI can virtually screen millions of compounds in a fraction of the time it would take in a laboratory, rapidly narrowing down the list to the most promising candidates for further development. This means obtaining explicit consent from patients before their data is used and allowing them control over their information. Patients should have the right to access, correct, or delete their data from AI systems. AI systems must comply with regulations like HIPAA in the United States, ensuring that patient information is handled with utmost care.

Four apps utilized AI generation, indicating that the user could write two to three sentences to the healthbot and receive a potentially relevant response. Healthbots are computer programs that mimic conversation with users using text or spoken language9. The advent of such technology has created a novel way to improve person-centered healthcare.

While many organizations in the healthcare domain are bullish on the potential of conversational AI, its widespread adoption still remains hurdled by multiple challenges. The study designs also varied widely, with 29% (9/31) using cross-sectional designs, 26% (8/31) using RCTs, 23% (7/31) using qualitative methods, 19% (6/31) using cohort studies, and 1 using a cluster crossover design. The full data extraction table is available in Multimedia Appendix 4 [8,9,12-15,32-56]. Data were extracted by 1 reviewer, and key data points from the studies, specified in the protocol and identified on further study of the publications, were recorded in a spreadsheet and validated by a second reviewer. The data extraction form was based on the minimum requirements recommended by the Cochrane Handbook for Systematic Reviews [27]. Summary of the quality assessment and judgments of the ‘other’ studies using the Appraisal tool for Cross-Sectional Studies tool.

There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness. We identified 78 healthbot apps commercially available on the Google Play and Apple iOS stores. Healthbot apps are being used across 33 countries, including some locations with more limited penetration of smartphones and 3G connectivity. The healthbots serve a range of functions including the provision of health education, assessment of symptoms, and assistance with tasks such as scheduling.

But considering that each such interaction typically costs $5-$15 (sometimes more) and also results in long wait times and unhappy customers, healthcare organizations have a serious problem on their hands. Healthcare organizations like hospitals and clinics deal with a high volume of inquiries and requests from staff on a daily basis. These can range from administrative questions to issues with IT systems to guidance needed for patient care. During times of crisis like COVID-19, the flood of questions and the need for support skyrockets. This results in overwhelmed help desks and staff wasting time toggling between different systems or tracking down information.

The inclusion of only studies published in English is also likely to exclude relevant research on conversational agents conducted in other countries. These limitations should be addressed in future studies to ensure that the full body of relevant literature is examined. Suggestions such as this, that conversational agents have the potential to improve health care provision, save health care providers’ time, and reduce costs, were frequently made in the studies. However, as demonstrated above, very few studies quantified these claims and even fewer measured these outcomes with objective measures. Although many were in the early stages of testing, claims about the potential value to the health care system in terms of time or money should be substantiated. However, as evidenced by the number of neutral or negative coding in the evaluation, many of the studies did not consider whole system implementation outcomes.

The objective of this systematic review was to synthesize evidence of conversational agents’ usability, effectiveness, and satisfaction in health care. Although the studies generally reported positive outcomes relating to the agents’ usability and effectiveness, the quality of the evidence was not sufficient to provide strong evidence to support these claims. This study extended the literature by expanding its summary to examine a whole system set of evaluation outcomes, including cost-effectiveness, privacy, and security, which have not been systematically examined in previous reviews. In addition, it provides a distinct contribution by conducting a thematic analysis of the qualitative user perceptions of the agents. Further research is needed to examine the cost-effectiveness and value of these agents in health care, both in their current and potential states.

True conversational AI has the flexibility and intelligence to respond appropriately to an infinite array of possible conversational scenarios. Chatbots like Tess offer psychological support, demonstrating how AI can supplement traditional mental health services. These AI tools provide coping mechanisms and emotional support, making mental health care more accessible. As AI technology continues to evolve, its potential to revolutionize various aspects of healthcare — from patient triage to complex diagnoses — becomes increasingly evident.

Particularly in the healthcare industry that is ripe with so many use cases of AI, there is significant headroom for growth. AI has the potential to predict disease outcomes and health issues before they occur by analyzing large volumes of data, including medical histories, lifestyle information, and genetic data. However, the number of languages and the quality of understanding and translation can vary depending on the specific AI technology being used. To successfully adopt conversational AI in the healthcare industry, there are several key factors to be considered.

AI systems in healthcare are designed to learn continuously from interactions and feedback, thereby improving their accuracy and effectiveness. IBM Watson Health is a prime example, evolving through constant learning to assist in diagnosis and treatment planning. In the United States, compliance with the Health Insurance Portability and Accountability conversational ai in healthcare Act (HIPAA) is paramount for any AI application handling patient data. HIPAA sets the standard for protecting sensitive patient data, and AI systems must adhere to these regulations. This includes ensuring that data is encrypted, access is controlled and monitored, and that there are clear protocols for data breach notification.

Simple conversational agents are rule based, meaning that they depend on prewritten keywords and commands programmed by the developer. The user is therefore restricted to predetermined options when answering questions posed by the conversational agents, and there is little or no opportunity for free responses. If a user enters a question or sentence without a single keyword, the conversational agents will be unable to understand the input and will respond with a default message such as “Sorry, I did not understand” [2]. Despite these restrictions, simple conversational agents are increasingly used in executing tasks such as booking appointments, purchasing merchandise, ordering food, and sharing information without the need for human involvement [2].

However, most studies did not provide sufficient information on the implementation details. In order to identify the AI methods, a list of common words (Appendix B) used for building AI CAs [1,6,27] were employed. Several papers reported that AI methods could improve the user’s interaction with the system [1,2,5,6,27]. Half of the included papers utilized speech recognition in many CAs (e.g., chatbot, ECA, or relational agent). Although having speech recognition can capture speech much faster than typing, it could lead to difficulties with some keywords because of misinterpretation of words.