Recent health conference conversations point to a new wave of priorities in healthcare technology. Over the past few weeks, Caregility executives attended Becker’s, HLTH, and KLAS events where AI and nurse enablement were repeatedly underscored as crucial factors in successful digital health transformation. The events provided valuable insights into how healthcare leaders are using technology to support clinical and operational goals, from addressing documentation burdens to building resilience against security risks. Here are some of our team’s key takeaways.
Mike Brandofino, President & COO Event: Becker’s 9th Annual Health IT + Digital Health + RCM Conference
“Telenursing was top of mind for a lot of folks at Becker’s. There were quite a few discussions about what virtual nursing programs can do and how to effectively deploy technology without negatively impacting bedside teams. Just throwing new technology at problems does not move the needle. Healthcare organizations are taking a more thoughtful approach to collaborative virtual care models.
There’s still a lot of confusion about what to do with AI. There’s concern about the validity of leveraging it in workflows in a way that saves time. The biggest item that healthcare providers are looking for is that documentation piece. Nurse notes, specifically. One session noted that the average nurse spends about four hours of their day documenting care. Many health systems are paying overtime hours for nurses to stay after their shift to do documentation. If health systems are going to get to better ratios, they’re going to need to fix that problem.”
Kedar Ganta, Chief Product & Engineering Officer Event: KLAS Digital Health Investment Symposium (DHIS) 2024
“Financial margin strains and cybersecurity threats dominated conversations at the KLAS DHIS event. There is growing recognition that navigating the next Black Swan event will require strong planning, governance, redundancies, operational resiliency, and investment in technology. Naturally, AI and cybersecurity repeatedly came up in discussions about building the resilience needed to withstand future disruptions. The recent events involving Change Healthcare and CrowdStrike heightened cybersecurity awareness across the industry. Organizations are actively developing playbooks to train for downtimes and implementing workarounds to ensure resiliency.
There is genuine interest and excitement about the potential of AI, but payer and provider attendees didn’t mince words about the challenges. There is little appetite for ‘black box’ AI and a strong call for greater transparency into what goes into AI. There is significant optimism about ambient speech technology for documentation. Most felt comfortable with the application of AI in revenue cycle management, patient communication, and imaging, but remained cautious about adoption in clinical workspaces.
Overall, there is a strong appetite for adopting technology that drives clinical efficiency and provides performance insights while ensuring security. Organizations are streamlining their tech stacks, prioritizing existing vendors, and leaning on their EHR vendors more to support clinical workflow optimization. Now more than ever, potential solutions are being evaluated through a monetary lens. Organizations recognize that virtual care is here to stay and are taking a strategic approach to broader implementation and governance.”
Jenna Walls, VP, Business Development & Strategic Alliances Event: HLTH 2024
“At HLTH, it was clear that AI is taking center stage in healthcare, but it’s important to note the shift in sentiment, particularly compared to just six months ago at ViVE. We’re seeing a more comfortable embrace of operational AI, but the industry remains cautious with clinical AI. That stance reflects our own approach to prioritizing responsible, incremental adoption and focusing on how AI enhances workflows rather than simply deploying new technology.
One of the standout discussions was around ambient scribing, but the conversation largely focused on relieving physicians’ burdens. We heard a strong call for similar support for nursing. It was great validation for what we’re addressing at Caregility. Our work with partners to create solutions that serve bedside teams—taking on tasks they don’t want to do or don’t have time to do—is where we see AI making the most positive impact.”
As in almost every aspect of life, Artificial Intelligence (AI) has entered the healthcare space, driving healthcare leaders to simultaneously be optimistic and concerned. The potential for AI to modernize care delivery and deliver on the promise of improving patient outcomes, increasing caregiver efficiency, and allowing practitioners to work at the top of their certifications is garnering understandable attention. However, like an iceberg, there is much more beneath the surface to consider before diving into AI adoption.
The Hidden Layers of Health AI
As we approach the AI iceberg, it’s imperative to consider the unseen, equally important, and often challenging aspects of effective implementation. The healthcare AI market is nascent and in flux, as evidenced by the spate of recent acquisitions, company failures, and startups entering the market. With regulatory frameworks still pending, healthcare AI remains a moving target. A measured approach to adoption is crucial, particularly in the high-stakes world of patient care, where poor execution will cost money and can put programs and patient lives at risk.
On the surface is the shiny object called Artificial Intelligence, but right out of the gate, a great deal of technology that is touted as AI is nothing more than automated data gathering with some logic around what to do when certain data is captured. While this can still prove to be valuable and in some cases, even more impactful than true AI, it should be considered as a different potential tool.
True AI will certainly capture data, but the difference is what is being done with that data and what inferences are deduced from comparing that data against a proven historical model to provide predictive analysis. That predictive analysis can be in the form of a diagnosis, a recommended care plan, or an alert indicating patient decline. The permutations are endless but what all AI engines have in common is the need to compare current data against known models. AI designed to learn will add new data to the model to continue to learn and enhance the accuracy.
Responsible health AI implementation warrants a look below the surface to expose the hidden challenges and considerations when evaluating technology for use in healthcare.
Data Model Considerations
As mentioned above, the success of AI in healthcare depends largely on the quality of the training data used to develop models. Data quality, representation across diverse patient populations, and model accuracy are vital to ensuring that AI systems can be trusted to make sound clinical decisions without bias. Health systems should require transparency from AI vendors and rigorous testing to ensure that models are accurate and support reliable outcomes. It is important to ask critical questions including: How was the model built? Where did the data come from? Will you be using my data? Is the data anonymized? Where is the data stored? Can you opt out of having your data used? The bottom line is, the data model is what drives the accuracy of an AI engine, and the answers to these questions will provide valuable insight on the viability of the engine.
For AI to be effective, it must be embraced by staff. There is often apprehension that AI will replace human workers. It’s important to implement AI in a way that is clinically impactful, enhances workflows, and minimizes disruption. A key question to ask clinical leadership when evaluating AI technology is, “Does it add value?” This value can come in the form of decision support, productivity gains, or actionable information. The question to ask the business is whether this is worth paying for, and if so, how much.
As an example, there is AI technology that can count the number of patient coughs per hour and provide an alert on the frequency increasing and the probable cause of the cough. Is this valuable? Who will get this information? Will it create false alerts from visitors coughing? This is one example, and one you may find value in, but ask the challenging questions and think through the impact on the clinical team, the volume of potential data and alerts, and how this will fit into the operational flow. And also, is it valuable enough to pay for the service?
AI solutions can generate large volumes of protected health information (PHI), adding to patient data vulnerability. Safeguarding the PHI generated and processed by health AI solutions is paramount. Solutions that support local edge processing can enhance security by keeping PHI within the confines of the healthcare facility, minimizing the transmission of sensitive information over the internet.
Additionally, the data captured by AI should be used responsibly. The data required for models to support machine learning AI means the solution provider will want to leverage data from your patients. You should have the option to opt out of this data participation and if you do opt in, you must be confident in how this data is being handled and protected.
This plays into another critical component of the use of AI in healthcare: patient acceptance. Implementing AI raises questions about patient rights, awareness, and consent. Health systems should consider provisions for offering patients insight into the AI solutions being used by the care facility and offer clear patient opt-out options when applicable.
I have had numerous conversations with healthcare executives and innovation teams across the country, and there is an often glaring disconnect between them and the clinical team on the floor about what is feasible. There is an overwhelming desire to expand nurse-to-patient ratios and AI is seen as the panacea for the staffing shortages and high cost of care. However, what many fail to realize is that there are a number of potential logistical challenges that can derail the intended benefits of AI. The potential for a massive amount of information and alerts that need to go somewhere can lead to caregivers becoming desensitized and potentially missing critical warnings. Evaluation of any AI solutions must include understanding the impact on the clinical team and the logistics of fitting it into the workflows as an augmentation.
AI implementation in healthcare is not a one-size-fits-all proposition. Scalable, multi-solution setups require flexible foundational infrastructure that can support a variety of technologies, both native and third-party, on-premises and cloud-based. As the market evolves, health systems will want to avoid getting locked into siloed solutions that may become obsolete as technology advances. Agile infrastructure that allows for adaptability and growth is key, enabling organizations to integrate new AI tools as they emerge and extend use cases where it makes sense.
Due to the processing power required and the massive amount of data required by AI engines, many providers utilize cloud-based solutions. Understanding the impact on your network and the amount of data being transmitted can be an important aspect of determining if the solution can scale.
AI is undeniably the shiny new object in healthcare, but it’s really not about technology. It’s about our ability to create solutions that solve problems for caregivers. We’re standing at a technological pivot point in healthcare, and leaders must approach AI implementation with open eyes, looking beyond the hype to understand the full scope of challenges and opportunities that come with implementation.
Field testing and clinical feedback are essential to ensure that AI tools meet the real-world needs of healthcare professionals. This is not a race to adopt the latest technology. It is an intentional move toward more modern, future-proof care delivery models that better serve patients and healthcare organizations. By taking a measured, thoughtful approach to AI implementation, health systems can navigate the hidden challenges of the AI iceberg and chart a course toward intelligent, truly transformative care.
Caregility Releases First Edge-Based Computer Vision AI Capability for Healthcare
Caregility eliminates reliance on expensive cloud processing for computer vision application
Wall, NJ (August 13, 2024) – Caregility Corporation, an enterprise telehealth leader dedicated to connecting care for patients and clinicians everywhere, is excited to announce the release of new fall risk detection capability in its iObserver solution. Hospital care teams use iObserver for continuous observation of patients at risk of self-harm or falls. The new artificial intelligence (AI) capability, developed natively by Caregility, uses computer vision to analyze visual information, detect fall risks, and alert caregivers accordingly.
The Caregility platform allows AI technology to run entirely on telehealth edge devices in the patient room, eliminating the need to stream patient audio-visual data to the cloud. This approach ensures cost-effective, scalable solutions across thousands of rooms where Caregility endpoints are available today. Other comparable technologies face challenges related to bandwidth for video streams and the processing power needed to scale across numerous rooms. Most importantly, this purposeful application of computer vision technology underscores Caregility’s commitment to responsible AI, as visual data is processed locally, safeguarding data protection and privacy.
“Our open and adaptable platform allows us to develop native AI capabilities when possible while also evaluating other third-party solutions to determine if they can add value to caregivers’ workflows,” said Kedar Ganta, Caregility’s Chief Product and Engineering Officer. “This flexibility keeps us at the forefront of AI advancements without being locked into a single solution.”
“We’re redefining the future of telehealth with AI-powered edge computing,” said Bin Guan, Caregility’s Chief Innovation Officer. “By breaking down traditional barriers to AI adoption, we’re enabling healthcare providers to leverage cutting-edge technology without sacrificing operational efficiency or data privacy.”
“This breakthrough in AI at the edge is confirmation of Caregility’s ability to innovate,” added Mike Brandofino, Caregility’s President and Chief Operating Officer. “At the same time, no company can provide all the technology required to deliver on the modernization of care. Caregility remains committed to integrating with other AI technology partners where it makes sense in order to bring value and flexibility to our customers. This ultimately provides them with a foundational platform they can build on for the foreseeable future.”
Caregility’s computer vision capability is available to be used on every one of the more than 15,000 Caregility edge devices currently installed in hospitals around the globe. This is a testament to the value and durability of Caregility’s purpose-built devices, called Access Point of Care Systems (APS). The first APS system ever deployed by the company is still running after six and half years in continuous operation, proving that choosing Caregility means that health systems will have a future-proof solution that can adapt as new technology is introduced.
“Our approach to AI involves understanding the challenges our customers face and then identifying where technology can play a role,” said Ganta. “We believe that combining our video AI and audio AI with specialized third-party solutions, such as contactless vitals tracking, can provide caregivers with valuable insights to improve patient outcomes. By leveraging our edge devices and avoiding the challenges of cloud-based AI, we can achieve faster and more accurate results with our proprietary technology.”
About Caregility Caregility Corporation is dedicated to connecting patients and clinicians everywhere with its Caregility Cloud™ virtual care platform. Awarded the Best in KLAS Virtual Care Platform (non-EMR) in 2021, 2022, and 2023, Caregility Cloud™ powers a purpose-built ecosystem of enterprise telehealth solutions across the care continuum. Caregility provides secure, reliable, and HIPAA-compliant audio and video communication designed for any device and clinical workflow in both acute and ambulatory settings. Today Caregility supports more than 1,100 hospitals across over 85 health systems with over six million virtual care sessions hosted annually. From critical and acute, to urgent and emergent, to post-acute and ambulatory, as well as hospital-at-home, Caregility is connecting care everywhere. Follow Caregility on LinkedIn and Twitter at @caregility.
Digital Transformation and the Future of Acute Care
Healthcare delivery is changing rapidly as health systems adapt to modern-day obstacles and opportunities. Staffing challenges, clinician burnout, evolving patient expectations, and the accelerated pace of technological innovation are driving radical reinvention in acute care settings.
Most notably, virtual care and artificial intelligence (AI) are converging at the patient bedside to enable more agile workflows for care teams that improve outcomes, enhance capacity, and elevate patient and staff experience. AI-outfitted rooms, digital whiteboards, and remote support for no-touch patient care tasks are emerging as cornerstones in acute care of the future models.
For patients, the future of healthcare engagement is omnichannel, integrated, and digital-first. For clinicians, these advancements finally put clinical teams on par with other industries in terms of remote workflow support and digital enablement. As providers consider what the hospital room of the future will look like, there is an increased focus on making smart investments today in technologies that will help health systems most effectively work toward that future state.
Providers’ Digital Transformation Priorities
McKinsey & Company recently surveyed 200 global health system executives about their digital investment priorities and progress with results to date. 75% of respondents reported that their organizations place a high priority on digital and analytics transformation but lack sufficient resources or planning in this area.
70% of respondents flagged virtual care as one of the top three digital health priorities that will have the biggest impact on their organization. AI was the only digital health priority to edge out virtual care, with 88% of respondents indicating AI among the biggest potential areas of impact. Even so, roughly one in five respondents do not plan to invest in AI within the next two years.
What’s holding providers back from investing in these technologies? Some organizations are waiting out the early stage growing pains in health AI while others are taking a measured step with pilot program implementations. Roughly half of respondents cited budget and capital expenses as a key obstacle to investing at scale.
Virtual health to drive patient experience and access was cited as the digital investment area with the highest performance satisfaction level. And, as McKinsey reports, “Optimizing workflows to enable more appropriate delegation, with technical enablement, could yield a potential 15 to 30 percent net time savings over a 12-hour shift. This could help close the nursing workforce gap by up to 300,000 inpatient nurses.”
The Role of Virtual Care & AI in Acute CareTransformation
In the recent webinar “Acute Care of the Future: How Health Systems Should Position Themselves and How Geisinger is Getting Ahead,” hosted by AVIA, executives from Geisinger Health System, a leader in healthcare innovation, emphasized the importance of investing in and scaling the right digital health experiences. These digital health investments are setting the new standard for attracting patients and staff, driving efficiency, and improving processes.
Today the health system and Caregility customer site’s digital health portfolio includes virtual ICU, sepsis, observation, telemetry, and nursing programs. In 2023 over 73% of admissions and 58% of discharges were conducted virtually at Geisinger with executive leaders reporting several benefits:
Repurposed 12,000 hours for bedside staff due to fielding tasks virtually
3-hour reduction from order-to-admit to arrival from the unit (22% time savings)
20-minute reduction in time to discharge (11% time savings)
Geisinger executives emphasized that platforms and ecosystems that support multiple use cases are vital to providing relief and value to care teams. Ease of use is critical to the “stickiness” of these technologies, ensuring that they are adopted and utilized effectively.
Conclusion
The future of acute care lies in embracing digital health innovation to create more efficient, patient-centered, and agile healthcare systems. By investing in the right technologies, health systems can navigate the challenges of today and position themselves for success in the future. As we look ahead, the question we must continually ask ourselves is this: What investments can we make today to future-proof care delivery?
At its core, healthcare delivery is a human undertaking skillfully tailored to meet the needs of each individual patient.
At Caregility, we understand that AI will never replace the intuitive human touch of dedicated caregivers. What we believe AI can do is empower clinicians by automating routine tasks so they can focus on what truly matters.
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ViVE 2024 Recap: Embracing Practical Applications for Healthcare’s Future
Author: Kedar Ganta, Chief Product and Engineering Officer, Caregility
The third edition of ViVE demonstrated that healthcare technology has come of age. The 2024 conference wasn’t just about futuristic ideas; it showcased practical applications of health IT advancements that are augmenting care and revolutionizing healthcare delivery. Discussions centered on the significant shift from traditional models to hybrid care, promising improved outcomes across the healthcare ecosystem.
Beyond this overarching theme of progress, there was equal emphasis on both opportunities and challenges. Here are a few of the themes that dominated conversations at the event.
Expansion of Virtual Care
The growing significance of virtual care in supporting new care models is undeniable. There were numerous announcements of new virtual care platforms and partnerships that showcased their potential to enhance patient monitoring and care delivery. Despite its potential, virtual care still confronts significant hurdles such as bridging the digital divide, building patient trust, addressing privacy concerns, and navigating complex regulatory issues.
An offshoot of virtual care expansion that continues to get attention is virtual nursing. This year ViVE introduced a new track for nurse leaders at the conference, aimed at cultivating nurse-led innovation to address challenges and improve care delivery. Sessions on nursing’s role in the home, tackling staffing challenges, and training for future work point to the growing role technology will play in this field.
Responsible AI
AI continues to bask in the limelight, showcasing its potential for streamlining administrative tasks and optimizing workflows, impacting the entire patient care journey from admission to discharge. There are still many questions about the ethical considerations of applying this technology at scale, yet, despite these concerns, many organizations are eager to embrace AI-augmented solutions.
Natural Language Processing (NLP) saw a significant surge in momentum, offering a greater promise of reducing administrative burden. In addition to industry incumbents like Nuance and M*Modal, emerging players such as Abridge, Sunoh.ai, and DeepScribe are vying for prominence. Abridge’s recent funding garnered attention, accelerating their efforts to lead in this space. Notable is DeepScribe’s collaboration with Amazon Web Services (AWS) to scale their healthcare-focused large language models and seamlessly integrate with AWS HealthScribe, signaling a shift towards purpose-built healthcare language models.
As AI becomes increasingly pervasive, we can expect more discussions around the need for regulations and guidelines governing AI’s ethical and acceptable use in healthcare. Responsible use of AI is imperative to uphold patient data privacy and mitigate the risks of misuse.
Data, Security, and Trust
The emphasis on interoperability at the event underscored the significance of data in decision-making, leading to better patient outcomes and driving innovation to revolutionize healthcare delivery.
Cybersecurity took center stage, prompted by the recent high-profile attack on Change Healthcare. As my colleague Jeff Ryan put it, “The focus certainly shifted from AI to cybersecurity as news spread of the incident.” The cyberattack serves as a cautionary reminder of the risks of pursuing innovation without sufficient governance measures in place. It underlines the importance of implementing robust cybersecurity measures to safeguard patient privacy and maintain trust within the healthcare ecosystem.
A Glimpse into the Future
As the curtains closed on ViVE 2024, it dawned on me that we now possess the necessary technology to support the healthcare workforce and ecosystem. It offers a promise where staffing challenges are addressed, burnout is reduced, and the joy of healthcare is restored. From cybersecurity and interoperability to responsible AI, the digital health space offers ample opportunities for innovation. The onus lies on the industry and its leaders to be proactive, challenge the status quo, and forge a new path to reshape the future of healthcare as we know it.
About the Author Kedar Ganta brings 20 years of leadership experience in enterprise-scale and cloud-native applications to his role as Chief Product and Engineering Officer at Caregility. He previously served as Head of Product for Global Growth Verticals at Cisco Systems and shaped digital product strategy in technical leadership roles at athenahealth, GE Healthcare, and Microsoft.
Empowering Compassionate Care: Caregility’s Journey with AI in Telehealth
Author: Paul Oliver, CRO, Caregility
Some years ago, while in a discussion about technology innovation, the CIO of a world-famous healthcare institution spoke about his focus on compassionate care. That conversation struck a chord with me. At its very core, healthcare delivery is a human undertaking that is skillfully tailored to meet the needs of the individual patient. We should never lose sight of that fact. Digital health solutions must be a means to that end: delivering high-quality, compassionate, efficient, and safe patient care across the continuum of prevention, diagnosis, treatment, recovery, and follow-up. Technology must blend into the background and silently help human caregivers do their best work.
With this mission in mind, our team at Caregility is now embedding AI technologies into our proven virtual care platform to bring individualized patient care to a new level. The compassionate care belief system is a driving force as we set about that work.
We recognize the immense potential for AI in healthcare, but the consequences of getting it wrong, even in a tightly defined domain such as virtual care, could be harmful to care teams and patients. We’ve seen AI applied in broad strokes – delivering a standard set of capabilities to every patient without consideration of their individual needs. We believe that approach is a mistake, and we are committed to applying AI in a responsible way that respects the needs of individual patients and adapts accordingly.
Steering Principles for AI in Telehealth
To ensure our introduction of AI creates value for care teams and contributes to individualized patient care, we’ve embraced a few key steering principles. We believe these principles will help us stay true to our compassionate care beliefs, focus our development work, and allow us to adapt quickly as the underlying technology matures and we receive real-world feedback from our customers. Those steering principles include:
Partnering our clinical and development teams closely on product direction. Our clinical solutions team, comprised of licensed healthcare practitioners with decades of experience in bedside nursing and virtual care program rollouts across leading healthcare institutions, plays a significant role in ensuring that the new AI enhancements we bring to market will positively impact the day-to-day experience of bedside clinicians and their patients.
Responding to every patient as an individual. The more an AI-driven service is customized to the individual patient, the more meaningful the clinical insights that AI helps reveal are. Striving to achieve this level of personalized care delivery helps ensure actionability without introducing nuisance alarms.
A focus on Return-on-Investment. We are committed to being clear-eyed about the financial and clinical outcomes new AI enhancements must deliver to justify the resource investment in time, money, and opportunity.
Caregility’s AI Roadmap
At Caregility, we are providing an optional set of AI-driven services that enhance specific virtual clinical programs, such as Virtual Nursing and Continuous Observation. As we build out these and other AI solutions, in order to adhere to our guiding principles, Caregility is employing an agile software development approach to release AI enhancements for virtual care in phases.
The initial phase is a minimum viable product (MVP) that embeds selected AI capability into our platform, delivering a targeted subset of our overall AI vision to Caregility customers and their patients. We will deploy the MVP to early field trial customers willing and able to partner closely with our clinical solutions team. Together they will provide our development team with a feedback loop that answers essential questions: Are we heading in the right direction? Is our vision for AI enhancement something that solves problems that exist in this institution, or should we adjust? Do our customers trust that this technology shows promise?
We’ll then make modifications and improvements to deliver subsequent phases, using customer feedback to drive value at every stage: Does it increase efficiency? Does it improve care quality? Does it positively impact patient and staff safety?
Our goal is to create additional value for our customers through the Caregility point-of-care telehealth systems that they have already deployed and are planning to deploy. With powerful edge computing and high-resolution cameras and microphone arrays that simultaneously support live two-way audio/video sessions and multimedia streaming, the Caregility Cloud™ platform was built with medical-grade AI integration in mind.
Intelligent Telehealth Endpoints
Each Caregility point-of-care system we deploy today is equipped with sophisticated, purpose-built microphone arrays and HD cameras that can introduce both remote clinician support and AI-enhanced monitoring to care teams. Here are a few examples that illustrate what purpose-built means:
Patient rooms are often noisy environments. Caregility audio processing is purpose-built to perform well in this environment so that remote clinicians can pick up on alarms and hear patients and bedside teams clearly. That same capability can be dual-purposed to feed AI language processing components that enable ambient listening and documentation.
To support patient observation at night, Caregility cameras are purpose-built with infrared night vision. That capability also ensures that computer vision-based AI performs well under low-light patient room conditions.
Our point-of-care devices can send audio and video streams to multiple locations simultaneously to support simultaneous workflows. That means a remote doctor can consult with a patient at the same time the video stream is feeding our AI engine to monitor for patient safety issues such as the bed rail positioning.
The question we seek to answer through AI enhancement is: What tasks can AI help us automate to augment the work of care teams?
AI-Enhanced Audio, Video, and Radar for Virtual Care
As we set out to introduce clinical AI to our virtual care platform, we’re focused on three key areas.
Computer vision enhancements will analyze patient room video streams to look for safety risks, best practice adherence, and workflow optimization opportunities. If the engine detects something that requires human intervention, our intent is to flag it for the right member of the care team. We will leverage our existing iConsult and iObserver applications as the main way to surface useful AI-driven insights to care team members, with incremental updates expected. We want insights to be actionable, not disruptive. Customer feedback from early field trials will inform our roadmap.
In the outpatient context of telehealth, we plan to extend video stream processing to virtual visits to gather patient vital signs (i.e., respiratory rate, heart rate, blood pressure, and body temperature) from facial video analysis. We’ll present this live data to the remote clinician so they can see it as part of their remote consultation.
Acoustic-based AI will listen to audio streams for patterns that can alert staff to patient stress or behavioral issues. In the inpatient context, we are researching embedded AI to identify medically relevant parts of conversations between clinical staff and patients to relieve some of the clinical note-taking burden for care teams. Ambient listening will inform structured clinical data capture for nurses to review before being documented in the EHR.
We are also working to integrate an optional radar sensor with our point-of-care devices to support contactless vitals streaming in inpatient care. Trending heart and respiratory rates over time could signal deterioration or changes in the patient’s condition. Our goal is to support personalized compassionate care and alert the appropriate clinician if vital signs diverge away from that patient’s baseline. Sepsis is a major patient safety factor in our hospitals, and we believe widespread adoption of this type of technology will help attack that problem and others.
The AI journey ramps up for Caregility in 2024, when we release our first commercial offerings addressing two of our key focus areas: Augmented Observation and Vitals Trending. We will partner closely with our early field test customers to measure the impact those solutions have on key performance indicators. Customer feedback will fuel our next wave of intelligent telehealth enhancements aimed at advancing compassionate, personalized care.
AI’s Potential in Inpatient Clinical Care
Traditionally, healthcare has been regarded as a laggard in embracing IT innovations, primarily due to the inherent complexity of care delivery and the stringent regulatory environment. However, this perception is undergoing a significant transformation. The widespread adoption of acute telehealth has played a pivotal role in this shift, providing care teams with valuable hands-on experience that has helped build trust in health technology. As a result, healthcare professionals have become more comfortable with and open to leveraging digital health tools in their daily practices.
This newfound receptivity to digital enablement has set the stage for a remarkable leap forward in the industry. This is evidenced by the recent surge of interest in artificial intelligence (AI) in healthcare. The proliferation of generative AI and the urgent need to find new solutions to the ongoing staffing crisis are further fueling interest in exploring AI’s place in clinical care.
AI-Enhanced Telehealth
Although headway has been made regarding operational use cases for machine learning-based AI in backend process improvements, healthcare teams are eager to identify and implement AI solutions that can enhance clinical workflows, produce more precise diagnoses, and improve patient outcomes. For the many hospitals equipped with synchronous, audio/video-based telehealth services at the bedside, infrastructure exists to introduce AI services at the point of care as well.
In the realm of virtual care, the evolution of intelligent telehealth endpoints has been remarkable. As telehealth has evolved from audio-only interactions to feature-rich video encounters, camera and microphone quality have improved to keep pace. One noteworthy development is the integration of edge computing capabilities into telehealth endpoints, enabling the support of AI applications. New multiplexing technology supports multiple cameras, allowing care teams to simultaneously support virtual patient engagement and video-based AI solutions.
In addition to high-fidelity camera and microphone arrays, new sensors such as radar technology are making their way into telehealth platforms. These sensors are capable of continuously capturing valuable clinical data while minimizing disruption to the care process. These advancements in telehealth are allowing care teams to infuse AI services such as Augmented Observation and Vitals Trending into bedside care processes, arming clinical teams with patient safety reinforcement tools that reduce pressure on staff and lead to better outcomes.
Promise and Precautions in Clinical AI
While the adoption of AI in healthcare holds immense promise, it also comes with its set of challenges and precautions.
In a recent interview with Healthcare IT Today editor John Lynn at the 2023 HLTH conference, Caregility Chief Product and Engineering Officer Kedar Ganta lauded AI’s ability to bring in ambient intelligence from audio, video, and sensor feeds, “whether it’s collecting vitals or documenting notes for the clinician.” This technology operates in the background, collecting vital patient information and supporting care processes without causing disruption.
Ganta does note, however, that amidst the excitement surrounding AI, the topics of trust and accountability often receive insufficient attention. Establishing patient and provider trust in AI systems is crucial. “This is where regulation comes into the picture,” says Ganta. “It’s a balance between over-regulating something versus promoting innovation.” Reliable data and transparency in AI solutions are essential for building trust, and the inner workings of AI algorithms should be shared with providers to enhance visibility into these factors.
Additionally, “providers should have the ability to override the AI decision” Ganta advises. Creating a regulatory framework and an independent body to oversee AI in healthcare is vital to address these concerns. As AI continues to reshape the healthcare landscape, these discussions about trust, regulation, and accountability are essential for harnessing the full potential of AI while safeguarding patient wellbeing.
As the healthcare industry continues to embrace these advancements, the future holds exciting possibilities for improved patient outcomes and experiences.
Nurse Spotlight: Sarah Lake, MS, RN, CCRN
Many would agree that the nursing profession isn’t for the faint of heart. RNs see it all. Although Sarah Lake, MS, RN, CCRN, didn’t initially set out to be a nurse, her early years working in the criminal justice system offered plenty of parallels.
Sarah first pursued undergraduate studies in political science and criminal justice, earning her bachelor’s degree at the University of South Dakota (USD). The Sioux Falls native then held positions as a correctional officer and a court services officer, doing what she describes as “the equivalent of felony probation and supervision for community members who don’t go to prison.”
Sarah Lake, MS, RN, CCRN Clinical Program Manager, Caregility
Like the clinical work she’d eventually embark on, Sarah’s Corrections roles operated under a paradigm that put emphasis on prevention through early intervention. The challenge was that she oversaw a population that was profoundly underserved with no access to social services. Sarah recognized that the lack of support services was an impediment to her ability to adequately help those in her community. This point of frustration led her back to school to find a different way she could help.
Sarah earned her nursing degree at USD intending to go into community, public, or mental health. After a preceptorship at the Department of Health setting up points of distribution during H1N1, she landed a critical access nursing role in Chamberlain, SD, at Sanford Chamberlain, ultimately returning to Pierre, her home community, with a role in Avera St. Mary Hospital’s ICU. That role introduced Sarah to a relatively new theory of care. Avera St. Mary’s eICU program allowed patients to receive services from remote clinicians while remaining close to home and family.
“The hospital had an eICU system that allowed clinicians to push a button to get instant access to intensivists and critical care nurses to help take care of critical patients whom we would have otherwise had to transport to tertiary care,” Sarah explains. “When I pushed the button for the first time [to get help] on a drip I was unsure of, I was sold.” Sarah immediately recognized the potential that virtual care posed to broader use cases.
“The thing I am the most passionate about is improving the delivery of patient care.”
– Sarah Lake
“During my career, I did temp work outside of hospitals and worked as a flight nurse, but I always came back to the eICU at Avel eCare (then Avera eCare) because I liked virtual care’s ability to give folks world-class care in their home community. When I came back to work full time in the Sioux Falls area at Avel eCare, one of the service lines was a multi-specialty clinic offering specialties to IHS. Sixty percent of the services we were supporting were mental health or psych related – precisely the kind of services I thought we needed when I was in Corrections.”
When COVID-19 hit, Sarah and her team again turned to telehealth to remotely support patients isolated at home. It wasn’t long before Sarah was recruited by Caregility to put her virtual care experience to work supporting hospitals across the nation looking to follow suit. Today, Sarah helps health systems hone their telehealth strategy, design virtual clinical workflows, and stand up EMR-integrated programs that improve care delivery for patients and providers.
Sarah sees hybrid care fueling what’s possible in healthcare. That includes the ability to support remote family involvement or group visits, patient and staff education, and patient monitoring as an added safety layer and another way to build relationships with patients.
“Post-COVID, patients are sicker and there aren’t as many clinicians available to take care of them,” Sarah notes. “Adopting a virtual nurse is one way teams can meet in the middle. Medicine is also getting much smarter. Wearables and home-based apps have tremendous potential to further personalize care and proactively improve outcomes. Increased use of AI will not only enhance care delivery but also optimize operations. It will account for things people don’t think about when they’re putting patients into beds – like higher fall risk if the patient is placed at the end of the hall – to support the best utilization of space. What locations are best for the recovery of specific conditions? What staff do we have to take care of them? We’ll see the use of AI in those operations.”
For those looking to implement a virtual care program, Sarah offers five points of advice:
Work with a multidisciplinary team including clinical, administrative, and IT stakeholders to define your goals and objectives based on your unique pain points.
Conduct a feasibility study to determine what it will take to launch your program. Consider time and resource requirements, seeking outside expertise as needed.
Define your clinical protocol. Demonstrate ways the solution will benefit patients, lighten staff workload, and foster new professional development skills among staff who will use the tools.
Select technology that supports your identified workflows. Will telehealth endpoints be cart-based or wall-mounted? What integrations are desired? Be mindful of regulatory compliance and network factors.
Iterate and re-iterate constantly.
“Virtual care and telehealth bring us to a whole new level of being able to deliver care to absolutely everybody in a quick, cost-effective manner, even in geographically isolated communities with socioeconomic struggles,” says Sarah. “Today we can have a diabetes patient see a world-class endocrinologist at home on an iPhone. Virtual care lessons I’ve learned along the way have only broadened what I see as the future potential.”
Interested in connecting with a Caregility Clinical Program Manager to discuss your hybrid care strategy? Contact us today!
AI-Enhanced Telehealth: Hope or Hype?
ChatGPT and a plethora of other AI-powered applications are rapidly gaining popularity in today’s tech-driven world. In healthcare, AI and machine learning algorithms are being adopted to drive efficiency in patient-facing and back-office settings alike.
One of the clinical frontiers gaining attention is the augmentation of virtual care programs with AI tools such as computer vision, ambient clinical intelligence, and contactless monitoring. By bringing these AI enhancements into virtual workflows in the inpatient setting, healthcare organizations hope to positively impact patient safety, clinical outcomes, care team experience, and operational performance.
During a recent fireside chat, Caregility President and COO Mike Brandofino sat down with Healthcare Innovation editor Mark Hagland to explore the practicality, best practices, and perils associated with selecting and adopting AI technology to advance telehealth.
AI’s Potential in Acute Virtual Encounters
AI is showing promise in clinical use cases in acute care settings where staffing shortages and burnout are prominent. As Brandofino sees it, one of AI’s biggest benefits is in “augmenting the information that a clinician or caregiver has access to with more clinical insight than they’d be able to gather on their own.” When evaluating tools, he encourages stakeholders to consider the impact: “Is it taking tasks away that can potentially save staff time? Is it a tool that adds to productivity?”
One of the AI functions Brandofino sees potential in is radar-based contactless monitoring. These tools continuously capture patient vitals such as heart rate and breathing rate, as well as track motion in the room. This allows caregivers to see trends over time.
“The AI part of that is the algorithms can detect changes in that pattern that mean something,” Brandofino explains. He offers a practical use case example. “That radar device can tell you, based on telemetry, that a patient is starting to wake up. Now think of a post-op situation where the nurses have to be there when the patient wakes up disoriented. Can you just have a contactless device notify them when the patient is starting to wake up so they can get in there then instead of sitting there for 30 minutes waiting?”
Automated, contactless vitals monitoring also accelerates the frequency and timeliness of clinical documentation.
“If you think about what happens with nurses as they do their rounds and take vital signs, many times they don’t get that information into the EHR until the end of their shift or hours later,” Brandofino notes. AI tools can gather vital signs many times throughout the day and put it through an algorithm to evaluate if the patient is getting better or worse. This allows care teams to intervene earlier and potentially improve outcomes.
Ambient clinical intelligence uses AI tools like natural language processing to draft clinical notes and reports, posing similar efficiency benefits. In care environments where resources are thin and burnout is high, those incremental time savings can add up.
Caveats to Consider When Adopting Health AI
As you evaluate AI solutions to bring into patient care delivery, it’s important not to become enamored with the technology before understanding where it fits into the patient care workflow. Brandofino recommends including all stakeholders—clinical, IT, and operations—in evaluations. “How are you going to support your device fleet? Consider the clinical workflow as well as the experience on the patient side.”
“Think about the operational logistics of supporting what you’re doing,” Brandofino advises. “What we suggest to our customers is to understand the impact that you’re going to have on the staff on the floor and think about what that is going to be like at scale.” Nurses are some of the most interrupted people in healthcare. The last thing you want to do is introduce new tools that add to their stress level, whether that be an overabundance of false alarms or device overload.
Given the newness of many AI tools entering the market, it’s also important to consider who you’re partnering with. Has the tool been implemented in one or two patient rooms or thousands of rooms? Are there examples of in-market success that can offer a roadmap?
Combining AI and Telehealth to Empower Caregivers
By integrating AI with virtual care, healthcare organizations can modernize care delivery with innovative new tools and keep the human element of care intact. AI can drive intelligent clinical alerting, while virtual engagement channels serve as a bridge for immediate staff intervention. When combined, these resources amplify what virtual teams are capable of supporting remotely, doubling down on reducing the burdens on bedside staff.
“We believe that combining that remote nurse with smart technology to help gather telemetry in the room will be really impactful in improving care for patients in the long term,” says Brandofino. “Don’t feel like you have to put in a siloed solution just to get access to AI technology. Look for players that are capable of integrating with what you already have. If you already have high-end cameras and mics in the room with edge processing, what else can you gather in the patient room to give to caregivers?”
Ultimately, healthcare organizations that focus on applying technologies that solve real problems that exist today around the shortage of nurses, productivity, and quality of life for staff will have the most impact.
Interested in learning more about AI-enhanced hybrid care solutions? Contact us today!
