Connected care introduces thousands of endpoints into a single hospital environment. Each device is a digital touchpoint, and a potential weak link. As adoption scales, so does risk. Cybersecurity in connected care is no longer a technical afterthought. It’s a frontline issue that touches everything from patient safety to regulatory compliance.

The Threat Landscape Is Bigger, Faster, And Riskier

Healthcare is one of the most attacked sectors in the digital economy. The U.S. Department of Health and Human Services (HHS) reported more than 100 major healthcare data breaches in 2024 alone. Over 133 million patient records were exposed. That’s not just a data issue, it’s a systems issue. A security failure at one point in the chain can ripple across a hospital’s entire network.

IoT makes this more complex. The FDA estimates over 500,000 connected medical devices are active in clinical environments today. Many were never built with modern security in mind. Older firmware, weak access controls, and lack of encryption are common. Every device added increases the attack surface. Every unsecured sensor or monitor is another opportunity for breach.

Speed is also a factor. Unlike other industries, healthcare doesn’t get downtime. Devices must operate continuously. This limits patch cycles and extends vulnerability windows. In connected care, even seconds count, and attackers know it.

Legacy Tech Meets Modern Threats

Healthcare has a legacy tech problem. According to Healthcare Information and Management Systems Society (HIMSS), several healthcare organizations still rely on outdated operating systems for critical equipment. These systems often can’t run modern security software. Some don’t support basic features like encryption or multi-factor authentication.

Why aren’t they upgraded? It’s complicated. In many cases, regulatory constraints prevent frequent updates. Devices cleared by the FDA can’t be modified without recertification. Interoperability concerns make hospitals reluctant to change hardware. Downtime risks make routine maintenance difficult. So vulnerabilities linger, sometimes for years.

There’s also a dependency issue. Many devices can only be updated by the original manufacturer. That means hospitals often wait weeks, or longer, for critical patches. During that time, threats accumulate. One vulnerable infusion pump, left exposed, can compromise not just itself but the entire network segment it touches.

The Real Cost of a Breach

Cyberattacks in healthcare come with a price tag. A steep one. According to IBM’s 2023 Cost of a Data Breach Report, healthcare remains the most expensive industry for breaches, averaging US$ 10.93 million per incident. That includes forensic response, system rebuilds, legal fees, regulatory fines, and lost revenue. But financial damage is only part of the story.

Breaches also disrupt care. Diagnostic tools go offline. Patient records become inaccessible. Scheduled surgeries are delayed or canceled. In worst-case scenarios, clinical decisions are made without full visibility, raising the risk of medical errors.

Ransomware attacks are especially disruptive. Some hospitals have had to divert emergency patients or revert to paper-based systems. The impact is immediate, visible, and dangerous. These aren’t theoretical risks. They’re unfolding across the country in real time.

Also Read: How AI-Powered Healthcare Assistants Are Alleviating Physician Burnout

Regulation Responds

Federal agencies are tightening their grip on medical device security. In 2023, the FDA finalized new premarket cybersecurity guidance. Now, any internet-connected medical device submitted for approval must include a Software Bill of Materials (SBOM), threat modeling documentation, and a lifecycle update plan.

The PATCH Act adds more pressure. Passed into law in late 2022, it requires manufacturers to design devices with built-in security support, including the ability to receive timely updates and patches. Devices that lack encryption, monitoring, or update capabilities now face hurdles at the approval stage.

On the provider side, the Centers for Medicare & Medicaid Services (CMS) has linked cybersecurity readiness to quality improvement assessments. That means hospitals are financially incentivized to implement robust digital protections. It’s no longer just good practice, it’s a performance metric.

NIST is also stepping in. The updated Cybersecurity Framework (CSF 2.0) introduces tailored controls for connected medical environments. Special Publication 1800-30 walks through the steps for securing wireless infusion pumps, a real-world use case. This is not theoretical policy. It’s practical, implementable guidance built for the healthcare sector.

Inventory Chaos Includes Too Many Devices, Too Little Visibility

One of the most overlooked risks in connected care is device sprawl. A modern hospital doesn’t just have servers and laptops, it hosts thousands of connected devices across floors, wings, and departments. Many are portable. Many are unmanaged. And many fly completely under the radar.

According to a 2023 Medigate and Philips study, a 500-bed hospital typically runs over 10,000 connected medical devices. That includes everything from infusion pumps to smart thermometers. Most hospitals don’t have full visibility into that inventory. Devices move. Locations shift. Documentation is incomplete.

The result? Shadow IoT. These are devices that live on the network but aren’t tracked, protected, or monitored. They evade firewall policies. They miss antivirus sweeps. And when breached, they’re hard to trace.

Improving visibility is step one. Passive network monitoring tools can detect, identify, and profile devices in real time, without disrupting patient care. They analyze communication behavior and flag anomalies automatically. This kind of behavioral fingerprinting is critical. If a thermometer starts acting like a file server, something’s wrong, and the system knows it.

Isolation as a Defense

Once device visibility is established, segmentation becomes the next defense layer. It’s simple in principle, separate critical clinical systems from general-purpose IT networks. In practice, it’s a powerful way to limit damage if a breach occurs.

Healthcare providers are beginning to adopt Zero Trust security models. These frameworks assume that no device, user, or request is trustworthy by default. Every interaction is verified continuously based on context, who is requesting access, what they’re accessing, and why.

Zero Trust is especially effective in high-stakes environments like operating rooms or ICUs. In these zones, every millisecond counts. There’s no room for broad network access or outdated trust models. Role-based access, encrypted communications, and policy-based segmentation can keep the mission running, securely.

Security Begins Before Deployment

Cybersecurity in connected care is not limited to hospitals and clinics. Risks start earlier, deep in the medical device supply chain. Many hospitals now depend on off-the-shelf IoT components sourced globally. These parts often ship with pre-installed firmware. Some contain hidden vulnerabilities. Others lack critical security features like secure boot or signed updates.

Regulators and agencies are responding. The Cybersecurity and Infrastructure Security Agency (CISA) now recommends that healthcare procurement teams embed cybersecurity requirements into their vendor agreements. Device manufacturers must show how they test code integrity, manage access control, and deliver long-term patch support.

The Health Industry Cybersecurity Practices (HICP) framework from HHS supports this shift. It advises tiered assessments based on device risk level. Low-risk wearables are treated differently from high-risk surgical systems. This kind of granular oversight is essential. It helps institutions prioritize their defenses based on clinical impact.

Hospitals must start thinking like supply chain auditors. Each product acquired should meet a cybersecurity standard before it ever touches the network. Otherwise, the risk enters through the front door.

People Are the Perimeter

No cybersecurity strategy is complete without people. In a connected care setting, that includes doctors, nurses, technicians, and administrators. Many of them interact with IoT systems daily. Few have formal training on how those systems can be exploited.

Human error remains a top vector for attack. Phishing emails. Misconfigured devices. Plugging unsecured hardware into protected systems. All of these actions can bypass even the most advanced defenses.

Cybersecurity culture cannot be siloed. It needs to be visible in onboarding, ongoing education, and everyday workflows. Healthcare workers are not just caregivers. In a connected system, they are also frontline defenders.

AI and Automation Is The Next Frontier

Healthcare generates data nonstop. Connected devices transmit thousands of signals every second. AI is emerging as a critical tool for making sense of that volume and identifying threats in real time.

Machine learning algorithms can spot behavioral anomalies long before a signature-based system would. A heart monitor that suddenly initiates outbound data transfers is flagged. A nurse’s tablet connecting to an unauthorized server gets locked down. These systems learn from device patterns, not just predefined rule

As 5G and edge computing expand, connected care environments will grow more complex. Predictive threat intelligence systems will be essential for managing that complexity. Hospitals are not just looking for alerts. They want actionable insights. What happened, why it happened, what to do next. AI systems can offer that visibility without overwhelming human operators. When integrated correctly, they become force multipliers for small IT teams.

Future-Ready Cybersecurity

Connected care is only getting more connected. From remote diagnostics to autonomous delivery robots, the number and variety of devices in healthcare will keep expanding. The cybersecurity strategy must evolve just as quickly.

What does that look like? It starts with scalable frameworks. Hospitals need modular architectures that support new devices without breaking existing protections. They also need lifecycle management. A device should be tracked from procurement to decommissioning, with full security oversight at each stage.

Expect more federal oversight. Agencies are moving toward mandatory cybersecurity certification for medical devices. Hospitals that meet best practices could see enhanced reimbursements or reduced penalties after a breach. Those that do not may face increased scrutiny.

Cross-sector collaboration will also increase. Government, manufacturers, providers, and academic institutions are forming consortia to develop open standards and shared threat intelligence. The goal is faster response, better resilience, and fewer surprises. Cybersecurity in connected care will no longer be a static checklist. It will become a dynamic, living process that adjusts in real time. That is what the ecosystem demands.

Conclusion

Securing IoT in healthcare is no longer optional. It is foundational. Connected care technologies bring speed, accuracy, and efficiency to clinical workflows. But they also reshape the security perimeter. Risks are more distributed, more sophisticated, and more urgent.

Hospitals and providers must move from reactive defenses to proactive architecture. That means investing in device visibility, segmentation, supply chain controls, and cultural readiness. It also means embracing automation and AI to scale response efforts. The future of connected care is already here. Its safety will depend on how well the healthcare ecosystem secures every device, every connection, and every decision point.

The post Cybersecurity in Connected Care: Securing IoT in the Healthcare Ecosystem appeared first on ITDigest.

Step 1: Establish Scope, Objectives, and Stakeholder Buy-in

Every successful CRQ initiative starts with clarity of purpose and alignment across the organization.

Define Scope and Objectives:

Decide what you want to quantify (e.g., enterprise-wide risk, specific business units, critical assets) and why (regulatory compliance, board reporting, investment prioritization, insurance optimization). Be explicit about the audience for your results – Are you informing the board, operational teams, or both?

Identify Stakeholders:

Form a core CRQ team that includes risk managers, cybersecurity leaders, finance, and business unit representatives. Extend the team to include data owners and process experts who can provide critical input.

Secure Executive Sponsorship:

CRQ requires cross-functional collaboration and access to sensitive data. Executive support is essential for breaking down silos and ensuring cooperation.

Set Success Metrics:

Define what success looks like; be it improved risk visibility, better investment decisions, reduced insurance premiums, or regulatory compliance.

Before starting CRQ, it is crucial to have a clear picture of the scope. This includes the target audience for the quantification results, and whether this is targeting top management for strategic decision making, or more technical stakeholders for operational decision making.

Step 2: Inventory and Prioritize Information Assets

CRQ is only as good as the data and context you feed into it. Start with a comprehensive asset inventory.

Catalog Digital Assets:

List all hardware, software, data repositories, cloud services, and third-party connections. Include both internal and external assets.

Assess Asset Criticality:

Assign a criticality rating to each asset based on its business importance, data sensitivity, and potential impact if compromised. Use risk matrices to visualize severity and prioritize assets for deeper analysis.

Map Dependencies:

Understand how assets relate to business processes and each other. Even seemingly minor assets could serve as attack vectors to more critical systems.

Update Continuously:

Asset inventories must be living documents, updated as your environment evolves.

Assigning criticality ratings to all internal and external assets reduces the amount of data that needs to be processed in the CRQ process. Risk matrices play a valuable role here, as they visually map the severity of risks across digital assets and third-party vendors, helping organizations focus on the most significant vulnerabilities.

Step 3: Select a Quantification Methodology and Gather Data

With your asset landscape mapped, it’s time to choose a quantification model and collect the necessary inputs.

Choose a Framework:

The Factor Analysis of Information Risk (FAIR) model is a widely recognized standard for CRQ, translating risk into financial terms using probability and impact. Other frameworks like ISO 27005, NIST SP 800-53, and COBIT 5 are also used, but FAIR is especially used for board-level communication.

Define Risk Scenarios:

Identify specific threat scenarios relevant to your business (e.g., ransomware attack, data breach, third-party compromise). Avoid overlap and double counting by focusing on business consequences, not just technical causes.

Collect Data:

Gather historical incident data, vulnerability assessments, threat intelligence, and business impact information. You’ll need both technical (frequency, vulnerability, controls) and financial data (cost of downtime, legal fees, lost revenue, insurance limits).

Leverage Technology:

Use CRQ platforms or tools to streamline data collection, run simulations (such as Monte Carlo), and generate range-based estimates for loss expectancy.

Validate Inputs:

Involve stakeholders to verify assumptions and ensure data quality. Where data is lacking, use industry benchmarks or vendor-provided estimates.

With FAIR, asset-based risks can be quantified per their threat and vulnerability exposure leading to the calculation of the final dollar value at risk. Monte Carlo simulation can also be triggered by users to generate a range-based estimate and predict the probability of different outcomes for the Annual Loss Expectancy.

Step 4: Calculate, Analyze, and Prioritize Cyber Risks

Now, translate your data into actionable insights.

Perform Quantitative Analysis:

For each scenario, calculate risk as:

Cyber Risk = Likelihood of Event × Financial Impact

This yields a dollar-value estimate of potential loss, which can be annualized or scenario-based.

Consider Risk Factors:

Factor in vulnerability severity, threat level, asset exposure, and effectiveness of existing controls. For impact, include direct costs (remediation, legal, notification) and indirect costs (reputation, lost business).

Prioritize Risks:

Focus on the highest-value risks, those with the greatest potential financial impact or likelihood. Use the results to build a prioritized roadmap for risk mitigation, rather than spreading resources thinly across all threats.

Benchmark and Contextualize:

Compare your quantified risks to industry peers or regulatory thresholds to justify investments and set realistic risk reduction goals.

Prioritize among risks based on probable loss exposure in dollars. Communicate to the board and the business clear financial goals for risk reduction.

Step 5: Communicate, Act, and Continuously Improve

The final step is to turn your analysis into action and ensure your CRQ program evolves with your business.

Translate Results for Stakeholders:

Present findings in business terms – dollar values, risk reduction ROI, and clear recommendations. Tailor communication for boards, executives, and operational teams.

Document and Report:

Maintain clear records of your risk quantification process, assumptions, and results. This supports regulatory compliance and enables repeatable, defensible decision-making.

Drive Action:

Use CRQ outputs to justify cybersecurity investments, inform insurance negotiations, and guide incident response planning. Focus on remediating the most impactful risks first.

Monitor and Refine:

Cyber risk is dynamic. Regularly update your models, data, and assumptions as your threat landscape, business priorities, and technology stack evolve. Track the effectiveness of risk reduction efforts and adjust as needed.

Foster a Risk-aware Culture:

Keep stakeholders engaged and informed, building a shared sense of responsibility for cyber risk management across the enterprise.

To get long term benefits from CRQ, you need to plan for repeatable and consistent quantifications. It is important to be able to explain changes in the quantification model itself, as well as the changes to the organization and wider environment that lead to changes in the inputs and consequently the outputs.

Real-world Impact

• Bystronic’s CISO halved cyber risk exposure using quantified metrics to drive targeted action.
• A large retail corporation prioritized 12 major projects with projected ROI ranging from 17% to 165% using CRQ.
• A financial services firm eliminated US$ 22 million in cybersecurity risk and secured executive buy-in for increased security investment.

These examples underscore the value of a disciplined, data-driven CRQ approach: measurable risk reduction, smarter investment, and stronger alignment between cybersecurity and business objectives.

Conclusion

Building a successful Cyber Risk Quantification program is not a one-off project. It’s an ongoing journey that transforms cybersecurity from a technical silo into a strategic business enabler. With this 5-step guide, your organization can move beyond compliance and fear-driven spending-toward a future where cyber risk is understood, managed, and mitigated in business terms. By following these five steps, your enterprise can:

• Bridge the gap between technical risk and business impact
• Prioritize investments for maximum risk reduction
• Communicate effectively with boards and executives
• Demonstrate the ROI of cybersecurity initiatives
• Foster a culture of resilience and informed decision-making

In a world where cyber threats are ever-present and ever-evolving, CRQ is your enterprise’s compass. It points the way to smarter, more confident, and more effective risk management.

The post 5 Steps to Build a Successful Cyber Risk Quantification Program for Your Enterprise appeared first on ITDigest.

Understanding the Burnout Crisis

Physician burnout is more than a buzzword. It’s a big problem. It causes emotional exhaustion, depersonalization, and less feeling of accomplishment. The causes are complex. They include a lot of EHR paperwork, red tape, and the stress of patient care. Nearly half of all doctors say they feel burnt out. Many are thinking about retiring early or changing jobs. The ripple effects are serious. They include compromised patient safety, higher turnover rates, and rising healthcare costs.

The traditional response has been to encourage self-care or mindfulness. Yoga classes and wellness apps can’t fix the real issue. The system is broken. It overwhelms clinicians with paperwork and takes away their time with patients. AI comes in here, not to replace human skills but to help with repetitive tasks.

AI as an Administrative Workhorse

Imagine a world where physicians spend less time typing and more time healing. AI assistants are making this vision real. They automate the most time-consuming parts of healthcare. Take EHR documentation, for example. Clinicians routinely devote hours daily to updating records—a process riddled with inefficiencies. NLP algorithms make this task easier. They turn voice conversations into structured clinical notes. Tools like Nuance’s Dragon Medical One and Ambience Healthcare’s AI scribe listen to talks between patients and doctors. They extract important information and fill EHRs instantly. The result? A dramatic reduction in after-hours charting and cognitive strain.

Prior authorization is a well-known barrier in care delivery. It’s a key area ready for change. Insurers often ask doctors for long explanations for treatments. This delays care and frustrates providers. AI systems with predictive analytics can automatically create prior authorization requests. They do this by checking clinical guidelines and patient histories. A new survey conducted by the Harris Poll and Google Cloud revealed that over 90% of healthcare workers feel optimistic about the potential of generative AI (GenAI) to alleviate administrative burdens. This lets doctors focus on more complex cases.

Enhancing Clinical Decision-Making

Beyond administrative relief, AI assistants are proving invaluable in clinical settings. Diagnostic uncertainty is a significant stressor for physicians, particularly in primary care. AI algorithms use large datasets to analyze symptoms, lab results, and imaging studies. They can suggest possible diagnoses or highlight any unusual findings. PathAI’s tools help pathologists find cancerous cells more accurately. Meanwhile, K Health provides AI-driven symptom checkers. These empower patients to assess their symptoms before going to a clinic.

These tools don’t undermine physician expertise—they augment it. AI makes it easier by providing evidence-based recommendations. This reduces mental fatigue. You won’t have to sift through endless research or second-guess your decisions. A Stanford study found that doctors who used AI tools felt less stress. They also felt more confident in their treatment plans.

Also Read: How Natural Language Processing(NLP) is Transforming Medical Coding 

Humanizing Patient Interactions

Paradoxically, AI is also helping physicians reconnect with the human side of medicine. Burnout often comes from feeling like just a part of a machine. This feeling gets worse with quick appointments and red tape. AI chatbots and virtual assistants help reduce the strain. They manage routine patient communications. Sensely’s virtual nurse, “Molly,” does post-discharge check-ins. She answers medication questions and tracks chronic conditions using conversational AI. This allows physicians to delegate follow-ups without sacrificing continuity of care.

AI tools like Woebot support mental health. They provide patients with cognitive behavioral therapy (CBT) techniques. This support eases the workload for psychiatrists. These platforms aren’t a replacement for human therapy. Instead, they help patients get timely support while clinicians focus on critical cases.

Case Studies

The proof lies in the trenches. At Mercy Medical Center in Baltimore, an AI scheduling system cut physician overtime. It did this by studying patient flow and improving shift patterns. Doctors saw less burnout and felt more in control of their schedules. The University of Pittsburgh Medical Center also added an AI assistant. It helps with prescription refills and lab result notifications. The tool saved each clinician about three hours a week. They can use this time for patient care or for themselves.

In rural clinics, there are often not enough doctors. AI is helping to change that. A telehealth platform in New Mexico uses AI to help with emergency cases. It links patients with specialists. It also turns medical terms into simple language for those who don’t speak English. For busy providers, this technology isn’t just helpful; it’s vital for survival.

Navigating Challenges and Ethical Considerations

Adopting AI isn’t without hurdles. Concerns about data privacy, algorithmic bias, and the depersonalization of care loom large. A poorly designed AI tool can increase burnout. Clinicians may need to double-check its results or deal with awkward interfaces. Transparency is key. Health systems need to include doctors in the design process. This helps ensure tools fit with workflows instead of disrupting them.

Ethical questions also arise. Should an AI assistant prioritize efficiency over patient rapport? How do we prevent algorithms from perpetuating disparities in care? Leaders should promote strong testing. They need regular feedback from clinicians. Also, they should establish solid governance frameworks to lower these risks.

The Road Ahead

The future of AI in healthcare isn’t about machines replacing humans—it’s about partnership. Picture this: an AI assistant that knows what a doctor needs. It creates tailored treatment plans and spots early burnout signs. It does this by analyzing speech patterns or how doctors use electronic health records (EHR). Startups like Notable are already experimenting with such predictive capabilities.

For health systems, the ROI extends beyond productivity metrics. Reduced burnout translates to lower recruitment costs, higher retention, and better patient outcomes. Investors and executives should focus on long-term gains instead of short-term savings. Clinician well-being is closely linked to the success of the organization.

Conclusion

AI-powered healthcare assistants are not just a luxury. They are an ethical necessity. As burnout continues to destabilize the workforce, health systems must act decisively. Test AI tools in important areas like documentation, prior authorization, and patient communication. It’s also important to create a culture of openness. Physicians should see AI as an ally, not a threat.

Tech innovators face the challenge of creating solutions that are intuitive and ethical. These solutions must also respect the important clinician-patient relationship. The goal isn’t to make perfect machines. It’s about empowering healers. We want to give them time, space to breathe, and the freedom to practice medicine purposefully. In this quiet change, the true win is not just in tech skills. It’s about bringing humanity back to healthcare.

The post How AI-Powered Healthcare Assistants Are Alleviating Physician Burnout appeared first on ITDigest.

Recent research from the Uptime Institute points to the painful costs of outages. 16% of respondents stated their most recent outage cost more than $1 million, and 54% said costs exceeded $100,000, reinforcing the need for automating IT operations so mission-critical applications and data can be available locally.

“With the rise of edge computing, IoT solutions, front-line interactions, perimeter threats, and hybrid workers, detailed visibility into remote resources, exchanges, and application experiences is more critical than ever,” said Mark Leary, research director, network analytics and automation, IDC. “Key functions such as scalable deep packet inspection and synthetic transaction testing provide comprehensive views and valuable control mechanisms for the digital infrastructure, bolstering operational efficiency, worker productivity, customer satisfaction, security posture, and, ultimately, financial performance.”

Also Read: Conceal Announces Pioneering Partnership with Fortem Information Technology to Elevate Global IT Solutions 

The increased complexity of SASE, SD-WAN, SaaS, and UCaaS service usage further challenges ensuring high-quality digital experiences at remote sites. Traditional methods, including application point tools, synthetics-only, user self-diagnosis, and ad hoc troubleshooting, are ineffective and time-consuming. Since IT teams often have limited resources at remote locations, they require instrumentation that provides a combination of synthetic transaction capabilities and deep packet inspection (DPI) to prevent visibility gaps and determine the root cause quickly.

NETSCOUT Business Edge Observability offerings combine the power of the company’s patented Adaptive Services Intelligence^® (ASI) technology with synthetic testing and automatic decryption together to deliver comprehensive visibility that scales to any essential business edge. For remote sites, nGenius Edge Sensors and Remote InfiniStreamNGs become the eyes and ears of an IT team, reducing the time to troubleshoot and resolve issues.

“Consider the intricacies of hospitals,” said Bruce Kelley, Chief Technology Officer, NETSCOUT. “When electronic patient records, or imaging and radiology applications, are unavailable, the impact extends beyond the downtime cost. Hospital applications are business-critical, supporting patient safety, patient treatment, customer services, and the overall trust of the institution. A service disruption related to performance or security is more than a major business issue as patient care is on the line.”

SOURCE: Businesswire

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“As enterprises increasingly converge IT and OT resources — including cloud endpoints and unmanaged, unagentable assets — they require enterprise-ready solutions that enforce consistent governance and control,” said Barry Mainz, CEO, Forescout. “Together, we empower security leaders to integrate and enhance their security programs seamlessly.”

Built on Azure: Forescout Risk and Exposure Management (REM)

Forescout REM, a comprehensive, AI-enhanced asset intelligence solution, is now built on Azure. REM provides enterprises with an automated and multi-factor risk-prioritization approach to remediate vulnerabilities and strengthen the security posture across the attack surface. Enterprises can now accurately assess, classify, and quantify the risk severity and exploitability of every connected asset, managed or unmanaged. This development marks a significant step in providing advanced cyber-defense capabilities to organizations using the Azure cloud platform with control, reliability, speed and scale.

Also Read: SentinelOne Transforms Cloud Security for AWS Customers 

Forescout Risk and Exposure Management Integration with Microsoft Copilot for Security Defines Seamless Enrichment for Security Operations

Forescout REM now has an integration with Copilot for Security, designed to dynamically query the product to provide specific details about a device’s risk posture, exposure, and historical trends. Combining the contextual data from Forescout REM with Copilot for Security allows security teams to quickly assess the risk posed across their environments and prioritize work by getting clear information on exposure to different vulnerabilities or configurations that exist. Benefitting from the power of the large language model behind Copilot for Security, this enriched data seamlessly integrates with Microsoft products, offering a comprehensive end-to-end view of security threats.

Forescout Research Expands Access to Threat Intelligence with Microsoft Copilot for Security Integration

In another move, Forescout introduces a Copilot for Security plugin that accesses Forescout’s invite-only threat intelligence service powered by Vedere Labs, a leading global team dedicated to uncovering vulnerabilities in and threats to critical infrastructure. The Forescout Research Vedere Labs team provides a threat intelligence feed containing IP, URL and file hash indicators, attack mapping and actor intel for all activity monitored by Forescout. This includes information on Known Exploited Vulnerabilities and Vedere Labs reported CVE’s. This service takes one of the industry’s leading threat intel data lakes for IoT and OT and correlates security events detected by Microsoft products with data sourced from Forescout’s rich threat intelligence using advanced AI algorithms.

SOURCE: Businesswire

The post Forescout Marks Innovation Milestones in Strategic Partnership with Microsoft appeared first on ITDigest.

SKT and SGH also intend to leverage their complementary capabilities to enhance customer offerings in the development of differentiated global end-to-end AI factory and data center solutions and services, advanced memory market products and services, and NPU-based AI edge servers.

“We are thrilled to have SKT as a strategic investor,” said Mark Adams, CEO of SGH. “Today’s announcement is a testament to Penguin Solutions’ capabilities in the deployment of AI factories at scale through our systems, software, and managed services suite of solutions. We look forward to executing on strategic collaboration opportunities with the SKT team and believe they will create value for our respective stakeholders.”

Also Read: Cognigy Selected as Winner for Artificial Intelligence Innovation in 2024 AI Breakthrough Awards Program 

Ryu Young-sang, CEO of SKT added, “SGH and Penguin Solutions have a proven methodology to deploy their AI infrastructure solutions at some of the most innovative and demanding large-scale enterprise customers in the world. We believe that this collaboration leveraging SKT’s vast technology portfolio with Penguin’s AI data center solutions would enhance the reach and capabilities of both companies and position them to continue innovating in their fields.”

Penguin Solutions has demonstrated its premier technology position through the development and deployment of innovative AI-driven technologies, such as high-performance and high-availability systems design, fluid-based immersion cooling, “zero fail” memory solutions, end-to-end managed services offerings, and its AI cluster management software suite. Penguin Solutions presently has over 75,000 GPUs under management, among the most in the world.

SKT’s current AI-related investments and partnerships include Anthropic, Lambda, and Perplexity. Along with these investments, a partnership and investment with SGH will advance SKT’s goals in the AI value chain across these three pillars: AI semiconductor, AI infrastructure and AI services.

SOURCE: Businesswire

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What Is Healthcare Data Analytics?

Healthcare analytics is the analysis of current and historical industry data to predict trends, reach more patients, and manage disease spread. It covers all types of businesses and gives insights at the macro- and micro-level. It can show paths to improvement in patient care, clinical data, diagnosis, and business management.

When combined with business intelligence tools and data visualization, healthcare analytics helps managers operate better by giving them real-time information to support decisions and actions.

Types of Healthcare Data Analytics

There are many types of healthcare data analytics. Here are four:

1. Descriptive Analytics: Descriptive analytics uses past data to give insights into trends or benchmarks. It helps healthcare professionals know what has happened in the past and provides a basis for decision-making. For example, descriptive analytics can be used to analyze patient demographics, track disease prevalence, or monitor healthcare usage.
2. Diagnostic Analytics: This field of study examines the causes of certain medical outcomes. Finding patterns, connections, and linkages using data entails data analysis. Healthcare practitioners can ask questions like “What contributed to this health outcome?” and “Why did this outcome happen?” with the aid of diagnostic analytics.
3. Predictive analytics: This technique forecasts future occurrences or results by utilizing statistical models and past data. It supports the trend-spotting, risk-awareness, and decision-making of medical professionals. Predictive analytics, for instance, can be used to identify high-risk individuals, forecast disease outbreaks, and project the amount of healthcare resources required.
4. Prescriptive Analytics: Beyond just forecasting results, prescriptive analytics also provides instructions on how to get the intended results. The system integrates optimization techniques, predictive models, and historical data to recommend the optimal course of action. Healthcare workers may ask better questions like “What should we do to improve patient outcomes?” and “What interventions are best for a certain condition?” with the use of prescriptive analytics.

Also Read: Augmented Reality in Healthcare: A Comprehensive Guide 

Benefits of Data Analytics in Healthcare

So what’s the big deal with healthcare data analytics? Well, everyone’s all about giving patients the best care possible and making healthcare organizations run smoother. There are many benefits to using healthcare data analytics. Check it out:

• Extensive electronic health records: Data analytics allows us to maintain extremely thorough records of a patient’s medical history. This aids medical professionals in making more informed treatment decisions.
• Clinical decision-making support: Data analytics can also assist physicians in determining what’s best for their patients. With access to all the information, they may make decisions with more knowledge.
• Alerts for providers in real-time: When something happens to a patient, physicians and nurses can receive alerts in real-time thanks to data analytics. They can maintain superior care and organization as a result.
• Deep comprehension of patient health and response to therapy: By analyzing all the data, we may gain a very comprehensive understanding of a patient’s health and response to treatment. This enables us to adapt and provide more individualized service.
• Efficient healthcare and cost savings: Lastly, using data analytics can make healthcare more efficient and save money. We can see where we’re spending too much or not getting results and make changes to improve.

Challenges of Healthcare Data Analytics

• Data Quality and Integration: Healthcare data comes from many sources and is complex and unstructured. Getting quality data and integrating data from different systems is hard. Incomplete or inaccurate data, data inconsistencies, and a lack of interoperability between EHR systems can prevent data from being analyzed.
• Privacy and Security: Healthcare data is personal and sensitive, so privacy and security are top of mind. Protecting patient data from unauthorized access, compliance with privacy regulations (like HIPAA), and data security throughout the analytics process is a big challenge.
• Data Governance: Data governance is key to successful healthcare data analytics. This means defining data ownership, data accuracy and consistency, and data governance policies and procedures. Lack of data governance can cause data quality issues and stop analytics initiatives.
• Skills and Expertise: Healthcare organizations need people who can analyze and interpret healthcare data. But there are not enough data analysts and data scientists with healthcare analytics experience. Organizations need to invest in training and upskilling to build an analytics team.

How Do These Challenges Affect Patients?

Healthcare data analytics challenges affect patients in many ways. Firstly, they impact the care patients receive. Data quality issues and data integration problems can lead to misdiagnosis and treatment plans that don’t work and harm patient outcomes. Secondly, healthcare data analytics challenges limit patients’ access to care. Patients are hesitant to share their data due to privacy and data security concerns, and data analytics can’t improve healthcare outcomes. And in rural areas, access to care disparities exacerbate healthcare inequities. Healthcare organizations must address these challenges so patients get high-quality, equitable, and patient-centered care.

Final Takeaway

Healthcare data analytics have become a game-changing force in the industry. We have looked at the various ways that data analytics enhances patient care during our conversation. Healthcare companies may find risk factors, customize treatment regimens, diagnose patients more quickly and accurately, increase operational effectiveness, and make evidence-based decisions by utilizing data. These developments could result in far improved patient outcomes, more efficient use of resources, and a healthcare system that is more focused on the needs of the individual patient.

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The Pylo GL1-LTE is designed for use by patients enrolled in remote patient monitoring (RPM) programs for diabetes. With roaming 4G connectivity, the device requires no configuration and securely transmits real-time readings to patient care teams. Rigorously tested, FDA cleared, and FCC certified, the GL1-LTE delivers plasma glucose results with accuracy comparable to laboratory testing equipment. Patients benefit from an intuitive user interface, an easy-to-use test strip ejector mechanism, and a convenient carrying case that comes standard.

“Clinical evidence shows that remote blood glucose monitoring is associated with improved glycemic control for diabetic patients,” says Daniel Tashnek, founder and CEO of Prevounce. “Reliable devices and consistent readings are essential for RPM programs to realize their full clinical and financial potential. We’re excited to introduce a new Pylo glucometer that is not only dependable and accurate but also comfortable and simple for patients to use.”

Also Read: AiM Medical Robotics and Brigham Women’s Hospital for a First Human Clinical Study for MRI-guided Device Placement in the Brain 

Healthcare organizations can personalize the GL1-LTE blood glucose meter to deliver a consistent brand experience. Pylo devices and their packaging are designed for easy customization, allowing for the addition of logos, taglines, patient inserts, and more. Prevounce also offers clients inventory and logistics support. Devices or multi-device kits can be shipped to organizations or individual patients anywhere in the United States within two business days. Return and refurbishment services help ensure organizations get the most out of their device investment.

Prevounce delivers comprehensive cloud software, cellular-connected devices, and expert services that help healthcare organizations simplify the provision of remote patient monitoring, chronic care management, preventive care, and annual wellness visits. Through its Pylo line of remote patient monitoring devices, Prevounce offers blood pressure monitors, weight scales, and blood glucose meters, with additional devices under development. Devices connect natively with the Prevounce remote care management platform and integrate with other health software via the Pylo cloud API.

SOURCE: PRNewswire

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Aidentified provides financial advisors, marketing teams, and other sales professionals with tools to identify, qualify, and convert new client relationships. Aidentified will use the investment to fund further technological development and grow its team as client adoption accelerates. Aidentified currently serves clients at top financial services firms and independent registered investment advisors.

workforce is powered by sophisticated technology that can significantly streamline time-intensive and laborious tasks. Our artificial intelligence platform helps wealth advisors, financial planners and others in similar roles identify, connect with and build valuable relationships with prospective clientele more easily,” said Tom Rauker, President and Co-CEO of Aidentified. “Our platform’s rich dataset enables anyone to spend their prospecting time more strategically. We’re thankful to the team at FactSet for their support and investment as we continue on our growth journey.”

Also Read: YourStake Announces Collaboration with Apex Fintech Solutions Inc to Deliver The Building Blocks For The New Paradigm Of AI-First Fintech 

Aidentified gives users the ability to search for specific identity markers such as income, wealth segment, location, and hobbies, along with verifiable contact information. Further, it employs AI and machine learning-based algorithms and models to visualize user connections with prospects and harness its clients’ networks for referrals. As of 2024, the company has mapped 16 billion 1st-degree connections and 800 billion 2nd-degree connections, making Aidentified a leading provider of relationship data.

“There is currently no other technology that surfaces key personal and professional connections to the same level of detail as Aidentified. We believe that the Aidentified service, in combination with FactSet’s public and private company data and analytical capabilities, will dramatically accelerate prospecting and business development efforts for advisors and other financial professionals,” said Greg King, Head of Wealth Management at FactSet. “The team at Aidentified has a proven track record of building successful businesses, and we’re pleased to support their ongoing work.”

SOURCE: PRNewswire

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The non-exclusive agreement introduces Telstra’s network APIs to the Vonage platform, giving enterprises and developers the opportunity to leverage network capabilities to create innovative, advanced applications and to enhance the customer experience. The partnership complements Telstra’s T25 ambition to grow and extend its API leadership position, while opening up new opportunities for Vonage in the wider Asia Pacific region.

Network APIs are key to uncovering new capabilities for use by developers from within the 5G network that have never before been exposed and made available in an easy, consumable way. Vonage and Telstra will collaborate on and continue investing in enhancing existing APIs, such as Silent Authentication – a new verification method that provides secure authentication without requiring users to input passwords or verification information. Telstra and Vonage will also work together to identify new open APIs to bring to market such as slicing, edge computing, fraud and spam protections, and network analytical insights, while exploring the integration of Telstra’s APIs with Vonage’s API platform.

Also Read: Informatica Summit in Saudi Arabia to Accelerate Path to Cloud-Powered Digital Transformation 

“By combining Vonage’s API platform, Ericsson’s 5G network capabilities and Telstra’s network APIs, we are providing developers and enterprises the tools they need to create new, secure, reliable and engaging solutions,” said Niklas Heuveldop, Vonage CEO and Head of Business Area Global Communications Platform for Ericsson. “Together with frontrunner service providers like Telstra, we are driving an open ecosystem for exponential innovation that will enable the development of new applications that transform how enterprises operate and engage with customers, while monetising the network.”

Kim Krogh Anderson, Telstra’s Group Executive for Product and Technology said: “We see this partnership with Vonage as one of a variety of ways we can evolve and build a thriving ecosystem around our network product. Opening up new avenues for connectivity to be consumed will lead to new monetisation opportunities – in the same way hyperscalers monetise compute on their cloud as a platform. This is just one of the ways we’re addressing the Business-to-Business (B2B2X) developer market opportunity.”

Vonage, a global cloud communications leader, helps businesses accelerate their digital transformation. Vonage’s Communications Platform is fully programmable and allows for the integration of Video, Voice, Chat, Messaging, AI and Verification into existing products, workflows and systems. The Vonage conversational commerce application enables businesses to create AI-powered omnichannel experiences that boost sales and increase customer satisfaction. Vonage’s fully programmable unified communications, contact center and conversational commerce applications are built from the Vonage platform and enable companies to transform how they communicate and operate from the office or remotely – providing the flexibility required to create meaningful engagements.

SOURCE” Businesswire

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