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

While healthbots have a potential role in the future of healthcare, our understanding of how they should be developed for different settings and applied in practice is limited. There has been one systematic review of commercially available apps; this review focused on features and content of healthbots that supported dementia patients and their caregivers34. To our knowledge, no review has been published examining the landscape of commercially available and consumer-facing healthbots across all health domains and characterized the NLP system design of such apps. This review aims to classify the types of healthbots available on the app store (Apple iOS and Google Play app stores), their contexts of use, as well as their NLP capabilities. In the context of remote patient monitoring, AI-driven chatbots excel at processing and interpreting the wealth of data garnered from wearable devices and smart home systems. Their applications span from predicting exacerbations in chronic conditions such as heart failure and diabetes to aiding in the early detection of infectious diseases like COVID-19 (10, 11).

We’re organizing a course in transformational leadership with the Crummer Graduate School of Business at Rollins College and Jim Snabe, chairman of Siemens and founder of IDONEA. From Feb. 26 to April 17, 2024, we’ll team up with our partners to offer an online executive education course that tackles all the challenges we focus on here at Impact Report. Generative AI can boost government efficiency and effectiveness when paired with other automation tools and human judgment.

Continuous Learning and Adaptation

In this regard, a conversation with an AI Assistant would efficiently substitute the initial phone call you might make to your doctor to discuss your concerns, before making an in-person appointment. At Haptik, we’ve already witnessed the success of this tech-driven conversational approach to raising public health awareness. We collaborated with the Government of India to develop the MyGov Corona Helpdesk – a WhatsApp chatbot to answer a wide range of queries about the COVID-19 pandemic, including symptoms and transmission, preventive measures, official government helplines, and more. Behind the scenes, though, much more drastic AI applications are brewing, and in no sector more so than health care.

Notably, chatbots like Woebot have emerged as valuable tools in the realm of mental health, engaging users in meaningful conversations and delivering cognitive behavioral therapy (CBT)-based interventions, as demonstrated by Alm and Nkomo (4). This progression underscores the transformative potential of chatbots, including modern iterations like ChatGPT, to transcend their initial role of providing information and actively participate in patient care. As these AI-driven conversational agents continue to evolve, their capacity to positively influence patient behavior and lifestyle choices becomes increasingly evident, reshaping the landscape of healthcare delivery and patient well-being.

Post-Treatment Care and Support

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. However, this ranged significantly, with usability, agent performance, and satisfaction having the most support across the studies, and cost-effectiveness receiving hardly any support. It should also be noted conversational ai in healthcare that the definitions of effectiveness were highly varied and, as evidenced by the methodological limitations identified in the quality assessment, rarely evaluated with the scrutiny expected for medical devices. Although the results reported are promising for the use of conversational agents in health care, there are a number of limitations in both the studies analyzed and the structure of this review that questions the validity of this finding.

While some healthcare organizations may choose to build out their own gen-AI capabilities or products, the majority will likely need to form strategic partnerships with technology firms. There may also be the potential for private payers and healthcare providers to partner with other organizations that also have rich data sets, to improve gen-AI outputs for everyone. Gen-AI-enabled technology could also streamline health insurance prior authorization and claims processing, two time-intensive and costly tasks for private payers. In summary, the benefits of Conversational AI in healthcare are numerous and diverse, playing a key role in improving patient engagement and transforming healthcare delivery. By leveraging the power of AI-powered chatbots healthcare providers can offer better patient care, further healthcare outcomes, improve operational efficiency, and save costs in the long run. In addition to these use cases, there’s growing interest in using conversational AI for mental health support, chronic disease management, and patient education.

Some of this similarity in results is likely because of the overlap in included studies; 7 of their 17 included studies were also included in our review [2]. 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? Second, how do users rate the usability and satisfactoriness of the conversational agents, and what specific elements of the agents do they like and dislike? Finally, what are the current limitations and gaps in the utility of conversational agents in health care?

• Most importantly, they will aim to shift resources towards preventative care in order to reduce the load on their staff so they can serve patients better.
• For the past fifteen years, his research has covered a wide range of topics in the realm of health plans, as well as hospital and health systems.
• Apps were assessed using an evaluation framework addressing chatbot characteristics and natural language processing features.
• 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.
• A total of 6 studies could not be classified as RCTs, cohort, qualitative, or cross-sectional studies, and their study design was coded as other [12,39,40,44,52,55].

His recent research has focused on the future of health, health equity, and health care financial transformation. Develop the chatbot’s capability to perform preliminary symptom analysis and triage. Provide users with insights into potential health issues, and guide them on whether they should seek immediate medical attention or follow homecare measures. Capture relevant information during interactions to tailor responses based on individual health needs. Personalized interactions contribute to a more engaging and effective user experience. Integrate advanced NLP algorithms to enable your chatbot to understand and respond to user queries in a human-like manner.

Integrating with Existing Systems

This is particularly relevant for patients seeking mental health support and explains why the uptake has been so impressive in this regard. Helpfulness, satisfaction, and ease of use were the common features in more than half of the included studies. Regarding diabetes–type 2 [28], a study reported the feedback from patients through various measures, such as competency (85%), helpfulness and friendliness (86%). On the other hand, some patients described the embodied CAs as annoying (39%) and boring (30%). Another study for diabetes [40] illustrated the user experience through attractiveness (0.74), perspicuity (0.67), and efficiency (0.77), by using the scale of Cronbach’s Alpha Coefficient correlation. In mental health, a study for treatment and education reported that some users felt the chatbot was hard to engage with and had no availability to ask questions [33].

The information also acts as a goldmine for valuable insights that healthcare service providers can utilise to improve the quality of care offered and the overall patient experience. Conversational AI, which includes chatbots and virtual assistants, can provide a host of benefits to healthcare organizations, from improving patient care to increasing productivity for clinics and hospitals. For those interested in implementing conversational AI, there are a few key considerations that need to be taken into account. Future reviews of conversational agents in health care could be extended to include constrained NLP and non-NLP conversational agents.

Potential uses of generative AI in healthcare

Dr. Dhar’s teams have developed powerful view of the Future of Health which explains how health will leverage disruptive technologies to transform the industry to make it consumer focused, personalized, preventative, equitable and sustainable. He has a deep passion for climate, sustainability and equity and is an executive sponsor for Deloitte’s Health Equity Institute. Dr. Dhar has a deep interest in cancer that goes well beyond his day to day business responsibilities at Deloitte. He is a board member of the American Cancer Society and works with numerous organizations to end cancer as we know it. Building an AI healthcare chatbot comes with challenges, including ethical considerations, potential biases in AI algorithms, and the need for ongoing maintenance and updates. Addressing these challenges is essential to ensure the responsible and effective deployment of healthcare chatbots.

These processes, while critical for ensuring safety and efficacy, can be time-consuming and resource-intensive. Consequently, addressing the issue of bias and ensuring fairness in healthcare AI chatbots necessitates a comprehensive approach. This includes being cognizant of the potential for bias in the data and the model development process, as well as actively implementing strategies to mitigate such bias (24).

Challenges to adoption remain

Prior systematic literature reviews explored a variety of CAs in general health care [1,6,24] and aspects of the personalization of health care chatbots using AI [25]. However, there is little evidence on the use of AI-based CAs in chronic disease health care. This paper aims to address the gap by reviewing different kinds of CAs used in health care for chronic conditions, different types of communication technology, evaluation measures of CAs, and AI methods used. In addition to data and conversation flow, organizations developing conversational AI chatbots should also focus on including desirable qualities, such as engagement and empathy, to create a more positive user experience. While conversational AI systems cannot replace human care, with the right qualities, they can augment the healthcare staff’s efforts by automating repetitive tasks and offering initial emotional support.

In fact, it may be virtually the only thing anyone talks about at events like Davos. At one dinner I moderated at Davos this month, which included executives from global tech, health care, and other firms, the group was so engaged we needed to call time to end the conversation. Generative AI has elicited buzz across various industries, and health care is no outlier. Many health care organizations see generative AI’s promise in achieving greater efficiency, effectiveness, and innovation from code to cure, and are planning to accelerate AI investments this year. Maulesh Shukla, Deloitte Services LP, is an executive manager with the Deloitte Center for Health Solutions. For the past fifteen years, his research has covered a wide range of topics in the realm of health plans, as well as hospital and health systems.

Our IVA makes patient experience efficient and effortless, helping brands improve customer satisfaction and NPS. Patients will feel understood and supported after interacting with Interactions IVA. Moreover, the rapidly evolving nature of AI chatbot technology and the lack of standardization in AI chatbot applications further complicate the process of regulatory assessment and oversight (31). While efforts are underway to adapt regulatory frameworks to the unique challenges posed by AI chatbots, this remains an area of ongoing complexity and challenge. Nonetheless, the problem of algorithmic bias is not solely restricted to the nature of the training data. One of these is biased feature selection, where selecting features used to train the model can lead to biased outcomes, particularly if these features correlate with sensitive attributes such as race or gender (21).

Real-Estate Chatbot RealFriend’s Pitch Deck for Its Seed Round

In a “time is money” industry, creating an MLS-ready image on Collov AI takes just seconds. With traditional methods, agents are still coordinating furniture rentals, photography sessions, and back-and-forth revisions with outsourced teams. Key values from documents like lease agreements and property condition assessments can be extracted and compared to find issues like mismatched values or different addresses listed on each document. Inconsistencies can be flagged for follow-up, helping reduce the risk of fraud or delays in the loan approval process.

Once shoppers choose their favorite features, HomLuv creates a profile and then matches shoppers with a growing network of home builders. “The defendants in this case unlawfully lined their pockets at the expense of New Jersey renters who struggled to pay the increasingly unlivable price levels imposed by this cartel,” Platkin said in a press release. New Jersey Attorney General Matthew Platkin filed his own lawsuit in April against 10 of New Jersey’s largest landlords and RealPage over the alleged use of software that forced “tens of thousands” New Jersey residents to overpay for rent. The ordinance comes on the heels of a similar bill proposed in fall 2024, which is currently stalled in the state Legislature, Gothamist reported. Rent prices in the city have increased by 50% since 2015, according to a 2024 report by the nonprofit Regional Plan Association. The city was the country’s third most expensive city to rent in April, according to Zumper, right after New York City and San Francisco.

• Although there are paid tools available, you can start for free using tools like ChatGPT.
• Would you like to see how the new furniture you love looks in a home you’re considering?
• It does not modify photos in ways that distort room dimensions or alter permanent fixtures.
• Real estate brokerages and online marketplaces devote careful scrutiny to the development and oversight of AI.

Potential Challenges For Industry Leaders

As data availability and transparency increase, AI’s profound impact on the commercial real estate landscape will empower industry participants to embrace data-driven approaches to enhance decision-making. Stakeholders who understand this monumental shift now can have a significant advantage compared with their peers. Matias Recchia is Co-Founder and CEO of Keyway, the commercial real estate technology platform designed for small and medium businesses. The app then schedules a rental showing, and then after the showing, asks for feedback. The user explains why they want to pass on it with quite specific details, prompting Luke to suggest another apartment that matches one of the user’s concerns. Those concerns are also logged into RealFriend’s backend database, so that the company doesn’t recommend this apartment to people with the same concerns.

Early experiments with chatbots and a bad experience apartment hunting led the two to make real estate their focus.

“Our mission is to help professionals and first-time investors build passive income and long-term wealth through real estate, regardless of immigration status or investing background.” With technology rapidly advancing, Zumper is the latest company in the real estate industry to dive into artificial intelligence (AI) assistants. The process continues to be dominated by stagers, photographers, marketing agencies, and human virtual staging providers—leaving sellers to shoulder the cost, time, and coordination. According to a recent survey of 750 CFOs at major real estate firms, only 14% of real estate companies are actively using AI. And for many professionals, “using AI” still means little more than drafting listing descriptions or social media copies with ChatGPT.

PropertyNest

Over the past few years, Zillow has incorporated AI into a number of its products, including home searches, tours, fair housing goals and a “natural language search” assistant. This user-friendly product has helped many agents—especially those new to AI—build confidence and interest in integrating technology into their daily workflow. The simplicity of the one-click design has even inspired some buyer agents to snap photos of empty properties during showings and present AI-staged versions to clients on the spot.

HomLuv focuses solely on new construction and lets buyers shop based on design features. “People are completely unprepared, even when they’re looking for a property to rent,” says Shin, who has worked as an agent at EXR and Compass, and was an editor at the personal finance site MyBankTracker.com. “My goal was to actually have that conversation and eliminate that education period so consumers are right away prepared.” “There are a lot of problems with going to websites and seeing the same property over again,” says Sean Muir, the company’s product designer. Enforcement of code violations includes the ability for residents to sue landlords or submit complaints to the city over the use of algorithmic rent-setting, according to Solomon.

How AI Is Changing The Real Estate Industry

But with any technology, especially newer ones, there is the risk of inaccuracies and misinformation. Industry leaders say there haven’t been major AI missteps, but they are being vigilant. That said, overall, AI is revolutionizing the real estate industry by improving operational efficiency, reducing errors and automating manual tasks. While the examples mentioned here are just the beginning, they demonstrate the immense potential of AI in real estate.

Are lender assistance programs like Zillow’s the future of home buying?

Would you like to see how the new furniture you love looks in a home you’re considering? There are AI apps for that, too, at popular retailers such as Wayfair, Target, and IKEA. Finally, the adoption of AI technology could inadvertently widen the digital divide, making it more difficult for entry-level analysts to navigate real estate transactions.

eXp team leader David Brooke on how to overcome industry myths

• In 2019, the chatbot spoke to almost 54,000 renters in a city where 50,000 apartments are rented a year.
• In New York, Klinger said they’re seeing lower prices and landlords offering many concessions to potential tenants, with landlords offering winter pricing during the peak summer months.
• The app then schedules a rental showing, and then after the showing, asks for feedback.

Luke’s engine, which combines information from many apartment listing sites, is able to give data about its affordability compared to the market as well. The company used the above chat log, which shows a user “introducing” Luke to their mom, as an example of the bot’s conversational skill, especially in relation to other rental search options online. That problem is what convinced long-time collaborators Omri Klinger and Hadar Landau to apply the AI-chatbot they had been building to the world of real-estate rentals in 2017 with the launch of RealFriend. Rental income has outpaced wage growth by 270% since 2010, making real estate one of the most resilient wealth-building opportunities. The average U.S. landlord earns $97,000 annually across just three properties.

The platform provides users with exclusive access to off-market rental properties, automated deal analysis, creative financing options, and expert coaching, addressing key challenges faced by first-time investors and visa holders. CovertNest is an AI-powered real estate investment platform helping W-2 employees, immigrants, and first-time investors build passive income through rental properties. The company combines exclusive property opportunities, automated analysis, and expert coaching to make real estate investing accessible for busy professionals.

The information allows landlords to boost profits by selecting the most financially advantageous rental prices, lease terms or occupancy levels for their buildings. The demand came from users who were reevaluating their current apartment after being locked-inside, as well as those looking for lower prices and potential concessions, said Klinger. Chatbots are also more widely accessible to users who aren’t as comfortable with tech and don’t want to learn a whole new app. Landau gave the example of an older customer who uses voice-to-text to communicate with Luke, something that would be significantly more challenging if Luke were an app.