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.
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!
Is Health AI Finally Having its Heyday?
Artificial intelligence has been bandied about in the healthcare industry for years but, if the 2023 health IT tradeshow circuit is any indication, health AI may finally be entering its heyday.
The HIMSS23 conference recently wrapped up, with roughly 35,000 healthcare professionals gathering in Chicago to showcase and explore solutions aimed at improving care delivery, patient outcomes, and operational efficiency. And much like the recent ViVE conference, there was no shortage of health AI news and innovation on display:
The focus on AI is unsurprising given the explosive popularity of platforms like ChatGPT. Mainstream access to AI tools is fueling innovation across industries as stakeholders look to leverage the technology in their respective fields. For healthcare, AI is rapidly moving from hype to practical application in both clinical and operational use cases.
“AI is currently in the spotlight, with a mix of anticipation and apprehension about its potential to alleviate staffing shortages and expand care in a challenged healthcare system,” observed Caregility Chief Product and Engineering Officer Kedar Ganta, who was among those in attendance at HIMSS 2023. “While some are still struggling to grasp AI’s role beyond the buzz, others are already exploring its integration with various technologies.”
Many of the health AI solutions in and entering the market aim to help providers improve patient outcomes by supporting the early detection of patient deterioration and adverse events using things like computer vision and contactless monitoring. Health AI tools can also improve the clinician experience by adding a digital line of support to simplify tasks, for example, using ambient intelligence tools to support clinical documentation.
As Caregility President and COO Mike Brandofino put it, “AI advancements will enhance clinical insight and enable care for more patients, in spite of challenges related to limited staff resources.Nothing can replace a knowledgeable, experienced caregiver, but how much more effective can they be if we augment the information available to them? Continuous virtual observation of patients, data capture through wearables, and access to predictive algorithms that can help providers anticipate conditions affecting patient outcomes will combine to improve care.”
The potential benefits are undeniable, but health AI implementation won’t come without its challenges. An overwhelming array of innovative health AI solutions are entering the market. As healthcare organizations pilot emerging technologies, adopting health AI solutions without introducing new tech silos and solution sprawl that may congest networks will be pivotal.
In a recent interview with HIMSS TV, Brandofino touched on ways virtual care platform integration can help hospitals and health systems more easily integrate health AI solutions into clinical workflows.
“There are going to be a lot of things that come on the market – shiny new objects – and not all of it is going to work,” said Brandofino. “Our approach through our ecosystem is to show that we can integrate one system in a room with [a variety of] different AI platforms.”
In adopting a platform approach, health systems are equipped to experiment with health AI solutions in a way that leverages existing virtual care infrastructure, rather than introducing an additional framework to manage new resources that might become obsolete.
“Combining virtual care solutions and AI technology yields tremendous potential to enhance and improve a variety of clinical workflows in acute care settings,” said Pete McLain, Chief Strategy Officer at Caregility. “Artificial intelligence has the potential to enhance and augment – not replace – care teams, helping them save time, capture more meaningful patient data, and support clinical decisions and interventions that lead to better care and improved patient outcomes.”
To learn more about Caregility’s AI-enhanced hybrid care solutions, contact us today.
Healthcare AI and the Push for Transparency
Efforts to bring regulatory oversight and transparency to healthcare AI received a push from notable advocacy groups recently.
The Coalition for Health AI
In October 2022, members of the Coalition for Health AI (CHAI) convened to finalize regulatory framework recommendations on the responsible use of artificial intelligence. ONC recently joined the FDA, NIH, and the White House Office of Science and Technology Policy (OSTP) as federal observers of the coalition, which counts Johns Hopkins University, Mayo Clinic, Google, and Microsoft among its members.
CHAI announced plans to share its recommendations, culled from healthcare stakeholder workshops and public feedback, by the end of the year. The organization aims to identify priority areas that require guidance to ensure equity in healthcare AI research, technology, and policy. Healthcare IT News reports that CHAI researchers are also developing an online curriculum to support standards-based training on AI development, support, and maintenance.
The White House OSTP
CHIA’s news came on the heels of the White House OSTP introducing its broader Blueprint for an AI Bill of Rights. The Blueprint identifies five guidelines for the design, use, and deployment of automated systems that seek to protect Americans, including:
Safe and effective systems – Diverse stakeholder and expert feedback; testing and risk mitigation; evaluation and reporting
Data privacy – Patient agency over how data is used; data is secure and only used for necessary functions
Notice and explanation – Patient notification of AI and how and why it contributes to outcomes
Human alternatives, consideration, and fallback – Patient opportunity to opt out; human alternatives if system fails or patient opts out
The framework applies to automated systems that “have the potential to meaningfully impact the American public’s rights, opportunities, or access to critical resources or services,” including healthcare.
The FDA
Clinical Decision Support (CDS) software guidance issued by the FDA in late September 2022 includes more explicit recommendations related to the use of AI in healthcare. The FDA recommends that CDS Software-as-a-Medical-Device (SaMD) solutions provide plain language descriptions of underlying algorithms, data sets, and research validation methods, including:
A summary of the logic and methods used to provide clinical recommendations (e.g., meta-analysis of clinical studies, expert panel, statistical modeling, AI/ML techniques)
A description of data sources used so providers can assess if data is representative of patient populations
A description of the results from clinical studies conducted to validate the algorithm and recommendations so providers can assess potential performance and limitations (such as missing patient data or highly variable algorithm performance among sub-populations)
You can find a list of software functions this would impact here.
Establishing Trust in Healthcare AI
Each of these initiatives seeks to contribute to a more comprehensive regulatory framework for healthcare AI and offers a glimpse into what is likely ahead for the flourishing – and currently largely unregulated – field.
These tools hold tremendous potential clinically (i.e., disease prediction) and operationally (i.e., process automation). From enterprise imaging workflow support to advanced video analysis for patient fall detection, providers are eager to leverage AI to drive efficiency in care delivery. There is, however, growing awareness of the potential for bias in underlying algorithms, which can lead to health inequity.
Stakeholders are calling for transparency in healthcare AI algorithms, and rightly so. The kind of “explained AI” that CHAI, the White House, and the FDA are championing would pave the way for new regulatory frameworks that foster trust for clinicians and patients and accountability for vendors.
“Existing models of regulation are designed for ‘locked’ healthcare solutions, whereas AI is flexible and evolves over time,” notes EY GSA Life Sciences Law Leader Heinz-Uwe Dettling. “Devices may need reauthorization if the AI continues to develop in a way that deviates from the manner predicted by the manufacturer.”
The coming years will undoubtedly see friction between AI innovation and regulation. As a broader regulatory framework materializes, those who embrace algorithm transparency could benefit from proactively leading the charge to build trust between solution providers, clinical teams, and patients.
How AI will Transform Virtual Patient Sitting
Just a few years ago, virtual sitting was being touted as one of the most important technology implementations a hospital could make to reduce costs and improve patient safety. Hospitals could install cameras into patient rooms, or make use of existing video-enabled carts, and then set up a remote monitoring center where patient sitters keep an eye on patients in multiple rooms (or even multiple facilities) all at once.
Yet such is the pace of change in information technology that even the traditional patient sitter model is now ready for a transformation. How? Enter “augmented intelligence,” commonly known as artificial intelligence, based on machine learning.
What you need to know about the existing virtual sitter model
The current virtual patient observation model is a proven, cost-effective strategy to replace in-person sitters, or hospital staff who sit in rooms with patients who are at risk of harming themselves, such as when attempting to get out of bed unattended.
Virtual patient observation can be used in a variety of settings but is key to helping hospitals avoid costs from fall injuries. Every year hundreds of thousands of patients fall in hospitals, with one-third resulting in serious injury. The Joint Commission estimates that, on average, a fall with injury costs $14,000, but depending on the severity of the injury, unreimbursed costs for treating a single hospital-related fall injury can be up to $30,000.
Yet the existing model also has shortcomings.
To start, these systems tend to send a high number of false alerts to the remote patient sitters. According to a study published by the Journal of Healthcare Informatics Research in 2016, medical professionals in hospitals can encounter more than 700 alarms in a single day – making it difficult to differentiate a true emergency. ECRI listed false alarms among its top 10 health technology hazards in 2020. With so many false alerts, patient sitter fatigue becomes very real and dangerous as human monitors begin to tune out alarms.
Next, the current approach to virtual sitting usually only focuses on a rectangular area around the patient bed, unable to monitor or analyze what else is in the room.
With a new generation of machine learning-enabled video analysis software paired with video monitoring technology, we have the potential to solve these core problems.
What is Augmented Intelligence or “AI”?
It’s important to note that nothing can replace a knowledgeable, experienced caregiver, but how much more effective can they be if we augment the information they have at their fingertips?
This is precisely where AI comes in.
AI can be applied to the video recordings taken in most hospital patient rooms to better categorize alarms related to movement in those rooms. Augmented Video Analysis (AVA) systems can provide additional information and data to hospital decision makers, resulting in more accurate warnings and alerts, among other benefits.
AVA leverages real-time video and experiential knowledge to learn what is going on in the room, and alert clinical staff if help is needed. Over time, AVA learns how to better observe not just that one patient, but all patients, everywhere. It learns continually—and it doesn’t get tired.
What can AVA do that existing virtual sitting systems can’t?
As mentioned, the current systems have their shortcomings. But the AVA system uses unique algorithms (or sets of rules) for the computer to follow. The algorithms can determine the severity and/or scope of the action in the room.
The more video the system receives, the more detail it can detect, and the more accurate assessment it can provide. Has the patient fallen out of bed – or just leaned over to pick something up off the floor? Did a visitor reach to hold a patient’s hand– or did they pull out an IV?
Some distinguishing features of AVA systems include:
Identifying “regions of interest” that provide a holistic view of the patient room, rather than the traditional rectangular “static box”
Differentiating a caregiver, patient, or visitor – and applying different rules for each persona
Producing “bounding boxes” around each object in the room and indicating when they interact (for example, when a visitor touches a patient or a patient touches an IV pump)
Protecting patient privacy
With all this capability to observe and analyze what is going on in a room, AVA can still be configured to protect the patients’ privacy.
First, AVA de-identifies patients by blurring their faces. Second, the cameras only capture video—the software does not listen in on conversations. And third, all data is captured and stored in a secure, HIPAA compliant system.
Built to leverage existing infrastructure and grow over time
The new generation of advanced video analysis can make use of video technology that the hospital or health system is already invested in, whether that is carts, wall-mounted video cameras, or another system.
Plus, AI can be trained on and applied to new problems identified over time, or new risks. Whatever challenge your current virtual sitting solution is facing, there is a good chance that AVA can help.
AI and machine learning work hand-in-hand with video systems
AVA systems function in a hospital room
Patient privacy can be protected using AVA systems
AVA systems can benefit patient care and your bottom line.
Augmented Intelligence in Telehealth Holds Promise for Health Systems
If 2020 was the year that health systems embraced telehealth out of necessity and then discovered its many benefits, what might 2021 and beyond hold?
For health systems looking to further improve the cost savings and other advantages of telehealth, the new horizon is augmented intelligence: or, the use of artificial intelligence (AI) tools, such as machine learning, to assist and augment the capabilities of medical teams.
These tools can help with both routine administrative tasks and higher-level work, such as diagnosis, treatment, and patient monitoring.
Below we look at just a few of the helpful augmented intelligence tools that already exist in telehealth, preview potential future applications of augmented intelligence, and advise health systems on how best to take advantage of this new era in medical innovation.
Examples of augmented intelligence tools in telehealth and remote patient monitoring devices
The last few years have brought to market many remote patient monitoring devices that utilize augmented intelligence, enabling both hospital care staff and physicians to focus on other tasks while knowing that their patients are being continuously evaluated.
For example, EarlySense offers a sensor that is placed under a patient’s mattress and tracks multiple data points, including heart and respiratory rates. The sensor uses AI to analyze this continuous data stream and to detect early signs of deterioration, which the care team can then correct.
Similarly, Myia collects data from at-home patients with chronic conditions and uses machine learning to surface patients needing a clinical intervention.
Somatix offers the SafeBeing system, which is a remote patient monitoring device that relies on a wearable that uses AI to monitor gestures and passively collect biofeedback data. Somatix’s cloud-based system analyzes this data in real time to provide insights and alerts, such as an increased fall risk, for the care team. Somatix’s system works well for nursing homes and long-term care facilities.
Other companies are using AI to develop a more holistic portrait of patients’ health. Recognizing that clinical care accounts for only a small percentage of a patient’s health, with social determinants of health and behavior being major factors contributing to wellness, Innovaccer developed an AI-driven social vulnerability index that helps health systems see a fuller picture of both individual and population health.
We have also seen this type of technology being used to help with administrative tasks throughout various AI healthcare workflows.
For example, natural language processing, augmented by AI, can be used not only to transcribe patient-provider conversations during phone or video visits, but to assess which were the most salient points of the interaction and worthy of further attention. The resulting notes inform the provider’s care plan and also remind the patient of what was discussed.
In addition, as the Advisory Board wrote last year, at Providence St. Joseph Health in Washington and other health systems, system administrators have deployed chatbots to screen patients and direct them to the right resources, thereby discouraging the so-called worried well from unnecessarily coming into hospitals.
Future possibilities for augmented intelligence in healthcare
The possible future applications of augmented intelligence or AI in healthcare workflows are limited only by our collective imagination.
A Government Accountability Office Report envisioned that dermatology video visits may one day involve augmented intelligent patient care that assesses the patient’s skin for lesions and assists dermatologists in detecting precancerous and cancerous growths.
In this Becker’s Health IT article, a technology and data specialist with the University of California, Irvine, predicts that in the future individuals will have a digital health “twin” made up of all the data about an individual’s health. This twin’s data will continually be updated, and augmented intelligence tools will reveal health trends and trajectories for the individual as well as suggest personalized steps to better health.
Here, at Caregility, we predict that combining augmented intelligence with wearables and two-way video will be a game changer for virtual care, specifically when it comes to remote patient monitoring. Each of these components has had varied success to date in individual use cases, but when they are combined into a comprehensive virtual platform, providers will see the greatest benefit in improving care and reducing costs.
Taking advantage of the augmented intelligence revolution in telehealth systems
So, how can your health system benefit from all the latest applications in augmented intelligence in telehealth and be ready when new innovations reach the market?
To build a foundation for telehealth-enabled augmented intelligence technologies, the most critical step is adopting a flexible telehealth platform, capable of integrating with third-party apps and systems. Those who don’t start planning for the coming augmented intelligence healthcare transformation now may find themselves suddenly out-smarted and out-maneuvered: not by a human competitor, but by a learning machine.
For more on trends coming in the new year and beyond, check out our latest telehealth news roundup.
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_hjDoneTestersWidgets
Hotjar cookie. This cookie is set once a visitor submits their information in the Recruit User Testers widget. It is used to ensure that the same form does not re-appear if it has already been filled in.
365 days
_hjMinimizedTestersWidgets
Hotjar cookie. This cookie is set once a visitor minimizes a Recruit User Testers widget. It is used to ensure that the widget stays minimizes when the visitor navigates through your site.
365 days
_hjShownFeedbackMessage
This cookie is set when a visitor minimizes or completes Incoming Feedback. This is done so that the Incoming Feedback will load as minimized immediately if they navigate to another page where it is set to show.
365 days
_hjFirstSeen
The cookie is set so Hotjar can track the beginning of the user's journey for a total session count. It does not contain any identifiable information.
30 minutes
_hjAbsoluteSessionInProgress
The cookie is set so Hotjar can track the beginning of the user's journey for a total session count. It does not contain any identifiable information.
30 minutes
_hjptid
This cookie is set for logged in users of Hotjar, who have Admin Team Member permissions. It is used during pricing experiments to show the Admin consistent pricing across the site.
session
Vimeo is a video hosting platform for high-quality content, ideal for creators and businesses to showcase their work.
Collects analytical tracking information about videos and enables the player to function properly.
2 years
sd_identity
Collects analytical tracking information about videos and enables the player to function properly.
2 years
continuous_play_v3
Used to keep track of whether continuous play is on or not for a user
2 years
Player
This first party cookie created by Vimeo is used to remember user’s player mode preferences.
1 minute
vuid
This first party cookie created by Vimeo is used to assign a Vimeo Analytics unique id.
1 minute
Marketing cookies are used to follow visitors to websites. The intention is to show ads that are relevant and engaging to the individual user.
Name
Description
Duration
hubspotutk
Hubspot
1 day
__hstc
Hubspot
6 months
__hstc
Hubspot
1 day
__hssrc
Hubspot
1 day
__hssc
Hubspot
1 day
HubSpot is an all-in-one marketing, sales, and customer service platform that streamlines business growth.
Name
Description
Duration
hs-messages-hide-welcome-message
This cookie is used to prevent the chat widget welcome message from appearing again for one day after it is dismissed.
1 day
hubspotapi-prefs
This is used with the hubspotapi cookie to remember whether the user checked the 'remember me' box (controls the expiration of the main cookie's authentication).
1 Year
hubspotapi
This cookie allows the user to access the app with the correct permissions.
7 days
__hs_opt_out
This cookie is used by the opt-in privacy policy to remember not to ask the visitor to accept cookies again.
13 months
hubspotapi-csrf
This is used for CSRF prevention - preventing third party websites from accessing your data. Expires after a year.
1 year
__hs_do_not_track
This cookie can be set to prevent the tracking code from sending any information to HubSpot.
13 months
__hs_cookie_cat_pref
This cookie is used to record the categories a visitor consented to.
6 months
__hs_initial_opt_in
This cookie is used to prevent the banner from always displaying when visitors are browsing in strict mode.
7 days
__hs_gpc_banner_dismiss
This cookie is used when the Global Privacy Control banner is dismissed.
180 days
hs_ab_test
This cookie is used to consistently serve visitors the same version of an A/B test page they’ve seen before.
session
__hs_notify_banner_dismiss
This cookie is used when the website uses a Notify consent banner type.
180 days
hs-messages-is-open
This cookie is used to determine and save whether the chat widget is open for future visits.
30 minutes
__hsmem
This cookie is set when visitors log in to a HubSpot-hosted site.
1 year
hs-membership-csrf
This cookie is used to ensure that content membership logins cannot be forged.
session
hs_langswitcher_choice
This cookie is used to save the visitor's selected language choice when viewing pages in multiple languages.
2 years
__hstc
The main cookie for tracking visitors.
13 months
hubspotutk
This cookie keeps track of a visitor's identity. It is passed to HubSpot on form submission and used when deduplicating contacts.
13 months
__hssc
This cookie keeps track of sessions.
30 minutes
__hssrc
Whenever HubSpot changes the session cookie, this cookie is also set to determine if the visitor has restarted their browser.
session
messagesUtk
This cookie is used to recognize visitors who chat with you via the chatflows tool. If the visitor leaves your site before they're added as a contact, they will have this cookie associated with their browser.
13 months
LinkedIn Insight is a web analytics service that tracks and reports website traffic.
