"I am chatbot, your virtual mental health adviser." What drives citizens' satisfaction and continuance intention toward mental health chatbots during the COVID-19 pandemic? An empirical study in China

A systematic review of AI-based chatbot usages in healthcare services

PURPOSE

This systematic literature review aims to provide a comprehensive and structured synthesis of the existing knowledge about chatbots in healthcare from both a theoretical and methodological perspective.

DESIGN/METHODOLOGY/APPROACH

To this end, a systematic literature review was conducted with 89 articles selected through a SPAR-4-SLR systematic procedure. The document for this systematic review was collected from Scopus database. The VoSviewer software facilitates the analysis of keyword co-occurrence to form the fundamental structure of the subject field.

FINDINGS

In addition, this study proposes a future research agenda revolving around three main themes such as (1) telemedicine, (2) mental health and (3) medical information.

ORIGINALITY/VALUE

This study underscores the significance, implications and predictors of chatbot usage in healthcare services. It is concluded that adopting the proposed future direction and further research on chatbots in healthcare will help to refine chatbot systems to better meet the needs of patients.

Transforming healthcare with chatbots: Uses and applications-A scoping review

Purpose

The COVID-19 pandemic has intensified the demand and use of healthcare resources, prompting the search for efficient solutions under budgetary constraints. In this context, the increasing use of artificial intelligence and telemedicine has emerged as a key strategy to optimize healthcare delivery and resources. Consequently, chatbots have emerged as innovative tools in various healthcare fields, such as mental health and patient monitoring, offering therapeutic conversations and early interventions. This systematic review aims to explore the current state of chatbots in the healthcare sector, meticulously evaluating their effectiveness, practical applications, and potential benefits.

Methods

This systematic review was conducted following PRISMA guidelines, utilizing three databases, including PubMed, Web of Science, and Scopus, to identify relevant studies on the use and cost of chatbots in health over the past 5 years.

Results

Several articles were identified through the database search (n = 31). The chatbot interventions were categorized by similar types. The reviewed articles highlight the diverse applications of chatbot interventions in healthcare, including mental health support, medical information, appointment management, health education, lifestyle changes, and COVID-19 management, demonstrating significant potential across these areas.

Conclusion

Furthermore, there are challenges regarding the implementation of chatbots, compatibility with other systems, and ethical considerations that may arise in different healthcare settings. Addressing these issues will be essential to maximize the benefits of chatbots, mitigate risks, and ensure equitable access to these health innovations.

A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study

BACKGROUND

Artificial intelligence (AI)-based chatbots have emerged as potential tools to assist individuals in reducing anxiety and supporting well-being.

OBJECTIVE

This study aimed to identify the factors that impact individuals' intention to engage and their engagement behavior with AI-based well-being chatbots by using a novel research model to enhance service levels, thereby improving user experience and mental health intervention effectiveness.

METHODS

We conducted a web-based questionnaire survey of adult users of well-being chatbots in China via social media. Our survey collected demographic data, as well as a range of measures to assess relevant theoretical factors. Finally, 256 valid responses were obtained. The newly applied model was validated through the partial least squares structural equation modeling approach.

RESULTS

The model explained 62.8% (R2) of the variance in intention to engage and 74% (R2) of the variance in engagement behavior. Affect (β=.201; P=.002), social factors (β=.184; P=.007), and compatibility (β=.149; P=.03) were statistically significant for the intention to engage. Habit (β=.154; P=.01), trust (β=.253; P<.001), and intention to engage (β=.464; P<.001) were statistically significant for engagement behavior.

CONCLUSIONS

The new extended model provides a theoretical basis for studying users' AI-based chatbot engagement behavior. This study highlights practical points for developers of AI-based well-being chatbots. It also highlights the importance of AI-based well-being chatbots to create an emotional connection with the users.

Student loan debt and mental health: a comprehensive review of scholarly literature from 1900 to 2019

PURPOSE

The review had two purposes. The first was to examine the nature and extent of published literature on student loan and the second was to systematically review the literature on student loans and mental health.

MATERIALS AND METHODS

Data from academic databases (1900-2019) were analyzed using two methods. First, topic modeling (a text-mining tool that utilized Bayesian statistics to extract hidden patterns in large volumes of texts) was used to understand the topical coverage in peer-reviewed abstracts (n = 988) on student debt. Second, using PRISMA guidelines, 46 manuscripts were systematically reviewed to synthesize literature linking student debt and mental health.

RESULTS

A model with 10 topics was selected for parsimony and more accurate clustered representation of the patterns. Certain topics have received less attention, including mental health and wellbeing. In the systematic review, themes derived were categorized into two life trajectories: before and during repayment. Whereas stress, anxiety, and depression dominated the literature, the review demonstrated that the consequences of student loans extend beyond mental health and negatively affect a person's wellbeing. Self-efficacy emerged as a potential solution.

DISCUSSION AND CONCLUSION

Across countries and samples, the results are uniform and show that student loan burdens certain vulnerable groups more. Findings indicate diversity in mental health measures has resulted into a lack of a unified theoretical framework. Better scales and consensus on commonly used terms will strengthen the literature. Some areas, such as impact of student loans on graduate students or consumers repaying their loans, warrant attention in future research.

Effectiveness of a Web-based and Mobile Therapy Chatbot on Anxiety and Depressive Symptoms in Subclinical Young Adults: Randomized Controlled Trial

BACKGROUND

There has been an increased need to provide specialized help for people with depressive and anxiety symptoms, particularly teenagers and young adults. There is evidence from a 2-week intervention that chatbots (eg, Woebot) are effective in reducing depression and anxiety, an effect that was not detected in the control group that was provided self-help materials. Although chatbots are a promising solution, there is limited scientific evidence for the efficacy of agent-guided cognitive behavioral therapy (CBT) outside the English language, especially for highly inflected languages.

OBJECTIVE

This study aimed to measure the efficacy of Fido, a therapy chatbot that uses the Polish language. It targets depressive and anxiety symptoms using CBT techniques. We hypothesized that participants using Fido would show a greater reduction in anxiety and depressive symptoms than the control group.

METHODS

We conducted a 2-arm, open-label, randomized controlled trial with 81 participants with subclinical depression or anxiety who were recruited via social media. Participants were divided into experimental (interacted with a fully automated Fido chatbot) and control (received a self-help book) groups. Both intervention methods addressed topics such as general psychoeducation and cognitive distortion identification and modification via Socratic questioning. The chatbot also featured suicidal ideation identification and redirection to suicide hotlines. We used self-assessment scales to measure primary outcomes, including the levels of depression, anxiety, worry tendencies, satisfaction with life, and loneliness at baseline, after the 2-week intervention and at the 1-month follow-up. We also controlled for secondary outcomes, including engagement and frequency of use.

RESULTS

There were no differences in anxiety and depressive symptoms between the groups at enrollment and baseline. After the intervention, depressive and anxiety symptoms were reduced in both groups (chatbot: n=36; control: n=38), which remained stable at the 1-month follow-up. Loneliness was not significantly different between the groups after the intervention, but an exploratory analysis showed a decline in loneliness among participants who used Fido more frequently. Both groups used their intervention technique with similar frequency; however, the control group spent more time (mean 117.57, SD 72.40 minutes) on the intervention than the Fido group (mean 79.44, SD 42.96 minutes).

CONCLUSIONS

We did not replicate the findings from previous (eg, Woebot) studies, as both arms yielded therapeutic effects. However, such results are in line with other research of Internet interventions. Nevertheless, Fido provided sufficient help to reduce anxiety and depressive symptoms and decreased perceived loneliness among high-frequency users, which is one of the first pieces of evidence of chatbot efficacy with agents that use a highly inflected language. Further research is needed to determine the long-term, real-world effectiveness of Fido and its efficacy in a clinical sample.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05762939; https://clinicaltrials.gov/study/NCT05762939; Open Science Foundation Registry 2cqt3; https://osf.io/2cqt3.

General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

BACKGROUND

A conversational agent powered by artificial intelligence, commonly known as a chatbot, is one of the most recent innovations used to provide information and services during the COVID-19 pandemic. However, the multitude of conversational agents explicitly designed during the COVID-19 pandemic calls for characterization and analysis using rigorous technological frameworks and extensive systematic reviews.

OBJECTIVE

This study aims to describe the general characteristics of COVID-19 chatbots and examine their system designs using a modified adapted design taxonomy framework.

METHODS

We conducted a systematic review of the general characteristics and design taxonomy of COVID-19 chatbots, with 56 studies included in the final analysis. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select papers published between March 2020 and April 2022 from various databases and search engines.

RESULTS

Results showed that most studies on COVID-19 chatbot design and development worldwide are implemented in Asia and Europe. Most chatbots are also accessible on websites, internet messaging apps, and Android devices. The COVID-19 chatbots are further classified according to their temporal profiles, appearance, intelligence, interaction, and context for system design trends. From the temporal profile perspective, almost half of the COVID-19 chatbots interact with users for several weeks for >1 time and can remember information from previous user interactions. From the appearance perspective, most COVID-19 chatbots assume the expert role, are task oriented, and have no visual or avatar representation. From the intelligence perspective, almost half of the COVID-19 chatbots are artificially intelligent and can respond to textual inputs and a set of rules. In addition, more than half of these chatbots operate on a structured flow and do not portray any socioemotional behavior. Most chatbots can also process external data and broadcast resources. Regarding their interaction with users, most COVID-19 chatbots are adaptive, can communicate through text, can react to user input, are not gamified, and do not require additional human support. From the context perspective, all COVID-19 chatbots are goal oriented, although most fall under the health care application domain and are designed to provide information to the user.

CONCLUSIONS

The conceptualization, development, implementation, and use of COVID-19 chatbots emerged to mitigate the effects of a global pandemic in societies worldwide. This study summarized the current system design trends of COVID-19 chatbots based on 5 design perspectives, which may help developers conveniently choose a future-proof chatbot archetype that will meet the needs of the public in the face of growing demand for a better pandemic response.

Patients, Doctors, and Chatbots

Medical advice is key to the relationship between doctor and patient. The question I will address is "how may chatbots affect the interaction between patients and doctors in regards to medical advice?" I describe what lies ahead when using chatbots and identify questions galore for the daily work of doctors. I conclude with a gloomy outlook, expectations for the urgently needed ethical discourse, and a hope in relation to humans and machines.

Understanding citizens' attitudes within user-centered digital health ecosystems: A sequential mixed method methodology including a web-survey

Objective

Transitioning from digital health applications to digital health ecosystems, leveraging the advances in technologies and informatics, could be the next revolution in digital health. This includes offering centralized access to various health services and improving citizens' well-being, delivery, clinical processes, and data management. However, a limited understanding of citizens may impede adaptation. Therefore, this study investigates citizens' attitudes within digital health ecosystems, differentiated by their characteristics, to support health service-providers and governmental policymakers in establishing user-centered solutions.

Methods

This study follows a three-step sequential mixed method methodology: (1) a literature review. (2) Qualitative thematic analyses based on semi-structured qualitative interviews. (3) Quantitative analyses based on a web-survey (descriptive statistics, one-way analysis of variances, Tukey-honestly, and Cohen's d tests).

Results

N = 15 citizens were interviewed and n = 1289 responded to the web-survey, to our knowledge the largest survey on this topic. Citizens desire a more convenient management of health services and data (M = 5.2, SD = 1.59). Services with peer-to-peer interactions (M = 3.7, SD = 1.81) and lower involvement of health professionals (M = 3.8, SD = 1.75) are less demanded. Data protection is critical (M = 6.2, SD = 1.23). Public payers are mandated as orchestrators (M = 4.3, SD = 1.99), while private companies receive lower acceptance (M = 3.0, SD = 1.42).

Conclusions

Health service-providers could follow a three-staged approach to establish digital health ecosystems: (1) Increasing the convenience for citizens by enabling online management of health services and data. (2) Extending the citizen-healthcare provider partnership through online interactions. (3) Fostering preventative behaviors and quicker recovery by personalizing health services and interactions. Governmental policymakers should integrate an electronic health record.

An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives

Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.

ChatGPTs' Journey in Medical Revolution: A Potential Panacea or a Hidden Pathogen?

At the fascinating intersection of artificial intelligence and medicine, ChatGPT morphs into a compact, personal digital physician. With a simple click, it furnishes an abundance of health-related information, initial medical consultations, and a plethora of disease management recommendations. Moreover, it stands at the ready to provide immediate mental health assistance in times of psychological distress. Yet, each innovation carries inherent challenges. As we embrace the conveniences proffered by ChatGPT, it is imperative that we grapple with associated issues such as data privacy, risk of misdiagnosis, complexities in human-machine interaction, and particular situations that elude its understanding. Let's probe further into this intriguing world, brimming with contention and prospects, and observe how ChatGPT traverses the landscape of digital health, uncovering the potential it holds for the future evolution of medical practice.

Global impacts of COVID-19 on lifestyles and health and preparation preferences: An international survey of 30 countries

BACKGROUND

The health area being greatest impacted by coronavirus disease 2019 (COVID-19) and residents' perspective to better prepare for future pandemic remain unknown. We aimed to assess and make cross-country and cross-region comparisons of the global impacts of COVID-19 and preparation preferences of pandemic.

METHODS

We recruited adults in 30 countries covering all World Health Organization (WHO) regions from July 2020 to August 2021. 5 Likert-point scales were used to measure their perceived change in 32 aspects due to COVID-19 (-2 = substantially reduced to 2 = substantially increased) and perceived importance of 13 preparations (1 = not important to 5 = extremely important). Samples were stratified by age and gender in the corresponding countries. Multidimensional preference analysis displays disparities between 30 countries, WHO regions, economic development levels, and COVID-19 severity levels.

RESULTS

16 512 adults participated, with 10 351 females. Among 32 aspects of impact, the most affected were having a meal at home (mean (m) = 0.84, standard error (SE) = 0.01), cooking at home (m = 0.78, SE = 0.01), social activities (m = -0.68, SE = 0.01), duration of screen time (m = 0.67, SE = 0.01), and duration of sitting (m = 0.59, SE = 0.01). Alcohol (m = -0.36, SE = 0.01) and tobacco (m = -0.38, SE = 0.01) consumption declined moderately. Among 13 preparations, respondents rated medicine delivery (m = 3.50, SE = 0.01), getting prescribed medicine in a hospital visit / follow-up in a community pharmacy (m = 3.37, SE = 0.01), and online shopping (m = 3.33, SE = 0.02) as the most important. The multidimensional preference analysis showed the European Region, Region of the Americas, Western Pacific Region and countries with a high-income level or medium to high COVID-19 severity were more adversely impacted on sitting and screen time duration and social activities, whereas other regions and countries experienced more cooking and eating at home. Countries with a high-income level or medium to high COVID-19 severity reported higher perceived mental burden and emotional distress. Except for low- and lower-middle-income countries, medicine delivery was always prioritised.

CONCLUSIONS

Global increasing sitting and screen time and limiting social activities deserve as much attention as mental health. Besides, the pandemic has ushered in a notable enhancement in lifestyle of home cooking and eating, while simultaneously reducing the consumption of tobacco and alcohol. A health care system and technological infrastructure that facilitate medicine delivery, medicine prescription, and online shopping are priorities for coping with future pandemics.

Human vs. machine-like representation in chatbot mental health counseling: the serial mediation of psychological distance and trust on compliance intention

This study examined a serial mediation mechanism to test the effect of chatbots' human representation on the intention to comply with health recommendations through psychological distance and trust towards the chatbot counselor. The sample of the study comprised 385 adults from the USA. Two artificial intelligence chatbots either with human or machine-like representation were developed. Participants had a short conversation with either of the chatbots to simulate an online mental health counseling session and reported their experience in an online survey. The results showed that participants in the human representation condition reported a higher intention to comply with chatbot-generated mental health recommendations than those in the machine-like representation condition. Furthermore, the results supported that both psychological distance and perceived trust towards the chatbot mediated the relationship between human representation and compliance intention, respectively. The serial mediation through psychological distance and trust in the relationship between human representation and compliance intention was also supported. These findings provide practical guidance for healthcare chatbot developers and theoretical implications for human-computer interaction research.

A comparison between online social media discussions and vaccination rates: A tale of four vaccines

The recent COVID-19 pandemic has brought the debate around vaccinations to the forefront of public discussion. In this discussion, various social media platforms have a key role. While this has long been recognized, the way by which the public assigns attention to such topics remains largely unknown. Furthermore, the question of whether there is a discrepancy between people's opinions as expressed online and their actual decision to vaccinate remains open. To shed light on this issue, in this paper we examine the dynamics of online debates among four prominent vaccines (i.e., COVID-19, Influenza, MMR, and HPV) through the lens of public attention as captured on Twitter in the United States from 2015 to 2021. We then compare this to actual vaccination rates from governmental reports, which we argue serve as a proxy for real-world vaccination behaviors. Our results demonstrate that since the outbreak of COVID-19, it has come to dominate the vaccination discussion, which has led to a redistribution of attention from the other three vaccination themes. The results also show an apparent discrepancy between the online debates and the actual vaccination rates. These findings are in line with existing theories, that of agenda-setting and zero-sum theory. Furthermore, our approach could be extended to assess the public's attention toward other health-related issues, and provide a basis for quantifying the effectiveness of health promotion policies.

Development of a chatbot for depression: adolescent perceptions and recommendations

BACKGROUND

Chatbots are a relatively new technology that has shown promising outcomes for mental health symptoms in adults; however, few studies have been done with adolescents or reported adolescent user experiences and recommendations for chatbot development.

METHODS

Twenty three participants ages 13-18 (M_age  = 14.96) engaged in user testing of a chatbot developed to psychoeducate adolescents on depression, teach behavioral activation, and change negative thoughts. Thematic analysis was conducted of participants' responses to user experience questions, impressions, and recommendations.

RESULTS

Over half (56.5%) of the sample completed the full intervention and provided user experience feedback online. The average NPS score was 6.04 (SD = 2.18), and 64.3% (n = 9) said they would use the chatbot in the future. Of all user experience responses, 54.5% were positive. The most common impressions were related to symptom improvement (61.1%) and availability (52.8%) The most frequent recommendations were related to solving technical problems (66%).

CONCLUSIONS

Chatbots for mental health are acceptable to some adolescents, a population that tends to be reluctant to engage with traditional mental health services. Most participants reported positive experiences with the chatbot, believing that it could help with symptom improvement and is highly available. Adolescents highlighted some technical and stylistic problems that developers should consider. More pilot and user testing is needed to develop mental health chatbots that are appealing and relevant to adolescents.

COVID-19 Vaccine Equity and Access: Case Study for Health Care Chatbots

BACKGROUND

Disparities in COVID-19 information and vaccine access have emerged during the pandemic. Individuals from historically excluded communities (eg, Black and Latin American) experience disproportionately negative health outcomes related to COVID-19. Community gaps in COVID-19 education, social, and health care services (including vaccines) should be prioritized as a critical effort to end the pandemic. Misinformation created by the politicization of COVID-19 and related public health measures has magnified the pandemic's challenges, including access to health care, vaccination and testing efforts, as well as personal protective equipment. Information and Communication Technology (ICT) has been demonstrated to reduce the gaps of marginalization in education and access among communities. Chatbots are an increasingly present example of ICTs, particularly in health care and in relation to the COVID-19 pandemic.

OBJECTIVE

This project aimed to (1) follow an inclusive and theoretically driven design process to develop and test a COVID-19 information ICT bilingual (English and Spanish) chatbot tool named "Ana" and (2) characterize and evaluate user experiences of these innovative technologies.

METHODS

Ana was developed following a multitheoretical framework, and the project team was comprised of public health experts, behavioral scientists, community members, and medical team. A total of 7 iterations of ß chatbots were tested, and a total of 22 ß testers participated in this process. Content was curated primarily to provide users with factual answers to common questions about COVID-19. To ensure relevance of the content, topics were driven by community concerns and questions, as ascertained through research. Ana's repository of educational content was based on national and international organizations as well as interdisciplinary experts. In the context of this development and pilot project, we identified an evaluation framework to explore reach, engagement, and satisfaction.

RESULTS

A total of 626 community members used Ana from August 2021 to March 2022. Among those participants, 346 used the English version, with an average of 43 users per month; and 280 participants used the Spanish version, with an average of 40 users monthly. Across all users, 63.87% (n=221) of English users and 22.14% (n=62) of Spanish users returned to use Ana at least once; 18.49% (n=64) among the English version users and 18.57% (n=52) among the Spanish version users reported their ranking. Positive ranking comprised the "smiley" and "loved" emojis, and negative ranking comprised the "neutral," "sad," and "mad" emojis. When comparing negative and positive experiences, the latter was higher across Ana's platforms (English: n=41, 64.06%; Spanish: n=41, 77.35%) versus the former (English: n=23, 35.93%; Spanish: n=12, 22.64%).

CONCLUSIONS

This pilot project demonstrated the feasibility and capacity of an innovative ICT to share COVID-19 information within diverse communities. Creating a chatbot like Ana with bilingual content contributed to an equitable approach to address the lack of accessible COVID-19-related information.

Intention to use digital mental health solutions: A cross-sectional survey of university students attitudes and perceptions toward online therapy, mental health apps, and chatbots

Background

Globally, the high prevalence of mental disorders among university students is a growing public health problem, yet a small minority of students with mental health problems receive treatment. Digital mental health solutions could bridge treatment gaps and overcome many barriers students face accessing treatment. However, there is scant evidence, especially in South Africa (SA), relating to university students' use of and intention to use digital mental health solutions or their attitudes towards these technologies. We aim to explore university 2students attitudes towards and perceptions of digital mental health solutions, and the factors associated with their intention to use them.

Methods

University students from four SA universities (n = 17 838) completed an online survey to assess experience with, attitudes and perceptions of, and intentions to use, digital mental health solutions. We conducted an exploratory factor analysis to identify factors underlying attitudes and perceptions, and then used multivariate ordinal regression analysis was used to investigate the factors' association with students' intention to use digital mental health solutions.

Results

Intention to use digital mental health solutions was high, and attitudes towards and perceptions of digital mental health solutions were largely positive. Importantly, our analysis also shows that 12.6% of users were willing to utilise some form of digital mental health solutions but were unwilling to utilise traditional face-to-face therapies. The greatest proportion of variance was explained by the factor 'Attitudes towards digital technologies' utility to improve student counselling services, provided they are safe'.

Conclusion

SA university students are already engaging with digital mental health solutions, and their intention to do so is high. Certain attitudes and perceptions, particularly concerning the utility, effectiveness, and safety, underlie willingness to engage with these solutions, providing potential targets for interventions to increase uptake.

Effects of personalization and source expertise on users' health beliefs and usage intention toward health chatbots: Evidence from an online experiment

Objective

Based on the heuristic–systematic model (HSM) and health belief model (HBM), this study aims to investigate how personalization and source expertise in responses from a health chatbot influence users’ health belief-related factors (i.e. perceived benefits, self-efficacy and privacy concerns) as well as usage intention.

Methods

A 2 (personalization vs. non-personalization) × 2 (source expertise vs. non-source expertise) online between-subject experiment was designed. Participants were recruited in China between April and May 2021. Data from 260 valid observations were used for the data analysis.

Results

Source expertise moderated the effects of personalization on health belief factors. Perceived benefits and self-efficacy mediated the relationship between personalization and usage intention when the source expertise cue was presented. However, the privacy concerns were not influenced by personalization and source expertise and did not significantly affect usage intention toward the health chatbot.

Discussion

This study verified that in the health chatbot context, source expertise as a heuristic cue may be a necessary condition for effects of the systematic cue (i.e. personalization), which supports the HSM's arguments. By introducing the HBM in the chatbot experiment, this study is expected to provide new insights into the acceptance of healthcare AI consulting services.