In the rapidly advancing realm of healthcare technology, artificial intelligence (AI) chatbots are proving to be a valuable asset for improving patient care and optimizing medical processes. A comprehensive multinational cross-sectional study, conducted by researchers Abdelwahed, Abd El-Nasser, Heih, and others, uncovers insights into public perceptions and usage of AI chatbots in healthcare assistance. Scheduled for publication in BMC Health Services Research in 2025, this study not only captures the preferences of various cultural groups but also identifies potential challenges and motivators related to adopting these AI-powered platforms.

As technology continues to permeate healthcare, gauging public sentiment regarding AI chatbots is essential. The research surveyed a varied demographic across multiple nations, providing a well-rounded view of how this innovative technology can influence patient care. The insights gained from this study can guide healthcare providers and policymakers in implementing AI solutions effectively within clinical environments.

One of the pivotal revelations from this study is the trust that individuals place in AI chatbots for healthcare inquiries. Trust plays a fundamental role in the adoption of any technological advancement, especially in the sensitive field of healthcare, where both privacy and accuracy are critical. A considerable number of participants expressed confidence in chatbots for delivering dependable information, demonstrating that prior experiences with online resources have shaped their readiness to seek medical advice through these AI systems. However, addressing concerns about the reliability of AI is vital for wider acceptance.

The study also highlighted significant differences in enthusiasm for AI chatbots across various age groups. Younger participants were generally more open to engaging with these tools, likely due to their comfort with technology in daily life. Conversely, older individuals tended to be more skeptical, often driven by anxieties regarding data security and a lack of understanding of how AI systems function. This age-related divide presents challenges for the healthcare sector, necessitating strategies to promote broader acceptance of AI chatbots among all demographics.

Another key finding pertains to the services that people are most interested in accessing through AI chatbots. A majority indicated a preference for chatbots to handle administrative duties, such as scheduling appointments and retrieving medical records, signaling a strong desire for AI to improve operational efficiency. This insight presents healthcare providers with opportunities to create AI-driven solutions that enhance workflow while enabling human staff to concentrate on the more compassionate aspects of patient care.

Despite the generally positive attitudes expressed towards AI chatbots, some notable challenges remain for effective implementation. Participants voiced significant concerns about maintaining the confidentiality of personal health information during interactions with AI. Respondent hesitance regarding how their data might be used underscores the necessity for developers and healthcare organizations to prioritize privacy measures to build trust and encourage adoption.

The research further emphasizes customization as a critical factor in the effectiveness of AI chatbots. Many respondents showed a preference for chatbots that could tailor interactions according to their specific needs and preferences, rather than offering generic responses. This can involve employing natural language processing techniques to recognize emotional cues and personal medical histories, enabling chatbots to deliver responses that are both accurate and empathetic.

Moreover, the study indicates that educating the public about the capabilities and limitations of AI chatbots is crucial for successful implementation. A notable number of respondents were unaware of how these systems work or of the potential benefits they offer. Therefore, healthcare organizations must invest in educational initiatives to inform the public about AI's role in healthcare, alleviating fears and fostering a better understanding of the technology.

The geographical diversity of the study's participant pool highlighted how cultural attitudes towards technology significantly impact the acceptance of AI chatbots. The responses varied greatly between countries, emphasizing the necessity for localized strategies when introducing these technologies. For instance, nations with a strong focus on technological innovation exhibited higher levels of acceptance compared to regions with limited familiarity with AI, suggesting a vital role for healthcare professionals in different cultural contexts in facilitating the integration of AI chatbots.

Ethical considerations also surfaced in the findings, particularly regarding the potential for AI to inadvertently perpetuate existing biases. It is critical within the healthcare sphere to ensure that the algorithms behind chatbots are trained on diverse data to prevent inequalities in care quality based on socio-economic status or racial background.

As AI continues to reshape healthcare services, there is a pressing need for policymakers to engage in discussions regarding the regulation of these technologies. The study suggests that proper oversight will be essential to ensure that AI chatbots are deployed responsibly, protecting patients from potential risks associated with inadequate design or implementation. Establishing regulatory frameworks can help maintain ethical standards and safeguard user interactions with AI.

Looking forward, the successful integration of AI chatbots in healthcare will largely depend on collaboration between technology developers, healthcare providers, and patients. A transparent and cooperative effort will be necessary to refine AI solutions that genuinely meet user needs. This collaboration could facilitate the development of chatbots that not only assist with medical inquiries but also offer comfort and support during stressful healthcare experiences.

The outcomes of this multinational cross-sectional study reinforce the idea that AI chatbots could significantly enhance healthcare delivery, although several challenges remain. Public awareness, privacy concerns, ethical considerations, and cultural perceptions all play integral roles in influencing the success of these technologies. As stakeholders navigate this evolving landscape, carefully devised strategies and frameworks will be crucial for harnessing the potential of AI in healthcare while ensuring patient trust and safety.

Ultimately, the research findings from Abdelwahed and colleagues suggest a pressing need for further investigation into the ongoing growth of AI within the healthcare sector. As technological advancements continue to shape patient interactions with medical services, understanding public sentiment will be critical in designing initiatives that align with the needs and expectations of both patients and healthcare providers. The future of AI chatbots in healthcare is ripe with potential, and effectively engaging with public attitudes will be vital in determining their success in enhancing healthcare outcomes.

International Views on AI Chatbots in Healthcare

A sunlit, modern clinic room where a diverse doctor and patient sit at a sleek table, the doctor pointing to a tablet showing a friendly AI chatbot interface; behind them, a subtle world map mural on the wall with glowing connection lines linking continents; warm natural light floods in through large windows, creating a hopeful, collaborative atmosphere that speaks to AI healthcare’s global reach.

Global Perspectives on AI Chatbots in Healthcare - BIOENGINEER.ORG

- A multinational study reveals varied public attitudes towards AI chatbots in healthcare.  
- Trust in AI chatbots varies by demographic, with younger individuals more accepting.  
- Privacy concerns and cultural differences hinder widespread adoption of AI in healthcare.  

In the ever-evolving landscape of healthcare technology, the use of artificial intelligence (AI) chatbots has emerged as a promising tool for enhancing patient care and streamlining medical services. A recent multinational cross-sectional study conducted by Abdelwahed, Abd El-Nasser, Heih, and colleagues sheds light on public attitudes and practices toward AI chatbots in healthcare assistance. The findings, set to be published in BMC Health Services Research in 2025, reveal not only the preferences of individuals across various cultural backgrounds but also highlight the potential barriers and incentives related to the adoption of these AI-driven platforms.

As the integration of technology into healthcare becomes increasingly prevalent, understanding public perception of AI chatbots is crucial. This study surveyed a diverse population from multiple countries, ensuring that the results reflect a global perspective on this emerging technology’s role in patient care. Resulting insights offered by this research could inform healthcare providers and policymakers about necessary steps to successfully implement AI solutions in clinical settings.

One significant finding from the study is the level of trust the public places in AI chatbots for healthcare-related inquiries. Trust is a critical factor in the adoption of any technology, particularly in healthcare, where privacy and accuracy are paramount. Many respondents expressed confidence in chatbots’ ability to provide reliable information, stemming from their experiences with online resources that often serve as preliminary touchpoints for seeking medical advice. This indicates that, while many are open to AI integration, concerns about the reliability of such technology must be addressed.

Additionally, the study revealed varying degrees of enthusiasm for the use of chatbots across different demographics. Younger individuals showed a higher willingness to engage with AI tools, influenced by their familiarity with technology in everyday life. In contrast, older generations exhibited skepticism, often related to fears about data security and a lack of understanding regarding how these systems operate. This generational divide poses challenges for the healthcare industry, which must find ways to bridge the gap and encourage wider acceptance of AI chatbots among all age groups.

Another crucial aspect highlighted in the research pertains to the types of healthcare services that respondents desired from AI chatbots. Many preferred chatbots for administrative tasks, such as scheduling appointments and accessing medical records, suggesting that there is a strong interest in using AI for background processes that enhance overall efficiency. This preference underscores a significant opportunity for healthcare providers to develop AI-driven solutions that streamline operations while allowing human professionals to focus on aspects of care requiring personal interaction and empathy.

Despite the positive attitudes observed, challenges persist when it comes to implementing AI chatbots effectively. The survey indicated substantial concerns about the confidentiality of personal health information when interacting with AI. Respondents expressed wariness about how their data would be used and who would have access to it. Addressing these privacy concerns must be a priority for developers and healthcare institutions if they aim to cultivate trust and encourage adoption among potential users.

The study also highlighted the role of customization in the effectiveness of AI chatbots. Respondents indicated a preference for chatbots that could adapt to their individual needs and preferences, requesting personalized interactions rather than one-size-fits-all responses. This could entail using natural language processing to understand emotional cues or specific medical histories, allowing chatbots to respond more accurately and empathetically to users’ concerns.

In addition to addressing individual user preferences, the research indicated that successful implementation of AI chatbots would require significant public education about their capabilities and limitations. Many respondents were unfamiliar with how chatbots function and their potential benefits. Health organizations must invest in outreach and educational programs to inform the public about how AI can assist in their healthcare journeys, reducing anxiety and fostering a greater understanding of the technology.

The geographic diversity of the study sample revealed that cultural attitudes toward technology heavily influence the acceptance of AI chatbots. Responses differed markedly between nations, underscoring the need for region-specific strategies when rolling out these technologies. For example, in countries with a strong emphasis on technological innovation, acceptance levels were noticeably higher compared to regions with less familiarity with AI. Notably, this discrepancy raises questions about the role of healthcare professionals across different cultures in facilitating the integration of AI chatbots.

Several ethical considerations also emerged in the findings, such as the potential for AI to inadvertently reinforce existing biases. Within the healthcare ecosystem, it is essential to ensure that algorithms driving chatbots are trained on diverse datasets to avoid perpetuating inequalities. Misalignment between the training data and patient demographics could lead to discrepancies in the quality of care provided based on socio-economic status or racial background, further complicating the landscape of healthcare delivery.

Moreover, as AI continues to transform healthcare services, policymakers must engage in a dialog about the regulation of these technologies. The study suggests that oversight will be necessary to monitor the deployment of AI chatbots and protect patients from harmful practices that may arise from poor design or implementation. Establishing regulatory frameworks could help ensure that ethical standards are upheld and that users are safeguarded in their interactions with AI systems.

Looking ahead, the future of AI chatbots in healthcare depends on collaborative efforts among technology developers, healthcare providers, and patients. A transparent and cooperative approach will be key in refining AI solutions to better meet the needs of users. This collaboration can facilitate the development of AI chatbots that not only assist with medical inquiries but also provide reassurance and support during potentially stressful healthcare interactions.

The results of this multinational cross-sectional study bolster the notion that AI chatbots could significantly impact healthcare delivery, though challenges remain. Public awareness, privacy, ethical considerations, and cultural attitudes all play pivotal roles in determining the success of these technologies. As stakeholders work to navigate this landscape, prudent strategies and frameworks will be essential in harnessing the potential of AI in healthcare while ensuring patient trust and safety.

Ultimately, the findings presented in this study by Abdelwahed and colleagues invite further research into the ongoing evolution of AI in healthcare. As technological advancements continue to shape how patients interact with medical systems, understanding public sentiment is crucial for developing initiatives that align with the expectations and concerns of both patients and providers. The road ahead for AI chatbots in healthcare is filled with potential, and effective engagement with public attitudes will play a significant role in determining their success in improving healthcare outcomes.

Subject of Research: Public attitudes and practices toward AI chatbots in healthcare assistance

Article Title: Public attitudes and practices toward using AI chatbots for healthcare assistance: a multinational cross-sectional study

Article References:

Abdelwahed, A., Abd El-Nasser, M., Heih, O. et al. Public attitudes and practices toward using AI chatbots for healthcare assistance: a multinational cross-sectional study.

BMC Health Serv Res (2025). https://doi.org/10.1186/s12913-025-13832-0

Image Credits: AI Generated

DOI: 10.1186/s12913-025-13832-0

Keywords: AI chatbots, healthcare, public attitudes, trust, patient care, technology adoption, privacy concerns, ethical considerations

Tags: AI chatbots in healthcarebarriers to AI chatbot adoptioncultural differences in healthcare technology acceptanceEnhancing patient care with AIglobal perspectives on healthcare technologyhealthcare providers and AI solutionsincentives for using AI in healthcaremultinational study on AI chatbotspatient care and AI integrationprivacy concerns in AI healthcare applicationspublic attitudes towards AI in medicinetrust in healthcare technology

Global Perspectives on AI Chatbots in Healthcare

In just the first half of the year, a significant achievement was noted by a Virginia health system for enhancing patient care through improved follow-up processes in radiology, aiming to prevent advanced diseases and support value-based care (VBC) initiatives.

Sentara Health stands as a prominent health system not only in the Mid-Atlantic and Southeast regions but also ranks among the top 20 largest non-profit health systems nationwide. With a workforce of 34,000 employees across 12 hospitals in Virginia and Northeastern North Carolina, Sentara also encompasses the Sentara Health Plans division, catering to over one million members in Virginia and Florida.

The Challenge

Sentara's facilities handle an extensive volume of imaging procedures, with each study often containing follow-up recommendations for additional imaging required within specified timeframes. Traditionally, these recommendations were included in radiology reports and communicated to the ordering healthcare provider. However, after a report was signed off and sent, the ability to ensure that every follow-up recommendation led to a completed patient action was quite limited.

As with many health systems, Sentara depended on the diligence of individual clinicians, manual worklists, and informal tracking methods to ensure follow-ups occurred, according to Prasanna Mohanty, executive vice president and president of ambulatory care. He emphasized that while findings were documented in their electronic health record (EHR), the system wasn’t designed to systematically extract and prioritize follow-ups across various facilities and service lines. Consequently, teams had to navigate through various notes and messages or create their own spreadsheets, making the process labor-intensive and hard to standardize.

Sentara was particularly wary of incidental findings—such as lung nodules and adrenal lesions—that, while not urgent, required timely follow-ups, but could easily slip through the cracks amid daily clinical tasks. Their audits revealed substantial efforts and performances across different areas; however, they identified inconsistencies across sites and specialties and realized they lacked a robust, reliable process for follow-ups.

From a leadership perspective, their aim was to establish an enterprise-wide system to capture and track all actionable findings efficiently, alleviate clinician workloads, mitigate liability risks, and ensure no patient was overlooked.

“Our existing tool aimed at follow-ups fell short of offering a reliable, automated solution for completing follow-ups,” Mohanty noted. “We grappled with a labor-intensive workflow, marked by numerous patients flagged for follow-up who ultimately did not receive it.”

Proposal

As Sentara sought a new plan, its objective wasn't merely to introduce another worklist but to re-engineer radiology follow-up as a reliable process. They aimed to find a system foundationally based on quality improvement principles that could automatically analyze radiology reports, pinpoint all follow-up recommendations irrespective of format, and present information in a user-friendly manner for the teams to act upon, as outlined by Mohanty.

The selected proposal revolved around utilizing advanced large language models and orchestrated automation for processing radiology reports. This technology promised to "read" reports in real-time, identify any follow-up recommendations, and enhance them with context from the EHR.

The system was designed to channel cases into dedicated automated workflows for review, scheduling, and patient engagement. Crucially, it was intended to mesh seamlessly with their existing EHR ecosystem, particularly the Epic platform, allowing clinicians to remain within their usual workflows while the system undertook the challenging tasks of tracking and data analysis.

“We envisioned a comprehensive safety net, ensuring every actionable finding was recorded, each follow-up had a designated point of accountability, and that leadership could monitor performance across various regions, service lines, and care settings,” he explained.

The decision to collaborate with Inflo Health was guided by the vendor's platform, which was specifically tailored to embody this high-reliability vision. Inflo's AI is trained on diagnostic follow-up recommendations to identify all follow-up needs, automate communication, and organize cases, while also providing detailed analytics concerning completion rates and financial effects.

Staff anticipated that this AI-based method would substantially diminish missed or delayed follow-ups, enhance documentation quality, and furnish data for continuous process improvement.

Meeting the Challenge

Sentara Health initiated the implementation of Inflo Health's follow-up platform within radiology divisions where missed follow-ups posed serious risks—like in CT and MRI, emergency imaging, and select outpatient studies. Radiologists continued their traditional reporting process, and once reports were finalized, the AI engine would automatically analyze the narrative and extract any recommended follow-ups to create a structured record.

These recommendations feed into an automated, centralized follow-up work queue utilized by care navigation and imaging operations teams. In this queue, cases are arranged by urgency, imaging modality, site, and ordering provider, enabling navigators to see which patients require additional imaging or consultations and identify overdue recommendations along with potential barriers.

“At the click of a button, they can monitor patients who have scheduled and completed follow-ups, identify those needing extra outreach, and initiate the necessary next steps,” Mohanty explained.

Integration with the Epic electronic health record was a key element of the deployment, allowing Inflo to connect through secure interfaces to ensure patient demographics, provider details, and scheduling options are accessible within the follow-up workflow.

AI-identified recommendations generate tasks and messages within standard Epic in-baskets and work queues for clinicians and schedulers. Patient communications—whether via the MyChart portal, SMS, phone calls, or letters—are managed through existing tools but are all coordinated through the follow-up platform to ensure a complete audit trail.

“Daily, various teams engage with the system. Radiologists benefit from consistently visible and tracked recommendations rather than relying on manual notes. Imaging leaders and operational managers monitor dashboards displaying open versus completed follow-ups, site performance, and documentation trends,” Mohanty added.

Primary care and specialty providers receive notifications when patients they referred for imaging have critical recommendations, enabling direct follow-up study orders or referrals within their routine workflows.

All these components have transitioned staff from a largely manual, fragmented approach to a unified, reliable model, indicated Mohanty.

“Technology doesn’t replace clinical judgment; it rather empowers our teams with visibility and organization needed to act on that judgment consistently, even within a high-volume, multi-site environment,” he clarified.

Results

Sentara Health observed remarkable improvements from the communication and analytics AI engine.

"During the initial six months of platform utilization, 61% of follow-up appointments managed through this initiative were completed, directly linked to the revamped process,” Mohanty reported, encompassing imaging studies, specialty consultations, and other downstream care that might have otherwise faced delays.

"Though we are still compiling our longitudinal data, this marks a significant advancement toward reliable follow-up in a short timeframe," he added. "The AI-driven workflow has been pivotal in this progress."

Within that same six-month timespan, Sentara recorded 4,032 follow-ups resulting from cases identified and managed through the new system.

“These cases represent not only patients who underwent an initial imaging exam but also those who engaged in additional imaging or specialty care as recommended,” he elaborated. “For a health system of our size, this volume signifies a substantial number of potentially high-risk cases, assuring that care extended beyond the initial report.”

Moreover, this volume carries operational importance, as prior to implementation, quantifying the successful completion of follow-ups across the system was challenging, let alone analyzing it by modality, site, or timeframe. This level of visibility now allows for the identification of scheduling bottlenecks and highlights which service lines generate the most follow-up activity, directing process improvements where they would yield the best outcomes.

In addition to quality and safety gains, the health system has realized a significant financial impact.

“In the first six months, we estimate around $1.7 million in revenue linked to follow-ups captured and completed through the Inflo-supported workflow,” Mohanty shared. “This figure reflects downstream imaging, visits, and procedures that might have been neglected if follow-up wasn’t proactively managed.

“We see this financial benefit as complementary to our quality objectives,” he concluded. “Guaranteeing patients receive necessary follow-up care curtails the progression of serious diseases, bolsters value-based care initiatives, and fortifies relations with patients and referring providers. Demonstrating a clear return on investment also simplifies the task of sustaining and expanding the program and investing in additional navigation resources across new sites and clinical specialties.”

Sentara earns $1.7M utilizing an AI-driven workflow and analytics solution.

A sunlit hospital strategy room at morning light, a large wall-mounted digital dashboard displays dynamic charts and “$1.7 M gain” under the heading “AI Workflow & Analytics.” The Sentara logo is prominently on the wall. A doctor and a hospital administrator exchange satisfied glances as they review the data, warm natural light flooding the room to convey optimism and progress.

Sentara gains $1.7M with AI-based workflow and analytics tool - Healthcare IT News

- Sentara Health implemented an AI-driven system to enhance radiology follow-up efficiency and reliability.  
- In six months, 61% of managed follow-up appointments were completed, improving patient care outcomes.  
- The initiative resulted in an estimated $1.7 million revenue increase from successfully captured follow-ups.  

And that was just in the first six months. By catching needed radiology follow-ups and ensuring patients receive recommended follow-up care, the Virginia health system helps prevent more advanced disease and supports VBC initiatives, one exec says.

Sentara Health is one of the largest health systems in the Mid-Atlantic and Southeast, and among the top 20 largest not-for-profit health systems in the country. With 34,000 employees and 12 hospitals in Virginia and Northeastern North Carolina, it also includes the Sentara Health Plans division, which serves more than one million members in Virginia and Florida.

THE CHALLENGE

Sentara performs a very high volume of imaging across its hospitals, ambulatory sites and emergency departments. Each of the studies may include follow-up recommendations like additional imaging that should happen within a specific timeframe.

Historically, such recommendations were embedded in radiology reports and communicated through traditional channels to the ordering provider. Once the report was signed and delivered, however, the organization had limited ability to ensure every follow-up recommendation was translated into a completed action for the patient.

Like many health systems, Sentara relied heavily on the diligence of individual clinicians, manual worklists and ad hoc tracking methods to close the loop, said Prasanna Mohanty, executive vice president, president for ambulatory care, and enterprise leader for service lines and population health.

"We documented findings and recommendations in our EHR, but it was not designed to systematically extract, prioritize and track follow-ups across multiple facilities and service lines," he explained. "In practice, that meant our teams had to hunt through notes, inbox messages and problem lists – or build their own spreadsheets and reminder systems. It was labor-intensive, difficult to standardize and nearly impossible to scale to an enterprise of our size.

"We were particularly concerned about incidental or 'non-urgent but not optional' findings – lung nodules, adrenal lesions, and other abnormalities that require time-bound follow-up but are easy to lose in the day-to-day crush of clinical work," he continued. "While our internal audits showed dedicated efforts and strong performance in many areas, we also saw variation across sites and specialties and knew we did not have a truly closed-loop, high-reliability process."

From a leadership standpoint, Sentara wanted an enterprise-wide safety net that enables it to identify and track every actionable finding, reduce the burden on clinicians, protect against liability, and ensure no patient fell through the cracks.

"We had a tool that promised follow-up, but failed at delivering a high-reliability, automated path to closing follow-ups," he noted. "We struggled with a highly manual workflow with too many patients flagged for follow-up who did not have a follow-up."

PROPOSAL

When Sentara began looking for a new approach, its goal was not simply to add another worklist. It wanted to redesign radiology follow-up as a high-reliability process. That meant finding a system built on quality improvement principles that could automatically read radiology reports, identify every recommendation for follow-up (regardless of wording or format), and organize information in a way teams could reliably act on, Mohanty said.

"The proposal we ultimately selected centered on applying advanced large language models and orchestrated automation to our radiology reports," he explained. "Rather than asking radiologists or ordering providers to manually flag every follow-up, the technology promised to 'read' reports in real time, detect any recommendation linked to an exam, and enrich that recommendation with context from the EHR."

The system then would route cases to dedicated, automated workflows for review, scheduling and patient outreach.

"Equally important, the system was designed to integrate tightly with our existing EHR ecosystem, particularly our Epic platform, so clinicians could stay in their normal workflows while a separate system handled the heavy lifting of tracking, escalation and analytics," he noted. "We envisioned an enterprise safety net: Every actionable finding would be captured, each follow-up would have a clear owner, and leadership would gain line-of-sight into performance across regions, service lines and sites of care.

"We chose to work with Inflo Health because its platform was purpose-built around that high-reliability vision," he continued. "The vendor's AI engine is trained specifically on diagnostic follow-up recommendations and designed to identify 100% of follow-up needs, automate communication and case organization, and provide robust analytics on completion rates and financial impact."

Before implementation, staff's expectation was this artificial intelligence-based approach would help them significantly reduce missed or delayed follow-ups, improve documentation quality, and give them the data they needed to continuously refine processes.

MEETING THE CHALLENGE

Sentara Health began by implementing Inflo Health's follow-up platform in radiology service lines where the risk of missed follow-up is especially consequential – such as CT and MRI, emergency department imaging, and certain outpatient studies.

Radiologists continue to read and report studies exactly as they always have. Once a report is finalized, the AI engine automatically parses the narrative, identifies any recommended follow-up and creates a structured record of that recommendation.

Those recommendations feed into a centralized, automated follow-up work queue used by the care navigation and imaging operations teams. Within that queue, cases are organized by acuity, modality, site and ordering provider. Navigators can see which patients are due for additional imaging or consults, which recommendations are approaching or past due, and where there may be barriers to completion.

"With one click, they can track the patients who are scheduled and completed through automated pathways, identify patients that require additional outreach or escalation, and initiate the appropriate next step," Mohanty explained.

"Integration with our Epic electronic health record was a critical component of the deployment," he continued. "Inflo connects to Epic through secure interfaces so patient demographics, provider information and scheduling options are available within the follow-up workflow."

Recommendations identified by the AI can generate tasks and messages that appear in standard Epic in-baskets and work queues for clinicians and schedulers. Patient communications – whether through the MyChart portal, SMS, phone calls or letters – are driven by existing tools, but are all coordinated and tracked through the follow-up platform so Sentara maintains a complete audit trail.

"In daily practice, several different groups interact with the system," Mohanty said. "Radiologists benefit from knowing their recommendations are consistently surfaced and tracked, rather than relying on manual callbacks or free-text notes. Imaging leaders and operational managers monitor dashboards that show open versus completed follow-ups, performance by site, and trends in documentation.

"Care navigators and scheduling teams work from prioritized queues instead of reviewing charts one by one," he added. "Primary care and specialty providers are notified when a patient they ordered imaging for has a critical or time-sensitive recommendation, and they can order follow-up studies or referrals directly from within their normal workflow."

Together, these elements have allowed staff to move from a largely manual, fragmented process to an integrated, closed-loop, high-reliability model, he said.

"Technology does not replace clinical judgment; rather, it gives our teams the visibility and structure they need to consistently act on that judgment, even in a high-volume, multi-site environment," he noted.

RESULTS

Sentara Health achieved impressive results with the communication and analytics AI engine.

"Within the first six months of using the platform, 61% of the follow-up appointments managed through this program were completed and directly attributable to the new process," Mohanty reported. "This includes imaging studies, specialty consultations, and other downstream care that might previously have been delayed or overlooked.

"While we are still building our longitudinal data sets, this represents a significant step toward high-reliability follow-up in a relatively short period of time," he added. "The AI-driven workflow has been central to this improvement."

Over that same six-month period, Sentara completed 4,032 follow-ups that can be attributed to cases identified and managed through the new system.

"These are patients who not only received an initial imaging exam, but also completed the additional imaging or specialty care that was recommended as a result," he explained. "For a large health system, this number represents a material volume of potentially high-risk cases where we have greater confidence that care did not stop at the initial report.

"This volume is also important from an operational standpoint," he continued. "Before implementation, we did not have an easy way to quantify how many follow-ups were being successfully completed across the enterprise, let alone break that down by modality, site or timeframe. Having that level of visibility allows us to identify bottlenecks in scheduling, understand which service lines are driving the most follow-up activity, and target process improvements where they will have the greatest impact."

In addition to quality and safety benefits, the health system has seen a measurable financial impact.

"In the first six months, we estimate approximately $1.7 million in revenue is attributable to follow-ups captured and completed through the Inflo-supported workflow," Mohanty said. "That figure reflects downstream imaging, visits and procedures that might otherwise have been missed or shifted to external providers if follow-up had not been actively managed.

"We view this financial return as reinforcing – not competing with – our quality goals," he concluded. "Ensuring patients receive recommended follow-up care helps prevent more advanced disease, supports value-based care initiatives, and strengthens relationships with patients and referring providers. The fact we can demonstrate a clear ROI also makes it easier to sustain and scale the program, invest in additional navigation resources, and expand to new sites and clinical domains."

Follow Bill's health IT coverage on LinkedIn: Bill Siwicki

Email him: [email protected]

Healthcare IT News is a HIMSS Media publication.

WATCH NOW: In 2026, AI will power change in RTLS

Sentara gains $1.7M with AI-based workflow and analytics tool

Healthcare leaders express a mix of enthusiasm and caution regarding the evolving role of AI in the industry, projecting a significant year in 2025 that could see both progress and skepticism. Executives emphasize that the effective use of AI hinges on thoughtful integration into existing clinical environments, prioritizing solutions that address substantial problems over merely chasing the latest trends.

Phill Tornroth, VP of technical strategy at Elation Health, reflects on the current state of AI, suggesting that while there are thrilling possibilities, we may indeed be in a bubble. He draws parallels to the internet revolution, observing that while some startups showcase impressive capabilities, many may struggle to become viable businesses. He points out that the real challenge lies not in the technology itself but in integrating it effectively within healthcare systems.

Matt Cybulsky, managing director of healthcare at Catalant, believes AI is still in its nascent stages, cautioning against drawing comparisons to past tech bubbles like the dot-com era. He acknowledges that while the technology is rapidly advancing, there are concerns regarding inflated valuations and the environmental cost of extensive data center operations. The simultaneous pursuit of innovation and sustainability is an essential conversation moving forward.

The rapid surge in AI’s capabilities raises questions about resource allocation, with data centers consuming vast amounts of water and energy. Cybulsky notes a potential for waste if companies do not properly manage hardware investments amid an evolving landscape. He encourages a careful watch on market developments, asserting that the narrative is more about competitive fervor than a conventional bubble.

Adam de La Zerda, CEO of Visby Medical, points out that while there may be inflated expectations surrounding some AI companies, the underlying technology holds significant potential in healthcare. He emphasizes the importance of adhering to rigorous standards and using AI to address critical patient needs efficiently rather than succumbing to the hype.

Anu Sharma, founder of Millie, echoes that while AI holds transformative potential, excessive valuations in fundraising efforts indicate a need for market correction. She predicts that future winners will be those companies that effectively leverage data and create deep-rooted customer relationships through integrated AI solutions.

Dr. Julius Bruch, CEO of Isaac Health, observes that the excitement surrounding AI may have exceeded its current clinical applicability, particularly in cognitive health, where steady progress has been noted instead of radical disruption. He suggests that managing expectations will be key to navigating this landscape responsibly.

Kara Egan, CEO of Teal Health, recognizes AI's allure but stresses the importance of critical inquiry and cautious exploration of its applications to ensure that it serves real-world needs effectively.

Edmund Jackson, co-founder of UnityAI, recognizes a disparity between the hype surrounding AI and its actual capabilities, stressing that companies focusing on fundamental value creation will fare better long-term.

Eirini Schlosser, CEO of Dyania Health, notes a disconnect in some AI advancements that do not fully grasp the complexities of healthcare. However, she highlights the necessity of leveraging AI to manage large datasets effectively, emphasizing the need for solutions rooted in clinical understanding.

Sanjay Doddamani, founder of Guidehealth, argues that rather than a typical bubble, we are seeing rapid technological advancements alongside fundamental structural changes in healthcare that necessitate integration of AI for improved efficiency.

Emily Greenberg, co-founder of Joy Parenting Club, suggests that while the initial excitement may dim, the transformation process will continue to evolve. She predicts a future where AI enhances human capabilities rather than replaces them, promoting more responsible and contextual applications.

Stephen Smith, co-founder of NOCD, asserts that we are only at the beginning of realizing AI's potential. He cites recent advancements in AI-driven search functionalities as a testament to the technology's rapid impact and is optimistic about the ongoing evolution in the sector, regardless of occasional overvaluations or inflated promises.

Healthcare executives discuss the AI bubble, segment two.

A sunlit hospital boardroom with senior healthcare executives gathered around a polished wooden table. Above them floats a translucent bubble stamped with “AI,” its surface hairline-cracked as if about to burst. On the table lie stethoscopes, medical reports, and open laptops displaying patient data charts. The leaders’ faces show a mix of concern and curiosity as they lean in, mid-debate.

Healthcare leaders debate the AI bubble, part 2 - MobiHealthNews

- Healthcare executives see AI's potential to enhance workflows and patient outcomes by 2025.  
- Successful AI implementation requires thoughtful integration and a focus on meaningful healthcare problems.  
- Opinions vary on AI's market status, with some viewing it as hype, others as structural change.

Alongside warnings around AI, healthcare executives say the technology has long-term potential to improve clinical workflows and patient outcomes, making 2025 a year of both excitement and cautious scrutiny.

Healthcare executives told MobiHealthNews that successful AI implementation depends on thoughtful integration, tools grounded in clinical expertise and a focus on meaningful problems rather than chasing novelty.

Here is part 2 of healthcare leaders’ perspectives on whether we are in an AI bubble:

Phill Tornroth, VP of technical strategy at Elation Health

I do think we’re in a bubble. For the past couple of years when anyone’s asked me if I think AI is going to completely transform the way we live and work or is it over-hyped, my answer has always been "yes." I think the comparisons to the internet are apt. Both things can be true at the same time. I think there are a lot of health tech startups that are inspiring the industry with what these models can do, but many of them won’t turn into sustainable businesses because for a lot of this the technology isn’t the hard part. The hard part is thoughtfully designing and integrating this technology into clinical environments and earning the trust of healthcare providers. Healthcare is the hard part.

Matt Cybulsky, managing director of healthcare at Catalant

AI is in its early stages and it's useful to distinguish this from other forms of bubbles like the dot-com bubble. This isn't to say there's no trouble afoot in the future–we're seeing a massive expansion of the technology with some arguing valuations are inflated, not to mention the financing required to keep up with competition domestically and internationally. It's early in the AI revolution with plenty of yellow flags.

Data centers are consuming immense amounts of power and water to operate processing, the impact on electrical grids and water resources isn't to be understated. Each use of AI may represent many liters of water used; the rapid growth has taken on a Zoroastrian character between resources and possibilities. With the massive infrastructure investment and desire to create annual revenues, a threat of physical obsolescence with the hardware is a curious risk to observe. Digital rot, like a spoiled apple, looms as an obvious risk ahead for the barrel of speculative growth estimations.

Instead of seeing this as an AI bubble with a threat of value destruction, it may be more relevant to see this as competitive fever only relieved by stringent diligence and reality testing for the markets ahead, especially in healthcare.

We're at a reckoning, not a bubble, I'd say (credit Peter Thiel). Technology advances incrementally with a few steps forward and many steps back. With a wild adoption curve and plenty of threats and rewards ahead of us all–all I can really advise is to watch closely, stay grounded and expect one of the most exciting and challenging tech stories of our time to continue to unfold.

If there were a bubble, and it was to burst, I'd love to be the guy who said "I told you so." For today, I'm simply glued to the financial press to see what happens next!

Adam de La Zerda, CEO and founder Visby Medical

There may be a bubble in some of the hype and valuations of some companies, but not in the underlying usefulness of these tools, especially in healthcare. In our world, strict regulation and privacy requirements act as a natural speed limit, which means the risk is less about runaway AI and more about under-using technology that could reduce delays, errors and costs. The healthy path is to stay grounded in problems that matter, such as getting a woman from an at-home STI result to appropriate treatment quickly and to treat AI as one tool among many to make that journey better.

Anu Sharma, founder and CEO of Millie

The potential of AI in healthcare is undeniable, but the fundraising rounds have become increasingly supersized at sky high valuations without much differentiation in many cases, including to an AI bubble. Because of this, there must be some shake out for sure, and two kinds of winners will prevail: the AI products that stand on real data moats or go-to-market motions that make them incredibly sticky with customers and tech-first care delivery companies that embed AI deeply into their clinical workflows to deliver better outcomes, experience, with much lower cost structures vs. incumbents.

Dr. Julius Bruch, CEO of Isaac Health

From our vantage point, some of the market enthusiasm has definitely outpaced what’s clinically ready, but the underlying value is real. In cognitive health specifically, AI has already proven useful in screening and workflow support. If there’s a bubble, it’s likely around expectations. We’ve seen steady, incremental gains rather than dramatic disruption and that’s a healthier trajectory for clinical fields like ours that require caution and evidence.

Kara Egan, founder and CEO of Teal Health

AI is the shiny object of the moment, but it has real potential to help businesses on all levels, so the excitement is understandable. Like any new technology, especially one that is so dependent on its sources, we need to ensure we are asking the right questions before jumping all the way in.

Edmund Jackson, cofounder and CEO of UnityAI

There’s definitely a hype bubble, but not a technology bubble. We are simultaneously overestimating what AI can achieve in the short term and underestimating it in the long term. Companies that stick to the fundamentals of how value is actually created for clients, including with AI technology, will survive the long term.

Eirini Schlosser, founder and CEO of Dyania Health

There is a bubble in certain corners of AI–mostly where tools are being built without a deep understanding of healthcare’s complexity, or where marketing outpaces evidence. But the need to make sense of vast volumes of clinical text is not speculative; it's structural. Even the best-trained clinical teams cannot manually process millions of longitudinal records or match patients to opportunities at the pace modern medicine demands.

When AI is used to address those foundational constraints–and when it is developed with clinical input, transparency and safety frameworks–it feels less like a bubble and more like long-overdue modernization. The challenge ahead is distinguishing meaningful, validated systems from AI-washing. As the hype settles, rigor will matter more than velocity.

Sanjay Doddamani, founder and CEO of Guidehealth

We are not in a traditional AI bubble. Technology is advancing quickly, and the underlying pressures in healthcare are largely structural. Health systems badly need improved efficiency, stronger documentation, support for population and simultaneously precision-level patient management, as well as a relief from administrative burdens. These needs align closely with areas where AI has demonstrated tremendous promise.

The perception of a bubble often arises when organizations expect transformation without redesigning workflows or establishing clinical oversight. Solutions that depend solely on technology without complimentary operating models are unlikely to prevail. What will persist are models grounded in clinical governance, operational clarity, workflow integration and measurable performance.

Emily Greenberg, cofounder and president of Joy Parenting Club

The hype will cool, but the transformation underneath will continue to deepen. What we are seeing is not a bubble; it is a shift toward better discernment. The focus is moving from capability to responsible application. The next wave of leaders will build AI that is human-literate, meaning it understands tone, nuance, timing, cultural context and trust. Long-term value will come from using AI to extend human capacity rather than replace it.

Stephen Smith, cofounder and CEO of NOCD

I don’t think we’re in an AI bubble. If anything, we’re just beginning to see the surface of what’s possible. Look at how quickly AI has transformed something as fundamental as search in the past year, that’s one of the simplest use cases, and it’s already changed how billions of people access information every day. There will always be overvalued companies or overpromised products, but the underlying technology is real, and the pace of adoption is accelerating.

Healthcare leaders debate the AI bubble, part 2

In the past year, we have shared numerous stories focusing on agentic tools, advancements in medication management, guidance for medical devices, and the evolving landscape of technology in healthcare—a phenomenon some are calling the "great tech reckoning."

Recently, the U.S. Department of Health and Human Services issued a request for information to gather insights from stakeholders on how artificial intelligence (AI) and machine learning (ML) can effectively enhance health systems and be integrated into everyday healthcare practices.

Jim O’Neill, the Deputy Secretary at HHS, emphasized that the push for AI adoption should align with the genuine needs and experiences of both developers and healthcare providers: "Our efforts to accelerate AI adoption must be guided by the real needs and experiences of those developing these tools and delivering care."

AI's growth in the healthcare sector has been noteworthy over the years. By 2025, it has become evident that AI's adoption within healthcare has outpaced its use in other industries, offering practical applications and demonstrated return on investment (ROI).

The integration of AI and ML spans various areas—from billing automation to clinical documentation, and across specialties like radiology and cardiology. Hospitals and medical practices nationwide are increasingly adopting these technologies, enhancing provider experiences, promoting patient engagement, optimizing workflows, strengthening cybersecurity, facilitating drug discovery, and personalizing treatment options.

At recent HIMSS AI events held in Brooklyn and Chicago, healthcare and IT executives have begun to grasp the essential elements required for effective automation, including data governance and change management strategies.

As technology continues to revolutionize numerous aspects of healthcare, 2026 is poised to be another significant year. Reflecting on the previous year, several standout AI-related stories captured considerable attention.

One notable narrative discussed Kaiser Permanente's new AI head, who highlighted "two fundamental shifts" that AI would drive in healthcare by fundamentally altering clinician roles. Dr. Daniel Yang, the VP of AI and emerging technologies, reiterated that while AI should complement human expertise, it should never substitute it. He emphasized the necessity of evaluating any AI tool before it is implemented.

In a much-anticipated presentation at its annual Users Group Meeting, Epic revealed a range of new AI-driven tools, including a clinical assistant, a patient-facing chatbot, and an automated revenue management system. Dr. Jackie Gerhart, Epic’s chief medical officer, showcased innovations like generative medical event models designed to help clinicians enhance patient care.

Reflecting on the advancements anticipated in 2025, one CEO noted the "great tech reckoning," suggesting that the real transformation would largely occur behind the scenes, improving the operational frameworks essential for effective patient care and overall system performance.

Hackensack Meridian, in collaboration with Epic, demonstrated significant advancements by automating medication instruction mapping, which minimized manual data entry. This integration is expected to enhance medication safety and health outcomes for patients as it scales.

The FDA also provided new draft guidance for developers of AI-enabled medical devices. Troy Tazbaz, director of the Digital Health Center of Excellence at the FDA, acknowledged the unique considerations that come with these innovations.

At the University of California, San Francisco, a groundbreaking AI system was designed to support oncologists in providing personalized cancer treatments. The initiative aimed to integrate national guidelines and patient information with local best practices, ensuring that every patient receives evidence-based care.

At the HIMSS25 conference, Google Cloud introduced innovative generative AI technologies aimed at improving healthcare access and information management. Similarly, Mount Sinai launched a new center dedicated to AI in pediatrics, focusing on enhancing diagnostics and personalized care for adolescents.

Children's National's Chief Data and AI Officer, Alda Mizaku, shared strategies for leveraging AI to expedite patient diagnosis and treatment plans while ensuring customization and comprehensive documentation.

Despite AI's promise in reducing billing errors, challenges in system integration remain. A recent survey found that many healthcare workers spend considerable time rectifying billing mistakes. Kyle Ryan, chief product and technology officer at Tebra, urged healthcare IT leaders to prioritize automation solutions that provide detailed estimates and clear communication to bridge the gap between clinical and financial processes.

The 10 most popular AI articles of 2025

A sunlit modern desk with an open digital magazine titled “Top 10 AI Stories of 2025.” Scattered around are ten colorful story cards, each bearing a unique icon—a humanoid robot silhouette, a DNA helix entwined with circuits, a futuristic city skyline, an autonomous Mars rover, etc. Warm morning light streams through a nearby window, casting soft shadows on a sleek, minimalist surface.

AI's impact on the revenue cycle at Baylor Scott & White Health

A bright, sunlit hospital finance office where a healthcare administrator in professional attire reviews AI-powered revenue dashboards on a sleek monitor. Digital data streams flow from the screen into upward-trending dollar symbols hovering above the desk. Baylor Scott & White Health logo is prominently displayed on the wall behind. Colleagues exchange relieved, optimistic smiles in a warm, airy space.

How AI transformed revenue cycle at Baylor, Scott & White Health - Healthcare Finance News

- Baylor Scott & White Health expects over $40 million annual revenue improvement from AI.  
- The health system partnered with Accuity to enhance revenue cycle efficiency using AI.  
- Six months after deploying AI technology, BSWH captured an additional $20.6 million in revenue.  

The health system expects over $40 million in net revenue improvement on an annualized basis.

Baylor Scott & White Health’s use of AI in the revenue cycle is expected to result in an annualized $40 million net revenue improvement.

The large Texas health system of 52 hospitals was able to accomplish this through a partnership with Accuity, a physician-led technology company. BSWH started with a two-month pilot of Accuity’s Amplifi AI technology in February at its flagship hospital and realized strong ROI, according to Sarah Knodel, senior vice president of Revenue Cycle.

In May, BSWH deployed Amplifi AI technology at 26 of its locations.

Six months later in November, BSWH had captured an incremental $20.6 million in revenue, according to Knodel. In 2026, that number is projected to be $45 million, she said.

Annualized, that’s more than $40 million in net revenue improvement, she said.

The Case Mix Index impact has shown an average 3.5% improvement, leading to greater reimbursement.

“BSWH views AI solutions as having great potential to enhance its revenue cycle efforts,” Knodel said. “AI has added efficiencies that allow resources to be freed up to focus more on quality capture and other key initiatives.”

WHY THIS MATTERS

Accuity partners with hospitals and health systems on clinical documentation improvement (CDI) and coding review.

“Mid revenue cycle is where you find opportunities,” said Candice Daszewski, chief client officer for Accuity.

AI focuses on CDI and coding, pointing out inconsistencies in the patient chart that can prompt a query to the physician. This results in improved clinical documentation in an estimated 8% to 9% of cases.

Amplifi analyzes complete patient chart data, Daszewski said, as compared to the Diagnosis Related Group, or DRG, a classification system for medical conditions.

Leveraging AI in algorithms is creating a more efficient process, Daszewski said.

“It gives partners an AI solution without having to own it, without concerns of accuracy,” she said.

Knodel said her team felt like it had a strong CDI program but wanted confirmation of that as well as to go further to determine what dollars they could be leaving on the table.

“Baylor Scott & White was looking to optimize net revenue, reduce the cost to collect,” Knodel said. Also, the health system wanted to know, “How do we improve the overall patient experience?” she said.

The automation journey began over 10 years ago with Robotic Process Automation bots. That evolved to layer in machine learning, natural language processing and now agentic AI for more advanced capabilities, Knodel said.

“It’s really been exciting, they have validated we have a strong program,” she said. “More broadly, how does it augment and enhance our effectiveness?”

THE LARGER TREND

In June, the conversation around AI and revenue cycle was part of an HFMA panel presentation between Daszewski and clinical and revenue cycle leaders from Prisma Health, Mass General Brigham and MultiCare Health.

One key takeaway, said Daszewski, is the influence AI-driven automation is having on the revenue cycle.

Email the writer: [email protected]

How AI transformed revenue cycle at Baylor Scott & White Health

Rural healthcare institutions are increasingly embracing artificial intelligence (AI) technologies, dispelling initial apprehensions and finding unique pathways to integrate these innovations with their electronic health records (EHR). This can happen either through swift implementations or by gradually fostering acceptance among staff.

Typically, smaller and rural healthcare providers face challenges related to resources when it comes to adopting advanced operational improvements, particularly AI. However, those that successfully integrate these technologies are seeing enhancements in operational efficiency and a rise in employee morale, regardless of whether their adoption strategy is immediate or measured.

In discussions with two rural hospitals, we observed distinctly different methods in their AI integration experiences. While financial constraints can hinder the shift to modern technologies, both providers demonstrated that even facilities with limited resources can effectively leverage state-of-the-art technology in ways that suit their operational styles.

Patterson Health Center, a critical access hospital situated in Harper County, Kansas, opted for a gradual approach in its AI adoption, prioritizing a cultural transition to enhance staff work-life balance. By making AI functions within its Oracle EHR optional and ensuring open lines of communication about its usage, the hospital successfully diminished "tech anxiety," leading to a smoother acceptance of the new system throughout its small team.

Conversely, CGH Medical Center, a municipally funded, 99-bed facility in Sterling, Illinois, embraced a more aggressive strategy by integrating the Epic EHR alongside the Launchpad AI Starter Kit during a major system migration. This simultaneous upgrade allowed the hospital to alleviate documentation burdens on staff while facilitating a more intuitive learning experience for a new system.

While one facility benefited from a cautious and stepwise implementation, the other thrived on a comprehensive approach. As Jolee Parks, CGH's director of health informatics, noted, "You don't have to necessarily pilot it in areas and start small; you can just kind of go 'Big Bang.'"

Building trust in AI tools among patients in rural areas can be challenging for smaller healthcare providers. Sarah Teaff, the CEO of Patterson Health, pointed out, "Oftentimes in rural communities, we’re afraid of that pushback," emphasizing the strategic focus on staff wellbeing over immediate financial gains.

Patterson Health, a 16-bed critical access hospital, collaborated with Oracle Health's CommunityWorks to implement the Health Clinical AI Agent, which aids in automating the more tedious aspects of documentation. This gradual rollout—combined with the optional nature of AI for documenting patient visits—helped alleviate physician burnout and increased valuable interaction time with patients.

The introduction of AI allowed providers to shift from the monotonous task of "clicking through boxes" to concentrated, meaningful patient interactions. Teaff noted that the AI tool effectively filtered out irrelevant discussions, which helped maintain the quality of clinical documentation.

Integrating the AI system into its previously enhanced Oracle EHR was user-friendly and did not necessitate extensive retraining, although staff received regular demonstrations. Throughout these sessions, the important message conveyed was that the decision to use AI tools ultimately rests with both the providers and the patients. As a result, Patterson Health achieved a commendable 84% acceptance rate among patients regarding AI usage during visits.

The most tangible outcome of the integration was a reduction in documentation time—falling from 20 minutes per patient to just 12 minutes—while the time spent in the EHR decreased from 15 minutes to 9 minutes. For physicians employing AI tools, this shift translated into reclaiming one to two hours of their day, drastically cutting down on the time required for after-hours paperwork.

"Doctors are able to focus on their core competencies, which is engaging directly with their patients," Teaff stated.

On the other hand, CGH Medical Center filled a critical gap by launching its new Epic EHR with Microsoft's Nuance DAX AI, which took effect on November 3. This integration is designed to enhance scalability and address staffing shortages.

Rather than utilizing Epic's Community Connect program to share EHR costs with other small hospitals, CGH transitioned from a highly customized, outdated legacy system to a new standalone EHR. This move was aimed at addressing painful documentation processes that took up excessive physician time.

Currently, CGH's AI capabilities are focused on outpatient settings, where it facilitates order creation during consultations by drafting notes automatically based on conversations with patients. Even within a brief eight-week span, CGH has reported a significant drop in clinical documentation burdens and improvements in coding and reporting efficiencies.

Although CGH lacks specific data on error reductions from AI-integrated claims processing, early indications suggest that machine learning has already streamlined note creation and diagnosis coding, thus alleviating staff burdens.

The same advantage applies to data analytics; Parks noted that the hospital now has improved self-service reporting capabilities, allowing users to generate reports simply by engaging with the reporting module.

A notable early achievement for CGH was the identification of 310 incidental radiology findings from the first eight weeks post-launch, showcasing the value of AI in extracting relevant medical details from otherwise lengthy notes.

The simultaneous deployment of a new EHR and ambient listening tools expedited the learning curve typically associated with such transitions. Parks mentioned their strategic intent in determining which features to activate during rollout, contributing to their successful implementation.

With the eagerness of providers to utilize the new system, CGH designated specific responsibilities for staff, where input during documentation that was previously reliant on support staff became part of the physicians' workflow.

Benefiting from AI, cardiologists, for example, can now articulate procedures, which generates a narrative note that needs only minor adjustments before finalization.

CGH also appointed physician advocates to spark enthusiasm about AI, and those who initially supported the transition were impressed with the efficiency improvements. "Feedback indicates that providers would be hard-pressed to return to prior methods; they love it," Schaab remarked.

As Teaff indicated, while financial considerations are relevant, the advantage of AI for rural healthcare organizations lies in reclaiming valuable time for healthcare providers. "We did substantial outreach with stakeholders before going live, ensuring that the team understood they had options; this wasn’t a case of all or nothing."

At Patterson Health, clinicians appreciate the flexibility to alternate between the AI system and traditional data entry as needed. Both Parks and Schaab encouraged fellow rural healthcare institutions to be intentional in their AI tool implementation, always emphasize human oversight, and educate both staff and patients about data privacy and tool accuracy.

"AI is here to stay," Teaff affirmed. "It’s crucial to embrace these changes and start familiarizing ourselves with them."

A story of two approaches to AI implementation in rural areas

A split-screen rural panorama at dawn under warm, golden sunlight: on the left, a sleek agricultural drone hovers above waving wheat, as a weathered farmer in overalls checks a soil‐moisture sensor; on the right, a sunlit village square with a solar-powered AI kiosk, two adults leaning over a tablet showing predictive crop charts; a winding dirt road connects the two scenes, rolling hills and scattered barns in the distance, natural colors and storytelling composition.

A tale of two rural AI implementation strategies - Healthcare IT News

- Rural hospitals are adopting AI tools cautiously, integrating them with electronic health records.  
- Patterson Health implemented AI gradually, reducing physician burnout and enhancing patient interactions.  
- CGH Medical Center adopted AI rapidly during EHR migration, achieving quick operational efficiency gains.

Rural hospitals are overcoming fears of artificial intelligence and adopting tools that integrate with electronic health records on their own terms – whether that's all at once or by cultivating buy-in slowly and deliberately.

Small and rural providers often don't have the resources to roll out sleek new operational enhancements, especially artificial intelligence capabilities. But those that do are finding that AI is helping improve key operational metrics and gaining staff enthusiasm – whether they implement the tools quickly or gradually.

We spoke recently with two rural hospitals that structured their AI integrations very differently. If financial considerations can sometimes be a barrier to adoption, their experiences and early results prove that even lesser-resourced organizations can successfully implement leading-edge technology in a way that works for them.

Patterson Health Center is a critical access hospital (CAH) in Harper County, Kansas. It focused on a gradual culture shift to implementing AI to improve staff work-life balance. By making AI tools in its Oracle EHR optional – and actively communicating with providers about its use – the rural provider helped reduce "tech anxiety" and achieved adoption across its small organization.

CGH Medical Center, meanwhile, is a municipally owned, 99-bed hospital located in Sterling, Illinois. It used the opportunity of a major migration to Epic to make use of that vendor's Launchpad AI Starter Kit. By upgrading to a new EHR and introducing AI at the same time, the rural hospital was able to achieve reductions in staff documentation burden while lowering the learning curve for a new system.

While a slow and deliberate experience worked for one hospital, an all-at-once approach worked for the other.

"You don't have to necessarily pilot it in areas and start small," explained Jolee Parks, CGH's director of health informatics. "You can just kind of go 'Big Bang.'"

Steady approach builds buy-in

Still, earning rural patient trust with newer AI technologies can be plenty challenging for some smaller, rural organizations.

"Oftentimes in rural communities, we’re afraid of that pushback," said Sarah Teaff, Patterson Health's CEO. "We used it as more of a wellbeing-type strategy for our providers rather than a return on investment."

Patterson, a 16-bed CAH, worked with Oracle Health's CommunityWorks to implement the vendor's Health Clinical AI Agent and help automate the "hard work" of charting.

Through a gradual introduction and the optional use of AI for patient notes, the rural health center used a "long runway" approach to AI to reduce physician burnout and improve face time with patients.

AI removed the barrier of "clicking through boxes," allowing providers to be more "one-on-one" with patients, Teaff said. The tool’s ability to "weed out" non-clinical conversation – small talk about a patient's child's basketball game, for instance – and target the relevant medical data helps maintain the quality of AI notes the staff reviews.

Adding the clinical agent into its previously upgraded Oracle EHR was a "flip switch" experience and did not require a steep learning curve or extensive retraining, though the CAH's staff was also provided frequent demos.

During these sessions and other staff communications, the center repeated the message to staff that using AI is both the doctor's and patient's choice. Despite initial fears that a rural community might be skeptical of AI, the rollout was also successful from a patient perspective.

Tech training stressed the need for providers to obtain explicit consent from patients before using ambient listening and documentation tools during visits, Teaff said. To date, Patterson Health has achieved 84% agreement to use AI during patient encounters.

While Teaff said the most measurable success was using the new clinical AI agent to record and summarize visits, an Aha! moment came when providers using the AI tools realized they could also dictate notes during their commutes, Teaff said.

Per patient, documentation time dropped from 20 to 12 minutes, while time spent in the EHR per patient decreased from 15 to 9 minutes.

Use of AI has also given back to Patterson Health's doctors using the AI tools roughly one to two hours each day, significantly reducing their after-hours documentation time.

"We can see the timestamps of when they're in the [EHR], those that are having to do things after hours," she said.

Teaff said they are hopeful that all seven of its providers will adopt the tools.

"Hopefully with these first small wins with our providers who are utilizing it, we'll slowly get 100%," she said. "They need to be doing what they're good at, which is having that face time with their patients."

'Rip the Band-Aid' for faster gains

CGH Medical Center, a 99-bed hospital about two hours' drive west of Chicago, launched a new Epic EHR with Microsoft's Nuance DAX AI on November 3 to enhance operational scaling and solve staff shortages.

While Epic offers smaller community and rural hospitals a chance to share an EHR and its costs through its Community Connects program, CGH opted to migrate from a highly customized, 20-year-old legacy system with components from Cerner and NextGen to a new stand-alone EHR with the vendor's AI Starter Kit that would easily scale across the hospital's various departments and specialties.

"There was a lot in terms of the documentation requirements, things that we're asking them to do regularly – more discrete data fields," explained Ben Schaab, the center's chief financial officer. "There were a lot of pain points in terms of the flow of data and a lot of pain points in terms of physicians' time in charts."

The AI, only available in CGH's outpatient setting at this time, will "tee up orders through that conversation with the patient," he said."It'll prompt them for certain things that they discuss with the patient," including tests or prescriptions, and draft notes automatically.

Early results already show a drastic reduction in clinical documentation burden and immediate gains in coding and reporting. CGH does not yet have data on documentation burdens reduced or error coding rate drops from integrated AI claims processing tools.

But with machine learning automatically writing notes and extracting diagnosis codes for ancillary and emergency department visits, it does alleviate staff and staffing burdens.

The same is true for creating data dashboards and reports.

"We have a lot more ability to do self-service reporting," Parks said. "You can basically have a conversation with the reporting module and say, 'I'm looking for XYZ,' and it can build the report for you or build the dashboard for you."

One standout success CGH was able to share is that in just the first eight weeks of being fully live with the new system, it identified 310 incidental radiology findings by pulling out discrete findings in paragraphs of notes and then prompting follow-up with patients.

Notably, launching a new EHR and new ambient listening tools helped CGH advance quickly beyond a typical new EHR learning curve.

"We were intentional about what things we wanted to turn on and how we wanted to roll them out and use them," said Parks. "I think it paid off for us to do it all at once."

Because CGH wanted to launch its new EHR with AI documentation available to everyone – including nurses with challenging workflows like end-of-shift summaries – Schaab and Parks piloted ambient documentation tools with eager providers earlier in the year.

With the migration, CGH providers were then designated certain pieces of the new workflows, "where maybe a support staff had previously been more engaged," Parks explained.

That way, "they didn't have to spend so much time learning where to document certain things" in a brand new EHR.

With AI, cardiologists, for example, can "speak through what they're doing and then get a procedure narrative note that they can just modify, and move on."

CGH also tapped AI physician advocates to spark colleagues' interest, and even those superusers were shocked by some of the ways AI could enhance workflows.

"We did have some at-the-elbow support physicians that we brought in just to give a little bit of extra support to our providers," Parks said. When they saw the new AI features, "One of them commented, 'I would go back to being a hospitalist after seeing the hospital course draft note.'"

Finding their champions also meant CGH didn't need a full "all-in mandate" to start with AI – the word simply spread.

"From the feedback we're getting from [CGH] providers, I think we'd be hard-pressed to take it away from them now. They love it," Schaab added.

Embracing AI in rural healthcare

While financial return is important with new AI technologies, Teaff suggested focusing on the human element when making the case to staff: "We want to give you time back in your life."

"We did a lot of talking with the stakeholders and the providers before we went live," she said. "We made sure they knew that they had choices. This was not an all or nothing mentality."

At rural Patterson Health, doctors know that they can switch between the AI tool and traditional typing.

Parks and Schaab said that while their "Big Bang" rollout at CGH might seem intense, and while AI can be "scary" to both rural doctors and patients, the benefits are considerable. They recommend that their peers:

Be intentional about how AI tools are rolled out

Always emphasize that a human is in the loop

Educate staff and patients about data security and tool accuracy

"AI is coming," said Teaff. "This is something we need to step into and start playing around with and getting accustomed to."

Andrea Fox is senior editor of Healthcare IT News.

Email: [email protected]

Healthcare IT News is a HIMSS Media publication.

A tale of two rural AI implementation strategies

The administration of President Donald Trump has focused significantly on the integration of artificial intelligence within various government agencies. This strategic move aims to enhance operational efficiency and modernize services across the board. By prioritizing AI technology, the Trump administration hopes to improve decision-making processes and drive innovation in public sector operations.

3 ways AI is being utilized in Medicare

A bright, sunlit clinic office where a doctor and a senior patient sit across a desk. On the tablet screen between them are three large, warm-colored icons: a heart-rate waveform over a bed for real-time monitoring, a claims form with a green checkmark for streamlined billing, and a pill bottle entwined with a DNA helix for personalized prescriptions. A small U.S. flag stands on the windowsill, light flooding in to create an optimistic, human-centered scene.

Here are 3 ways AI is being used with Medicare - Modern Healthcare

- The Trump administration prioritized the integration of artificial intelligence in government agencies.  
- Efforts focus on enhancing efficiency and effectiveness through AI technologies.  
- Agencies are exploring innovative applications to improve public services and operations.  

President Donald Trump’s administration has placed a heavy emphasis on leveraging artificial intelligence across agencies.

(Modern Healthcare Illustration/Getty Images)

Here are 3 ways AI is being used with Medicare

Artificial intelligence is taking on a significant role in the realm of healthcare, influencing decisions ranging from treatment predictions for patients to determining insurance coverage. This transformation is beginning to unfold in New Jersey, where public health experts acknowledge both the potential advantages and considerable challenges that accompany this shift.

As reported by New Jersey Monthly, researchers and healthcare professionals across the state are examining how AI can enhance patient care, expedite diagnoses, and allow healthcare providers to devote more time to patient interactions rather than administrative duties.

Hospitals and research institutions have already integrated AI tools for various applications, such as analyzing patient records, crafting customized treatment plans, and aiding clinicians in quickly identifying critical information from medical scans and tests—often achieving results more swiftly than manual methods.

These innovations can streamline workflows significantly and may improve early diagnosis and personalized care for individuals battling severe health issues. Meanwhile, a nationwide initiative is set to impact New Jersey residents on Medicare. Federal health authorities are advancing a pilot project that will leverage AI to inform decisions on whether specific procedures and services will be covered by Medicare.

However, critics express concerns regarding this approach, suggesting that the deployment of algorithms in determining prior authorizations and coverage could lead to delays and rejections that might prove elusive for patients and medical professionals to contest.

This "AI involvement in coverage decisions" model aligns with a broader trend in which government payers and insurers increasingly use automated systems to process claims and authorize services, a practice that has already faced scrutiny over its potential to hinder patient care.

Health policy analysts have noted several concerns:

- **Care Delays:** Algorithms focused on identifying "low-value" services could impede timely approvals for essential procedures, particularly as patients or doctors may struggle to challenge automated determinations.
  
- **Transparency Issues:** Proprietary algorithms might not provide clear rationales for decisions, leaving both patients and healthcare providers unclear on how to proceed.

- **Bias Risks:** Emerging studies indicate that AI models could generate different care recommendations based on socioeconomic or demographic variables present in the training data, thereby potentially exacerbating existing health disparities.

Conversely, supporters argue that judicious use of AI can alleviate clinician stress by managing data-intensive tasks, accelerate diagnosis, and tailor treatments with greater precision than traditional methods—benefits that could enhance patient outcomes, provided these technologies are employed under rigorous human supervision.

The impact of AI's increasing presence in medical decision-making on patients in New Jersey will largely depend on how these systems are managed, their level of transparency, and the maintenance of robust human oversight in care-related decisions.

As this technology continues to integrate into the healthcare landscape, patients, advocates, and healthcare providers are urging for the establishment of safeguards that ensure access, equity, and accountability remain at the forefront of healthcare delivery.

Artificial Intelligence Integrates into New Jersey's Healthcare System

A sunlit modern hospital room in New Jersey, a doctor in scrubs reviews patient scans on a sleek touchscreen console marked “AI,” the patient sits calmly on the exam table, a New Jersey state flag hangs on the wall, warm natural light floods through large windows, soft tones underscore the blend of advanced technology and compassionate care.

AI Moves Into New Jersey’s Healthcare System - RLS Media

- AI is being explored in New Jersey to enhance health-care decision-making and efficiency.  
- Concerns arise about potential delays, lack of transparency, and bias in AI algorithms.  
- Proponents advocate AI's ability to reduce burnout and personalize treatments under human oversight.  

By: Richard L. Smith



Artificial intelligence is increasingly being used to help make real health-care decisions — from predicting which treatments patients might need to helping decide what services insurers will cover.



That shift is coming to New Jersey, and public health experts say it brings both potential benefits and serious concerns.





According to reporting from New Jersey Monthly, academic researchers and health-care leaders across the state are exploring how AI can improve care, speed diagnosis, and help doctors focus more on patients rather than administrative tasks.



AI tools are already being used in hospitals and research centers to analyze medical records, support personalized treatment plans, and assist clinicians in identifying critical findings from scans and tests faster than manual review alone.



These applications can improve workflow efficiency and, in some cases, enhance early detection and tailored care for individuals facing serious illness.

At the same time, a major shift underway nationally could directly affect New Jersey residents enrolled in Medicare. Federal health agencies are planning a pilot program that will use AI to support decisions about whether Medicare will cover certain procedures and services.



Critics of the program warn that using algorithms for prior authorization or coverage decisions — even if intended to reduce unnecessary care, risks introducing delays or denials that may be hard for patients and doctors to challenge.



This “AI in coverage decisions” model is rooted in broader trends where insurers and government payers rely on automated systems to review claims and authorize services, a practice that already draws scrutiny for its potential to slow care and create barriers for patients.

Potential concerns cited by health policy analysts include:

• Delays in care — Algorithms filtering for “low-value” services could slow approvals for necessary procedures, particularly when patients or providers must appeal automated decisions.

• Lack of transparency — Proprietary models may not clearly explain why a decision was made, leaving patients and doctors uncertain about how to respond.

• Risk of bias — Emerging research suggests that AI models sometimes recommend different care based on socioeconomic or demographic factors in their training data, potentially worsening disparities.





At the same time, proponents argue smarter use of AI can reduce clinician burnout by handling data-heavy tasks, speed diagnosis, and help personalize treatments more precisely than conventional methods, benefits that could improve outcomes for many patients when tools are used under strict human supervision.





Whether AI’s growing role in medical decision-making will ultimately help or harm patients in New Jersey depends on how these systems are governed, how transparent they are, and whether strong human oversight remains at the center of care decisions.



Patients, advocates, and clinicians alike are calling for safeguards that preserve access, fairness, and accountability as this technology becomes more deeply embedded in the health system.

AI Moves Into New Jersey’s Healthcare System

Wendell Bartnick, an attorney at Reed Smith, emphasizes the growing role of artificial intelligence (AI) in healthcare, particularly projecting significant advancements by 2026. This surge will include applications for back-office automation, ambient clinical documentation during exams, claims processing, and clinical decision support systems. However, with these opportunities comes an increase in critical risks related to privacy, security, and legal concerns.

In a detailed conversation with Information Security Media Group, Bartnick highlighted the importance of governance as organizations explore AI's potential within the healthcare sector. He pointed out that while risks associated with AI vary depending on the specific use case, every entity looking to implement AI must thoroughly address governance.

Bartnick recommended referring to the National Institute of Standards and Technology's AI Risk Management Framework as a solid resource to gauge these risks, especially in the absence of comprehensive regulations.

During the interview, he also covered several topics, including:

- Key security, privacy, and regulatory challenges associated with various AI applications in healthcare.
- Issues surrounding agentic AI, mental health chatbots, and the implications of patient encounter recordings.
- The potential risks associated with patients relying on AI for self-diagnosis and treatment.

As a partner at Reed Smith, Bartnick leverages his computer science background to advise clients in diverse sectors, prominently in healthcare, life sciences, and biotechnology. His expertise encompasses data rights, privacy, cybersecurity, technology commercialization, and compliance with regulatory standards, alongside guidance on governance strategies and partnerships, ensuring thorough preparation for the emerging landscape of AI in healthcare.

Healthcare AI Applications: Expansion, Oversight, and Risk Factors

A bright hospital corridor bathed in warm natural light. A doctor in a white coat and a healthcare executive stand side by side, studying a floating transparent display showing a rising “AI Growth” graph. Beside it, a delicate balance scale holds a shield icon labeled “Governance” on one side and a warning triangle labeled “Risk” on the other. Surgical instruments on a nearby tray catch soft sunlight, highlighting both promise and caution in AI-driven healthcare.

AI Use Cases in Healthcare: Growth, Governance and Risk - Bank Info Security

- AI use cases in healthcare will expand significantly by 2026, posing various risks.  
- Governance is crucial for organizations deploying AI technologies in healthcare environments.  
- The National Institute of Standards and Technology offers valuable guidance on AI risk management.  

Healthcare , HIPAA/HITECH , Industry Specific

Attorney Wendell Bartnick of Reed Smith on Critical Considerations

Use cases for artificial intelligence in healthcare will continue to explode in 2026 - including for back-office automation, ambient clinical documentation in exam rooms, claims processing and clinical decision support. So will critical privacy, security, legal and other risk considerations, said attorney Wendell Bartnick of law firm Reed Smith.

"I really think it comes down to governance," Bartnick said in an in-depth interview with Information Security Media Group about the expanding AI opportunities and associated risks in healthcare.

The types and degree of risks vary with different kinds of use cases of AI in healthcare but all entities considering AI deployments need to do their governance homework, he said.

"Until there's more regulation, the place I would go is to look at the National Institute of Standards and Technology's AI Risk Management Framework," he said. "I think that's a really good foundation for just understanding these risks."

In the interview (see audio link below photo), Bartnick also discussed:

Top security, privacy, regulatory, legal risks and pitfalls involving different use case example for AI in healthcare;

Concerns involving agentic AI use, mental health chatbots and patient encounter recordings;

The dangers of patients using AI to self-diagnose and treat themselves.

Bartnick, a partner at law firm Reed Smith, utilizes his background in computer science in counseling clients across a variety of industries, including healthcare, life sciences and biotech. His advice commonly involves data rights and privacy, cybersecurity, commercialization, licensing technology - including AI and machine learning - governance, partnering strategies and agreements, and other regulatory compliance counseling, as well as investigations related to technology and data issues.

AI Use Cases in Healthcare: Growth, Governance and Risk

Thank you for tuning in to Advancing Health! As we wrap up 2025, we're thrilled to highlight key moments from two influential episodes that ignited conversations about enhancing healthcare across the United States. Advancing Health will be back in 2026, bringing you new perspectives and engaging discussions. In the meantime, we wish you a wonderful holiday season and a prosperous, healthy New Year!

Encore: Connecting Through AI and Telemedicine While Reducing the Stigma of Mental Health for Healthcare Professionals

A warm, sunlit modern clinic room where a solo healthcare worker in scrubs sits at a white desk, holding a tablet that shows a friendly telemedicine video call and a simple AI interface with soft, glowing icons. A gentle arc of light “bridges” the tablet screen to a stylized heart-and-brain emblem painted on the wall behind them, symbolizing mental health support and destigmatization. Natural daylight streams through a large window, casting comforting shadows and a hopeful atmosphere.

Encore: Bridging Distances with AI and Telemedicine and Destigmatizing Mental Health for Health Care Workers - American Hospital Association

- Advancing Health shared highlights from two significant episodes before closing out 2025.  
- The podcast aims to inspire dialogue on improving health care in America.  
- Advancing Health will return in 2026 with new insights and discussions.  

Thank you for listening to Advancing Health! As we close out 2025, we’re excited to share highlights from two impactful episodes that sparked dialogue around improving health care in America. Advancing Health will return in 2026 with fresh insights and thought-provoking discussions. Until then, we wish you a joyful holiday season and a healthy, happy New Year!

AI in healthcare News