Why Third-Party Validation for Healthcare AI is Crucial

Seeking external validation for healthcare AI offers essential benefits:

• Builds trust by assuring patients, clinicians and payers that the AI tool meets independent quality and safety standards.
• Reduces risk by guiding ethical development, data governance and risk management, thereby mitigating potential harm.
• Accelerates market access by differentiating products and streamlining procurement for hospitals and health systems.
• Ensures regulatory readiness by aligning with changing standards and policies, supporting compliance and long-term sustainability.

Key Organizations in Healthcare AI Validation and Education

The healthcare AI validation and education landscape is relatively new, with several third-party organizations accrediting systems and certifying professionals. These groups help ensure healthcare AI technologies are safe, effective and ethical.

1. URAC — For Organizational and Product Accreditation

The Utilization Review Accreditation Commission, URAC, is the leading choice for the formal accreditation of AI systems and products for organizations. Established in 1990, URAC is a nationally recognized leader in healthcare accreditation. Its “Artificial Intelligence in Health Care Accreditation” marks a pioneering initiative, being the first of its kind in the nation. URAC focuses on ensuring that the development and use of AI is safe, ethical and transparent while offering a flexible, tailored approach that aligns with your organizational goals.

Key Features

• Offers two distinct pathways — one for healthcare organizations using AI and the other for technology companies that develop it.
• Ensures AI development and usage are safe, ethical and transparent.
• Tailors its approach toward specific goals.

2. Joint Commission — For Hospital System Guidance

The Joint Commission serves as a primary United States hospital and health system accreditor, ensuring that these entities meet rigorous performance standards. While it does not provide stand-alone AI product certification, the Joint Commission plays a pivotal role in guiding its accredited hospitals on how to govern and oversee the safe use of AI technology. The organization’s emphasis on patient safety and quality of care integrates AI oversight into existing hospital accreditation standards, thereby enhancing your overall healthcare delivery.

Key Features

• Focuses on patient safety and quality of care.
• Integrates AI oversight into established hospital accreditation standards.
• Provides a framework for internal governance.

3. AFNOR Certification — For International AI System Certification

AFNOR Certification is an international standards body recognized for certifying AI systems. It was among the first to certify a medical AI product, demonstrating a model for system validation. AFNOR attaches importance to risk management, ethics and performance, and awards the “Certified Trustworthy AI” label to organizations. This certification is particularly valuable if your entity has a global footprint, as it facilitates compliance with international standards.

Key Features

• Issues a certification label through thorough audits of AI systems.
• Prioritizes risk management, ethical considerations and performance measures.
• Caters to global entities with a worldwide footprint.

4. ABAIM — For Professional Certification

The American Board of Artificial Intelligence in Medicine, ABAIM. focuses on certifying individuals rather than products. Its primary role is to upskill healthcare professionals to ensure they are competent and knowledgeable in the use of AI in a clinical context. Through its virtual certification programs, ABAIM validates the proficiency of your clinicians and healthcare staff, helping your organization to build an AI-integrated workforce.

Key Features

• Offers virtual certification programs tailored for clinicians, staff and students.
• Validates individuals’ knowledge in medical AI.
• Facilitates the development of an AI-ready workforce to address modern challenges.

Frequently Asked Questions

Find the answers to common questions about AI accreditation and certification in healthcare.

What is the difference between accreditation and certification?

Accreditation is independent recognition of an organization’s competence to perform specific tasks, such as auditing or testing. Certification confirms that a product, person or management system meets particular standards. While accreditation is a rigorous evaluation of competence, certification simply verifies compliance with established requirements.

Is third-party accreditation for AI required by the FDA?

The FDA does not currently require third-party accreditation for AI or machine learning-enabled medical devices, as it directly regulates them. However, for low- to moderate-risk devices, the FDA’s 510(k) Third Party Review Program allows third parties to review device submissions at the manufacturer’s discretion, without a specific mandate for AI.

How should I choose the right path for my organization?

First, identify your goal. If you want to validate an AI product or improve your organization’s AI governance, an accrediting body like URAC is ideal, as it offers robust standards for quality and compliance. If you aim to educate your staff and strengthen their AI skills, a professional certification program such as ABAIM is more appropriate, offering targeted training and equipping your team with practical AI knowledge for healthcare.

Future-Proofing Your Organization With AI Credentials

In today’s evolving digital healthcare landscape, certification from recognized organizations enhances your credibility and builds trust among patients and stakeholders. Prioritizing healthcare AI certifications helps your organization stay competitive, innovative and prepared for future challenges. Adopting these credentials is a strategic move toward lasting success.

What Is Measurement-Based Care?

Measurement-based care (MBC) is a client-centered approach that uses client-reported data to guide clinical decisions. It is a continuous, collaborative cycle with four key steps:

• Measure: MBC starts with collecting standardized information from your client. At the beginning of care and at regular intervals thereafter, they will complete a questionnaire about their symptoms and overall well-being.
• Share: You will review results with the client using metrics and graphs. You can review the numbers and discuss them with your client.
• Act: If the client is improving, continue the current treatment plan. However, if no improvements were seen, you may need to adjust the treatment.
• Repeat: This cycle is repeated throughout the course of the treatment. Tracking scores over time can help you and your client quickly identify when a change in the treatment plan is needed.

Why a Scalable MBC Strategy Is Crucial for Growth

MBC is a fundamental pillar for sustainable growth. Here are several reasons why your organization needs one.

Transitions Care From Subjective to Data-Driven

As an organization grows, maintaining a consistent quality of care across various providers becomes a challenge. An MBC strategy helps standardize the definition of “success.” It ensures your approach to delivering care is measurable and consistent.

Boosts Client Hope

For many clients, especially in behavioral health, the subjective feeling of progress can be slow to emerge. An MBC strategy helps you offer clients concrete evidence of their progress. As clinical professor Ajeng Puspitasari, PhD, noted, clients often struggle with big feelings and fail to acknowledge their progress.

“But if we use measures that look at more than just symptoms, we can say, ‘OK, maybe your depression is not going down, but look at how far you’ve come in your ability to manage symptoms, to be mindful, to regulate your emotions,’” Puspitasari says. This kind of input can provide clients who feel hopeless with peace of mind.

Creates Significant Operational Efficiencies

Growth often leads to operational complexity and increased administrative cost. A scalable MBC strategy, when powered by an automation platform like Mend, can help streamline workflows. With this tool, you can automate the collection and integration of client-reported data, freeing your staff from time-consuming manual tasks.

What Does a Scalable Measurement-Based Care Strategy Look Like for Growing Organizations?

Discover the main pillars for creating an MBC strategy that will remain successful for years.

1. Standardized Measurement Tools
   Administering validated, evidence-based measures, such as the PHQ-9 for depression, helps standardize your results. It also lets you analyze outcomes at a macro level, compare the effectiveness of different programs and speak a common language when discussing client progress.

2. Automated Data Collection Process
   

   One of the biggest barriers to successful MBC is the administrative burden of manual data collection. A robust platform like Mend is the ultimate solution, which allows you to “automate your check-in workflow, so you can start visits on time with everything you need.”

3. Integrated Data Into the Clinical Workflow
   Collected data must be woven directly into the electronic health record (EHR). As your organization grows, the EHR becomes the main source for client information. Embedding MBC data helps ensure that every provider has access to the client’s history and progress, promoting better turnover and continuity of care.

4. Empowered Team
   Technology is the vehicle, and your clinical team is the driver. As you hire more professionals, make sure to implement a consistent training protocol to ensure care is implemented uniformly. That way, you can focus on what matters — interpreting MBC data and using it to facilitate more meaningful conversations with clients.

Overcoming Common Barriers to Scaling MBC With Mend

Here are the common challenges to scaling MBC and how Mend can solve them.

Technological Hurdles

When data is siloed, your staff cannot easily see a client’s progress over time, compare results or make informed decisions at the point of care. Mend acts as the central hub that connects the clinical record with the client experience. It creates a unified workflow, integrating with major EHR systems and sending the information as discrete data.

Increased Provider and Staff Burden

Without automation, MBC can generate significant administrative work. Staff have to manually send reminders, analyze completed forms and upload them to the EHR. Mend’s AI assistant, Emma, acts as your clinical coordinator and can handle tasks like scheduling, rescheduling and virtual visit technical support.

Low Client Engagement

The success of an MBC strategy depends on a continuous stream of client-reported data. If clients find the process too difficult or confusing, your organization may not reap the benefits. Mend can send clients a direct link to the form via email or SMS, allowing hassle-free access.

Frequently Asked Questions

Learn more about measurement-based care before implementing it into your organization.

Do You Need to Replace the Current EHR to Implement an MBC Strategy?

Not necessarily. Leading software platforms like Mend are designed to integrate with existing EHRs, not replace them. This is helpful for firms that have already invested heavily in their current system.

Can You Pilot an MBC Program in One Department Before a Full-Scale Rollout?

Yes. Many customers find a phased approach effective in managing change and measuring impact before committing to an organization-wide initiative. It also allows your staff to identify challenges and secure stakeholder buy-in, ensuring a more sustainable adoption.

Implement a Successful Measurement-Based Care Program With Mend

Mend can handle the heavy lifting of a scalable MBC strategy for a growing organization, providing automation features and a user-friendly design. Adopting it is a commitment to building a more resilient, accountable and effective workplace that is equipped for the future of health care.

If that warning was heard it certainly wasn’t heeded. Today the evolution of social media and artificial intelligence have drastically exacerbated the accessibility and weaponization of personal information. The killing of UnitedHealthcare CEO Brian Thompson has shaken healthcare organization C-Suites across the country, forcing leaders to ask themselves who knows where they live and where they’ll be at any given time. And the same PII that puts executives in danger fuels the phishing and ransomware attacks that exposed more than 2.3 million healthcare records in data breaches through just the first six months of 2025.

Zayas explains how we got here, and what can be done to safeguard organizations and their personnel.

1. Protecting personal information has been a crusade of yours for a long time.

Information is the currency of the realm, and privacy is like freedom: you either have it or you don’t.

2. Why is it so much worse now than it was 20 years ago?

In addition to the failure to pass the kind of privacy legislation that has made a real difference in Europe, the U.S. has become a place where it is now socially acceptable to attack somebody’s family or somebody’s home because of a grievance against a doctor or a healthcare organization. A person feels empowered to say, “I may not be able to change their policies, but if I kill their executive, somebody will pay attention.” And they have the tools to do that. One person’s anger can now also be shared through social media, something that was not possible 20 years ago. When somebody has a bad outcome – “a doctor botched my surgery” – and they post it online, other people see themselves in that. We saw how, when Brian Thompson was killed, there were many who shared the grievance of the alleged killer. 

3. How cognizant are healthcare organizations of this danger?

It’s getting better but it’s not where it should be. Most organizations, and the people who work there, don’t understand that receiving a threat at home or getting a phishing email that looks real and personalized could be a direct result of the free discount they got from a supermarket loyalty program, because they gave out their address. They don’t see the entity between them and that supermarket, which is a data broker that buys your information and then sells it or trades it very cheaply to other individuals who can weaponize it.

Also, most people feel safe at their workplaces, which are typically fortified with security personnel and cameras and other safeguards. They don’t recognize that someone determined to act on a violent threat is going to come after them at home. They’re going to come for their families.

4. Is data removal even possible, given the volume of information now accessible about all of us on thousands of websites?

If it weren’t possible, data brokers, social media platforms and advertising companies wouldn’t spend hundreds of millions of dollars trying to stop legislation like California has to protect privacy, or like the EU did with GDPR. We know it can be done. Ironwall protects more than 400,000 people and removes 1.5 million pieces of personal information every week. You won’t find our clients’ addresses online.

Unlike the Brian Thompson assassin, most of those who feel aggrieved enough to attack someone will do everything they can to get away with it, and they need information to do that. We shut down that supply. We provide tools that mask cell phone numbers and email addresses, and in our Executive Protection program we provide high-level law enforcement support, especially when an active threat has been reported.

5. How does data removal also lower the risk of a ransomware attack? 

Your employees will not be as careful as your IT department when it comes to online security. About 70% of data breaches over the last three years didn’t come from attacking the servers. They came from hackers going after individuals, compromising their devices and working their way into an organization. Someone gets an email that appears authentic because it appears to have been sent by a friend or relative, and they’re more likely to click on a link in that email, and that’s all it takes to give a hacker the access they need.

Hackers are smart but they’re lazy. If they look at two companies and one has thousands of pieces of information easily accessible, and another doesn’t have enough to leverage, they’ll go with the easier target every time.

6. Is it possible to safeguard a hospital or an organization with hundreds or thousands of workers? To secure all their personal devices? 

We’ve seen how CEOs and CIOs have started to understand that they can’t leave this huge vector open and say there’s nothing I can do about it. You can’t have a safe organization if the people who work for you aren’t safe. We’re never going to tell them and their spouses what they can do on their personal devices. But if we educate them on privacy and provide protection for them in a way that’s easy and will lower the amount of robocalls and scams and phishing emails, they’ll realize it’s not just a benefit to their employer, it can save them from headaches like identify theft.

7. Privacy protection has become a business with several different providers. Do they all provide the same service? 

Not at all. We’ve been doing this for more than a decade. We remove content anywhere it can be located with a search engine – other companies tend to only focus on people finder websites. That’s not enough. 

8. What are three steps healthcare executives and organizations can take right now to reduce the risk of threats that emanate from PII?

First, understand your vulnerability. Don’t put your head in the sand. Do an assessment. Or let us do it for you at no cost. How protected are your key personnel and executives? If somebody can quickly find their personal mobile number online in less than five minutes, they can find where they live. As soon as you start removing information, you become less of a target. We can also do risk assessments. We’ll show you where their information is and how it can be weaponized against them. If you don’t even know the threat level against your executives, you’re running blind.

Second, do not give our information. The preventative tools we provide help with that. You’re not going to stop using the internet, but using a VPN will encrypt your information and make it harder for people to steal it. Using a VoIP number hides your cell number. Using alias emails protects your email address. These tools generate fake data that will eventually start to replace the identifying information that’s out there.

Finally, get protection. You want to make your executives and all your personnel into hardened targets. at work as well as at home. If you have the budget, great. Pay it. If you don’t, make it an employee benefit through the organization. When we’ve offered this as a paycheck deduction, at a significantly reduced rate off retail, we typically see as many as 20% of employees quickly sign up. Providing privacy protection sends a great message to your team and also helps with executive recruitment and personnel retention.

Right from static portals to dynamic experiences

Historically, patient engagement has long been relegated to generic portals as well as static dashboards. These platforms, while being functional, treat the patients like faceless entities – same reminders, same check-in forms, and same educational content. However, patients are not homogeneous – they come with distinct medical histories, social context preferences, and risk profiles. The paradigm of hyper-personalized AI care pathways helps hospitals to craft their own unique engagement journeys. As patients reject the one-size-fits-all mentality. Rather, it embraces the individuality of every patient, creating engagement flows, which go on to adapt in real time as the new data streams in.

These dynamic pathways start with creating a digital twin – a virtual mirror of a clinical and behavioral profile of a patient. Helped by AI as well as machine learning, this twin happens to be the foundation for predicting disease progression, identifying any sort of care gaps if any, and anticipating the intervention requirements. When teamed with contextual AI algorithms that comprehend who the patient is, what time it is, and what the circumstances are, they guide at every touchpoint—be it the educational material, medication reminders, remote check-ins, or even emotional support elements.

The digital twins and contextual AI role

At the heart of this kind of revolution happens to be the convergence of digital twins along with contextual AI. A digital twin happens to be a continuously updated, multidimensional representation when it comes to a health journey of a patient – drawing from past experiences, diagnoses, vital trends, results of images, and lifestyle patterns to even the genomic data. When AI algorithms process this kind of information, they unleash patterns as well as trajectories that would in the past have eluded human analysis, such as the early signs of deterioration or even wellness opportunities that might be there.

Apparently, contextual AI layers in real-world influences. There are certain questions which arise – is the patient a full-time caregiver? Do the patients live in a food desert? What happens to be their emotional well-being score? An algorithm evaluating time-stamped data might go on to notice that due to caregiving duties, evening telehealth sessions happen to perform poorly. Due to this, the system recognizes the care pathway, perhaps offering a check-in in the morning instead. This kind of level of customization would be unmanageable at scale along with human effort alone. However, AI transforms it from a pipe dream into something very realistic.

Through combining these technologies, the concept of hyper-personalized AI Care pathways enabling hospitals to craft unique engagement journeys for patients goes on to become more than just a slogan. It is emerging as a systemic transition in how hospitals go on to deliver, measure, and even refine the care part. The result is a fluid and evolving care journey, which resonates with every patient – especially improving engagement and adherence.

Real-time EHR analytics – the foundation of personalization

Any effective customization system happens to depend on data. Real-time EHR analytics go on to serve as the backbone for these hyper-personalized AI care pathways. Every lab result, vital sign, clinical note, medication, refill, and even consent form happens to be converted into actionable insights. For instance, AI may as well detect that the HbA1c of a patient has plateaued for two readings. The pathways alter accordingly – triggering targeted nutritional counselling, glucose tracking reminders, and also virtual touchpoints so as to reinforce the adherence. This is not hypothetical at all. Early adopters go on to report that integrating EHR intelligence along with AI-powered messaging as well as remote monitoring happens to reduce the admissions by pre-empting the complications and making sure that there is a timely intervention that takes place. The narrative often underscores how such systems happen to identify patients who are veering off the care plans and quickly roll out proactive nudges, thereby reducing the expensive post-acute care scenarios.

Critical to this kind of success is seamless interoperability. The AI platforms must integrate in a very effortless way with EHRs, patient-reported outcomes, wearables, and also social determinants data. The ecosystem itself happens to become the protagonist within the hyper personalisation story – thereby fueling every interaction with context, clinical relevance, as well as continuity.

B2B advantages – hospitals and their ecosystem

The transformation into AI-driven care pathways is not just purely patient centric – it happens to deliver strategic gains for hospital executives, vendor partners, and even payers. For hospitals, the primary payoff happens to be the clinical efficiency. Hyper-personalized AI-care pathways help the clinicians to focus on high-value interventions, while the AI takes care of low-risk monitoring, freeing the staff in order to deal with complex cases. This kind of transition boosts the staff satisfaction and also, at the same time, reduces burnout along with improving throughput.

If we talk from a financial perspective, there is a triple bottom line impact – decreased readmission, shorter hospital stays, and even better chronic disease management – all at lower costs of care. For payers as well as risk-bearing providers, such pathways happen to support value-based contracting as well as shared savings models. Vendors who happen to be specializing in AI platforms, remote monitoring suits, or even contextual messaging engines find a fast-growing market as hospitals look to integrate their capabilities into clinical workflows.

Besides this, hospitals are building long-term engagement by way of establishing loyalty. When patients happen to feel that their care journey is completely customized, their retention, dependence, and advocacy grow. This elevates the brand reputation along with supporting future service line growth like virtual care subscriptions or even post-discharge wellness programs.

Netflix-style recommendation when it comes to care plans

It is well to be noted that one of the most compelling dimensions of this kind of evolution happen to be parallel to modern entertainment recommendation engines.

Imagine opening a hospital portal and witnessing not a static dashboard but a prioritized feed customized to your specific requirements. Just like Netflix, which suggests shows to you based on your past history, in a hyper-personalized care pathway, recommendations might include suggestions for nutritional alterations, the next education module, or even alarmingly early triggers in order to look out for medical attention.

A patient who happens to be recovering from orthopedic surgery might receive messages like, Your activity happens to be below expectations – would you like to start again with a gentle stretching guided exercise now? Or a person having congestive heart failure might be nudged towards a hydration check-in when experiencing a heat wave. These subtle yet timely personalization scenarios happen to infuse complex care routines along with human touch and are driven entirely by AI as well as analytics.

Making sure of ethical use as well as patient trust

Any transition towards hyper-personalization happens to bring critical consideration with regard to consent, privacy, and ethical design. Setups that are executing AI-driven, hyper-personalized care pathways help hospitals to craft their unique engagement journeys for patients and must do so in a very transparent way. Policies should clarify what data has been gathered and how it is being used and who has access to it. Opt-in models, granular consent flows, and even patient-friendly controls are necessary in order to build that level of trust.

Besides this, one should not wander into behavioral manipulation. The idea is to remain in sync with ethical frameworks – supporting the empowerment and not coercing anyone. Regular algorithmic audits are indeed very necessary so as to detect bias, make sure of equity, and also uphold the highest standards of clinical integrity. Patients should also understand that the system is indeed adapting for their own benefit and not creeping into them and that human oversight still happens to remain central to the entire process.

So, what is the road ahead – scaling the hyper-personalization pathways? 

Although the technology happens to be launching quite rapidly, widespread execution needs thoughtful planning. Hospitals have to invest in interoperable platforms, clinical governance models, and dynamic consent frameworks for AI usage. Staff training, not only for technological usage but also to trust as well as collaborate with algorithmic decisions, happens to be very critical.

Collaboration models are indeed emerging as effective thrust-givers. Hospitals happen to be teaming up with AI vendors, payer organizations, and even academic institutions in order to develop pathway frameworks as well as proof-of-concept rollouts. Regulatory bodies are starting to offer guardrails, which include guidance in terms of algorithmic validation as well as real-world evidence requirements. With time, these Pathways will add the self-learning trait. As patients move across the system, feedback loops are going to refine their journey. Success is indeed going to be measured not in terms of premium billing, but in terms of the enhanced health markers, decreased variability, adherence that is higher, and also measurable patient satisfaction improvements that are reinforced due to long-term loyalty.

Electronic Medical Record (EMR) systems can enhance the efficiency and performance of healthcare services by streamlining care workflows, promoting interdisciplinary working, and providing on-demand access to comprehensive patient information across healthcare teams.

EMR systems also provide a platform for more connected and patient-centric models of care across large geographies or populations, even though their impact in supporting this sort of care delivery reform has been limited to date.

Reported benefits of EMR solutions include improved patient safety, clinical outcomes, service efficiency, financial performance, and patient experience. These typically derive from improvements in care documentation, medication management, service insights and compliance, capacity and demand management, and patient communication, as well as reduced incidence of delayed or inappropriate care decisions.

However, achieving these benefits requires high levels of adoption by frontline staff and an ongoing program of value measurement and solution optimisation. Further, to realise value from technological advances like Artificial Intelligence (AI) and new service delivery models, EMR solutions must be architected with evolution and change in mind.

Realising value from EMR systems

While much evidence supports the value of EMR systems, their implementation can be challenging, requiring well-executed change management involving meaningful and continued engagement with time-poor staff.

EMR deployments often focus on organisational value, typically detailed by a business case. Frontline staff often have to extrapolate meaning for their day-to-day working practices, which may or may not happen. Staff can be challenged by change without understanding its value to them as individuals, resulting in poor motivation and change management difficulties.

Articulating value in terms that resonate with frontline staff can help maintain momentum and enthusiasm for change. Established techniques from other industries, such as persona analysis, provide a helpful tool to express role-specific value definitions.

Collecting qualitative and quantitative data is also fundamental in evidencing and communicating the value of a EMR solution. It is good practice to plan and action this activity before going live. This establishes credible baseline data to compare against post go-live data to determine the value delivered and any unanticipated negative value for prioritisation in continuous improvement programs.

An ongoing journey, not an event

The delivery and articulation of value is not an event but an ongoing journey. Post go-live analysis may focus upon a defined set of benefit and outcome measures, possibly described by the original business case. But an ongoing program of solution optimisation and value measurement helps to ensure the solution evolves with service needs, whilst identifying underperforming aspects that require corrective intervention.

An important post go-live dataset, commonly overlooked, relates to solution adoption and activity. This highlights areas of functionality and configuration that perform well or those requiring further interventions like user training or solution configuration changes. Automated adoption dashboards (see Figure 1) also provide dynamic insights.

Figure 1 – Example post go-live adoption dashboard

EMRs must evolve to support new value

All EMR solutions will continue to be shaped by technological innovation and changing healthcare demands. A solution’s long-term success will depend on quickly and cost-effectively supporting new value opportunities presented by advances in technology, science, and service delivery models.

AI and Machine Learning (ML), for example, present realistic and affordable options to enhance the quality and outcomes of care interventions. In fact, it is hard to imagine an enterprise-scale EMR solution without AI capabilities in two years’ time.

Generative AI solutions already help care professionals consolidate, synthesise, and summarise patient record data to improve the efficiency and precision of care planning and interventions. ML examples include predictive analysis, such as identifying patients at a higher risk of readmission, developing conditions, or unexpected deterioration.

Integrating interoperability standards, such as Fast Healthcare Interoperability Resources (FHIR), will also become crucial. These facilitate the sharing of structured, coded, and actionable patient information across health and social care services. This is particularly important in enhancing the efficiency and effectiveness of care transitions and collaborative decision-making across large geographies or populations.

Enterprise EMR solutions will also evolve to provide genomic capabilities relating to test orders and results and, importantly, pharmacogenomic decision support to guide and inform the correct and appropriate use of medications for each individual. This understanding, combined with EMR technology, has the potential to transform care outcomes on a scale similar to the introduction of antibiotics.

Transformation and new models of care

The term ‘digital transformation’ is hard to escape. But what does this mean for healthcare services adopting an EMR solution?

Digital transformation represents an organisational re-wiring to create value for the organisation and its consumers via innovative business models. However, while many initiatives deliver modernised healthcare interventions – such as video consultations, virtual wards, healthcare apps, and AI technologies – these have been primarily used to enhance existing models of care.

Recognition of the need for new models of care that reflect the demands of modern societies is rapidly gaining pace. Current integrated care models rely on the ability of EMR platform technologies to support the seamless flow of information across healthcare services for the planning, management, allocation, and delivery of healthcare interventions (see Figure 2).

Figure 2 – EMR-enabled integrated care model transformation

The transformation of healthcare to deliver genuinely patient-centric and joined-up care workflows will require significant service reforms and a paradigm shift. In particular, it will require moving away from the dominance of acute care. Working in interdisciplinary teams spanning care sectors will also be a significant change and challenge for healthcare professionals.

EMR solutions must provide a critical enabling and supportive role for the unimpeded flow of information across a healthcare region to inform the joined-up planning, management, allocation, and delivery of timely and appropriate care interventions.

Whilst the value currently delivered by EMR solutions is significant and important, the overwhelming value for a modern-day EMR system is in supporting the new and emerging models of care we so desperately need.

About the author

Darren Jones is Country Manager, Australia and New Zealand at InterSystems, a creative data technology provider which delivers a unified foundation for next-generation applications for healthcare, finance, manufacturing and supply chain customers in more than 80 countries, and electronic medical record systems which support advanced data management in hospitals. Based in Melbourne, Jones leads the company’s operations in Australia and New Zealand and supports the success of InterSystems customers.

About six in 10 Americans have a chronic disease, with four in 10 reporting two or more conditions. CDM is “an integrated care approach to managing illness, which includes screenings, check-ups, monitoring and coordinating treatment, and patient education.” This methodology is often personalized based on each patient’s needs and health conditions. To accommodate healthcare professionals’ ever-expanding knowledge of these diseases and the discoveries being made about them, continuous developments in innovative healthcare technologies ensure enhanced care for a broad spectrum of patients worldwide. To fully understand the significance of these technologies, it’s crucial to first analyze key developments and their impact on CDM.

The impact of tech developments on CDM

In recent years, there has been a significant rise in the development and application of digital health technologies. These innovative tools provide patients with continuous monitoring and real-time feedback, empowering patients to make more informed decisions about their health. The days of patients relying solely on in-person doctor visits for their health updates—often missing sudden or surprising developments—are waning. For patients to remain engaged with their health, they need ongoing information, even after leaving the doctor’s office. Regular updates and real-time data play a key role in effective CDM.

According to Health Information National Trends Survey findings, nearly one in three Americans use a wearable device to track their health and fitness. While some wearable wrist devices, such as Fitbit or Garmin, started as simple tools for tracking steps or calories burned, they quickly evolved into ways to receive updates on vital sign abnormalities or severe health risks. In certain instances, AI innovations can surpass human accuracy. For example, some conditions, such as organ cancer and colon polyps, are more easily and accurately detected through AI tools and implementations. In CDM, AI advancements can help patients manage conditions like asthma, diabetes, or high blood pressure by connecting them with relevant screening and therapy, and remind them to take steps in their care, such as taking medication. AI algorithms capture real-time data streams, providing users and healthcare professionals with personalized health updates and suggestions tailored to the patient’s needs and circumstances.

Patients dealing with conditions such as hypertension or heart disease don’t need to rely on quarterly checkups to stay in the loop. With wearable devices that track and display how their body acts and reacts throughout the day, users receive constant data to keep them well-informed. These innovations democratize healthcare by empowering patients with the knowledge necessary for active monitoring and management rather than relying solely on doctors. This can eliminate human error with data-proven results.

Leveraging digital solutions for proficient healthcare management

When leveraging these technologies to their full potential, it is essential that they possess the functionalities and capabilities necessary for personalized alerts and updates. Digital biomarkers, medication sensors, and AI/machine learning are empowering patients by providing them with real-time updates and notifications. In addition to supporting their personal healthcare, these tools communicate important information simultaneously to the patient’s specialist or provider, ensuring both parties are properly informed of any incoming changes and alerts. While the biomarkers detect changes in body temperature, eyes, odor, heart rate, respiratory rate, movement and balance, and other factors, medication sensors notify patients about missed doses and when the next dosage is necessary. From there, AI/machine learning analyzes the data and more traditional medical data sources to remotely communicate vital information to the proper healthcare specialists through cloud-based solutions and established algorithms.

It’s vital for personal tech to reflect users’ unique health journeys when managing their health conditions. By aligning remote medical technologies with individualized patient needs, healthcare professionals can foster stronger relationships through various methods such as shared health monitoring and telemedicine. When a patient’s doctor or healthcare professional has access to the patient’s real-time health data and updates, they can consult and discuss the next steps in real time, eliminating the need to schedule appointments and reducing hospital readmissions. Doctors can communicate with patients virtually with a shared view of the latest updates and discoveries. This is especially crucial for health issues related to vital signs such as blood pressure, heart rate, or oxygen levels. Real-time data and predictive analytics provide professionals with a more comprehensive understanding of how to best approach patient care.

Integrating remote medical care solutions and challenges

A shared view of algorithmic and analytical health data was not available to patients even a few years ago. It is critical for professionals to consider the ethical implications when integrating remote solutions into a patient’s health monitoring and management process, especially regarding patient confidentiality and the risk of exposing sensitive health data. Despite the need for ethical consideration regarding remote patient care, the benefits cannot be overlooked.

Two significant ways integrating these digital developments enhance patient care for the patient and the professional are virtual therapy and remote surgery. Long-distance travel no longer prevents patients from receiving the top-notch care they require. Teletherapy platforms create simplified access to mental health services remotely, reducing barriers to care and destigmatizing mental health support. This approach opens the door to receiving therapeutic care at patients’ fingertips without leaving the comfort of their homes. Teletherapy offers patients a wider range of therapists remotely.

Meanwhile, remote surgery utilizes robotic technology and wireless networking to connect patients and surgeons who are geographically distant. Providing technical accuracy while enhancing patient safety, surgeons can now perform procedures through wireless networking and robotic control of surgical tools and resources. This creates convenience and saves time for the patient and the surgeon. It also dramatically reduces the financial expenses of long-distance travel.

Completely replacing in-person visits and communications with digital care solutions is not recommended, despite their benefits. The challenges of a constant flow of health data and analytics include data privacy, algorithmic bias, and the need for extensive validation before widely implementing AI tools. This visibility also creates new ethical considerations for healthcare professionals to preserve patient privacy and security. This requires changes in policies and standards in the healthcare industry. Limitations need to be placed on certain solutions to provide safety, security, and accuracy for finding the proper treatment, medications, and solutions. While CDM is greatly simplified through digital health solutions, it’s imperative for facilities to balance this with human interactions and in-person appointments to ensure patients receive the best care possible.

The current impact of innovative healthcare technologies

The integration of AI, machine learning, and digital innovations is reshaping patient care and healthcare delivery. CDM is more trackable and accurate than before, and digital solutions offer patients better outcomes while reducing costs and improving accessibility. Actionable insights and health updates are more visible and shareable, creating a heightened synergy within the healthcare industry.

The current professional landscape demonstrates the exponential growth of AI and data technology. While healthcare professionals enjoy new and different ways to connect with their patients, technology rapidly creates a superior patient experience. Technology saves lives, and with great minds coming together to create and innovate, those in need may experience an enhanced quality of life.

About the Author:

Dr. Shanthan Reddy Ramidi is a board-certified hospitalist with extensive experience in the comprehensive care of hospitalized patients. He is a subject matter expert on evidence-based medicine, patient-centered care, care transitions, quality improvement, multidisciplinary collaboration, and medical education. Dr. Ramidi earned his medical degree from Kakatiya Medical College in India and completed residency in Internal Medicine at The Brooklyn Hospital Center. Connect with Dr. Ramidi on LinkedIn.

In 2023, more than 40% of hospitals operated at a loss due to increased labor costs from the pandemic. However, travel nurses aren’t the only solution for addressing staffing gaps. The per diem model, which enables hospitals to staff based on need, offers a more tangible alternative. The model also gives nurses the freedom to choose their schedules and locations—similar to the benefits they appreciate with travel nursing.

With per diem nursing, healthcare systems can address immediate staffing shortages while tapping into significant cost savings. It supports a more agile workforce and re-engages nurses who are looking for greater control over their careers. Given the unsustainable costs associated with travel nurses, it’s time that healthcare systems rethink their staffing strategies to create a more resilient workforce.

The Current Landscape of Healthcare Staffing

The nursing shortage has roots stretching back before the pandemic. Historically, high turnover and burnout rates have plagued the profession, but COVID-19 exacerbated these issues, pushing an unprecedented number of nurses to the brink. A 2022 survey revealed that over 30% of nurses were likely to leave their roles, primarily citing unmanageable workloads and insufficient work-life balance.

As demand for registered nurses rises—projected to grow by 6% over the next decade, outpacing the national job growth average—the strain on the workforce will only intensify, especially as thousands of nurses retire, switch careers, or leave due to burnout. Although travel nurses offered short-term relief, the solution has proven costly: revenues for the largest travel nursing firms exploded from $13 billion in 2019 to $62 billion in 2023.

Even after a correction that saw their revenues dip to $47 billion, travel nurse rates remain nearly twice as high as those for full-time staff. On average, hospitals pay $87 per hour for travel nurses, compared to per diem rates between $66 and $74 per hour. This disparity drives many systems to implement cost-cutting measures such as delaying technology investments or reducing services—strategies that often compromise care quality rather than deliver lasting relief.

The Financial Case for Per Diem Nursing

Alongside higher bill rates, additional costs associated with travel nurses—such as travel reimbursements, lodging, and agency fees—can quickly add up, especially when multiple positions need filling. A report from the American Hospital Association highlights just how costly this reliance can be, revealing that nursing hours covered by contract or travel staff soared from 3.9% in 2019 to 23.4% in 2022.

As the hours worked by contract travel nurses increased, so did the associated costs. In 2019, hospitals allocated just 4.7% of their total nurse labor expenses to contract travel nurses. By January 2022, that figure skyrocketed to 38.6%. While travel nurses worked 23.4% of all nursing hours in hospitals, they incurred a much larger portion of the overall nursing labor costs— nearly 40%.

Per diem staffing offers hospitals access to a larger pool of skilled nurses while keeping costs in check—an advantage over both full-time and travel nurse arrangements. Shifting to a per diem model could deliver billions in annual savings across U.S. health systems. For a typical health system heavily reliant on travel nurses, transitioning part of its workforce to per diem staffing could significantly cut labor costs without compromising care standards.

Re-engaging Nurses through Flexibility 

Over the past decade, burnout, inflexible schedules, and a lack of support have driven nurses to leave the profession, and this reality shows no signs of slowing. Addressing this exodus requires more than incremental changes; it calls for staffing solutions that fundamentally reshape nurses’ experience on the job.

Unlike traditional full-time roles with rigid scheduling or travel assignments that require extended periods away from home, per diem nursing allows healthcare professionals to choose shifts that fit their personal and family needs. The preference for control over work hours has become a core value for today’s nurses. According to ShiftMed data, 93% of surveyed nurses prioritize having control over their schedules, and 41% want more paid time off.

However, some stigma still surrounds per diem staff. Historically, healthcare leaders have held misconceptions that those who rely on per diem staffing lack commitment or stability. In reality, this model offers a valuable and flexible alternative that aligns with the evolving needs of today’s healthcare workforce, and more systems are beginning to recognize this. In fact, research has shown that facilities with a higher proportion of per diem nurses tend to outperform on quality metrics than others.

This flexibility matters not just to current nurses but also to those who have returned or are looking to return to the profession. Recent research found that it’s not enough to consider only professional factors to retain these returning nurses; personal factors must also be taken into account. According to the study, addressing work-life balance, providing recognition, and encouraging professional growth are critical for maintaining their motivation and satisfaction.

Broader Implications for Healthcare Systems

As major health networks expand and navigate fluctuating patient volumes, the per diem model can adapt alongside these changes—offering a flexible and reliable workforce capable of meeting varying demands. Over time, this model can help health systems stabilize staffing costs, reduce burnout-related turnover, and enhance job satisfaction among nursing staff. For example, large health systems in metropolitan areas have successfully implemented per diem programs to manage staffing peaks and maintain optimal care delivery.

While fixed schedules and long-term roles have traditionally defined healthcare staffing, the industry’s and its workforce’s needs have evolved. For healthcare executives, this shift presents a unique opportunity to modernize staffing strategies and focus on solutions that balance patient care standards with workforce satisfaction. Turning to the per diem model enables health systems to drive an industry-wide move toward sustainable staffing practices that prioritize resilience and cost-efficiency.

AI Technology has become one of the game-changing technologies in different human activities, but it is the most transformative in the domain of healthcare. Combining the power of advanced algorithms, machine learning, and data analytics, AI in Healthcare Industry is reshaping how healthcare providers deliver patient care, conduct diagnoses, improve efficiencies, and create new treatments. The global healthcare AI market is expected to be worth $188 billion by 2030, and that’s why AI has become a critical component of modern healthcare in this evolving world. This revolutionary technology closes gaps, eliminates inefficiencies and opens up new opportunities for patient care.

How AI is Transforming Healthcare Delivery

The greatest advantage that AI in healthcare industry has is its capability to process immense amounts of medical data quickly and accurately. Analyzing patient records, diagnostic scans, and medical histories using traditional manual approaches is time-consuming and prone to human error. Enter AI, which can comb through terabytes of data in seconds, recognizing patterns and insights that might evade all but the most experienced clinicians or health care providers.

Consider, for example, AI-enhanced diagnostic tools. Similarly, AI-enabled algorithms in settings such as radiology and oncology have achieved tremendous successes identifying diseases, including cancer at earlier, more treatable stages. Deep-learning models, which can interpret imaging data with extraordinary accuracy, are now being deployed to find anomalies in X-rays, MRIs, and CT scans. In a comparative study, more than 95% accuracy rates were noted with AI tools, significantly better than traditional diagnostic methods. Such high levels of sophistication have compelled hospitals to implement AI-powered diagnostic tools with great frequency, thus shrinking error margins in life-or-death scenarios.

AI and Personalized Medicine

AI in healthcare industry brings personalized medicine, a concept once thought of as science fiction, into the realm of the possible. The technology allows healthcare providers to create personalized treatment plans that take into account factors like a person’s genetic profile, medical history and real-time physiological data. The technology uses machine learning to comb through massive datasets of genomics data to predict how an individual patient might respond to certain treatments or forms of medication, thereby reducing the trial-and-error process of prescribing.

Another use case included in the report states that AI platforms such as IBM Watson for Oncology recommend treatment protocols based on evidence-based medicine by using patient-specific data. Just as AI in healthcare industry is revolutionizing pharmacogenomics – the study of how particular genetic variations affect responses to drugs. Thus, precision medicine is going from the edges to the mainstream of health care, providing therapies that are incredibly effective and also least invasive.

Streamlining Hospital Operations

AI aspires to improve the operational efficiency of healthcare facilities significantly. AI is being introduced in hospitals across the world to streamline workflows, improve resource management, and increase patient satisfaction. Exiting AI Tools Predict Hospital Admission Rates, Helping Administrators Allocate Beds, Staff and Equipment More Efficiently An example of this would be the Mater Hospital in Dublin, which has integrated AI-based models that enhance patient flow and reduce wait times during peak hours.

Moreover, AI-based chatbots and virtual assistants are increasingly crucial in automating administrative responsibilities. From scheduling patient appointments to verifying insurance claims, these tools automate repetitive tasks, enabling healthcare providers to focus on on direct patient engagement. Hospitals that have adopted AI Especially in Operational Tools, have reported 30% decrease in administrative workload leading to better service delivery and customer satisfaction.

Revolutionizing Drug Discovery

One of the most exciting applications of AI in healthcare industry may come in the form of drug discovery. It used to take a decade and billions of dollars to bring a drug to market. But AI is significantly shortening this lengthy timeline. AI systems can analyze molecular structures and biological pathways to pinpoint viable drug candidates with unprecedented speed and cost efficiency.

A prime example of an AI to drug candidate applied even to a world where drugs already exist is Atomwise, an AI driven system that upon analyzing existing drug databases of known drugs, found two drugs that might be effective against Ebola virus. It would have taken years of research to accomplish what AI did in one single day. Likewise, AI is allowing for the implementation of virtual clinical trials and predictive modeling, allowing pharmaceutical companies to test how drugs will interact with human biology — before they are used on real patients.

Telemedicine and Virtual Health

The COVID-19 pandemic accelerated the adoption of telemedicine, a trend exacerbated by developments in AI. AI-driven virtual health assistants have emerged as a vital component of remote patient care, providing services like symptom checking, medication reminders, and health monitoring. A fledgling in this field, Babylon Health, is among the platforms that use AI  in healthcare industry to provide patients with credible medical advice instantly based on what the symptom is and their medical history.

In addition, AI in healthcare industry is also improving video consultations by allowing for on-the-spot transcription and summarization of doctor-patient interactions. Innovative use of natural language processing enables AI to automatically convert speech data into structured documentation, saving time for healthcare professionals. As we head into a future where virtual healthcare is on the rise, AKAI will remain a constant force in maintaining accessibility and efficiency.

Challenges and Ethical Considerations

While AI in healthcare industry holds immense potential, the integration of AI health emerges with its own set of challenges. Among the many pressing concerns are data privacy and security issues. Not only does this mean AI systems need tons of patient data to function, but the fact is these systems are vulnerable to both theft and abuse. Regulations such as the GDPR in Europe and HIPAA in the United States must be strictly followed to protect sensitive information.

Moreover, another issue is the ethical question behind AI-based decision making. And while AI may improve diagnostic precision, it cannot replace the human interaction that is critical to addressing patients’ emotional and psychological needs. Additionally, the opacity of AI algorithms can complicate accountability when something goes wrong or leads to negative results.

In response to these concerns, there is an ongoing global dialogue to create ethical frameworks for integrating AI into healthcare. Governments, healthcare providers, and technology companies are working together to establish transparency and accountability in the way AI is applied to medical practice. The FDA’s Digital Health Innovation Action Plan is one example of an initiative to regulate how AI technologies can be deployed safely and effectively in clinical settings.

Future Prospects of AI in Healthcare

There are endless possibilities how ai can reform the healthcare. New technologies, such as generative AI and natural language processing (NLP) are poised to further improve diagnostic capabilities and patient interactions. By using AI-powered wearables and IoT devices these will enable preventive care by collecting real-time indicators of vital signs and informing users of potential problems.

Furthermore, the fusion of AI and robotics is opening new doors for minimally invasive surgical techniques. AI-assisted robotic surgical systems are allowing surgeons to carry out procedures with unprecedented precision, faster recovery times, and better patient outcomes.

With its progression, AI has the ability to revolutionise health care. The tech is set to revolutionise healthcare — from improving patients experiences to speeding up medical discoveries.

Conclusion

AI in healthcare industry has a huge impact. AI is changing the delivery of medical services, the management of diseases, and how patients receive care with the use of data, algorithms, and automation. There is a disparity between AI’s benefits and drawbacks although several challenges involving ethics, privacy and industry application remain. As the technology keeps advancing, it will definitely be a bigger and bigger part of enhancing health care outcomes — making the industry more efficient, accessible and patient-centered than ever before.

Altium is a cloud-based PCB design tool that allows you to work from any device with an internet connection, take your work with you, and collaborate with your team in real time. With real-time data, advanced analytics and artificial intelligence as their underpinnings, these virtual counterparts of physical objects, systems or processes aim to revolutionize patient care and operational efficiency throughout the healthcare system. Also, as digital ecosystems are becoming more developed and the Internet of Things (IoT) is increasingly integrating, digital twins empower precision medicine, making clinical workflows more efficient and healthcare environments optimized.

Digital Twins in Healthcare: What Are They?

Digital twins in healthcare are real-time virtual copies of physical assets, processes or systems, in this case, human organs, healthcare facilities, or even individual patients. These digital twins can connect different data sources ranging from electronic health record (EHR), imaging, and wearable data to genomics, thus enabling comprehensive analytics and predictive assessments.

The power of digital twins was demonstrated, for instance, with the FDA-approved virtual heart model developed by Johns Hopkins University. This model enables cardiologist to simulate and predict cardiac behaviors with high precision which heralds the advent of tailored treatments According to a recent study, the global digital twin in healthcare market size was valued at approximately $3.55 billion in 2023 and is expected to grow at a compound annual growth rate (CAGR) of 60% from 2023 to 2030, expanding to $21.1 billion by 2030.

Applications of Digital Twins in Healthcare

Digital twins in healthcare has tangible benefits seen in diagnostics, treatment, and operational (turning robots on/off) make digital twins in healthcare a significant application area of the digital twin concept.

Personalized Patient Care

One of the most exciting uses is the creation of digital twins of individual patients. And these models account for real-time biometric, genetic and physiological data to simulate treatments, predict responses to therapies and to plan surgical procedures. This makes precision medicine-based methods highly effective not only in improving patient outcomes but also in minimizing any unsafety factors. Researchers at Stanford University, for example, used digital twins to study calcium buildups in coronary arteries and use that research to guide algorithms to develop better cardiovascular risk predictions.

Surgery Planning and Training

How digital twins would change the field of surgery is by allowing surgeons to practice and perfect complicated surgical procedures in a digital environment before ever attempting them on an actual patient. This groundbreaking advancement reduces the risks associated with surgery while also improving accuracy and recovery time. For example, digital twin heart simulations have increased the success rates for ablation procedures for atrial fibrillation by limiting complications.

Growth of Drugs and Clinical Trials

Pharmaceutical research, in particular, benefits from digital twins that speed up drug development by simulating the interaction of the drug with a biological system of interest. This accelerates clinical trials and reduces the time and cost of developing new drugs. A prime example is Sanofi’s use of digital twins to enhance testing by simulating the responses of drug candidates during the preclinical stage.

Health care facility Management and operational effectiveness

Digital twins improve hospital as well as patient processes Virtual models of healthcare facilities replicate with patient flow, resource allocation, and personnel placement, enabling administrators to identify shortcomings and optimize performance. For example, Dublin’s Mater Hospital utilized a data-driven digital twin model to alleviate bed shortages and shorten wait times during peak patient admission hours.

Some technologies making digital twins possible

Various technological advancements drive the growth and adoption of digital twins in the healthcare world. These systems rely on high-performance computing, artificial intelligence, and IoT to integrate and analyze data as it is created. Nonetheless, machine learning algorithms used to improve prediction are provided through accurate imaging techniques, poking huge 3D scans or MRI of systems.

Additionally, digital twin models have been identified as transformative in terms of improving personalized medicine by integrating genomic and proteomic datatypes. Using these massively parallel datasets, researchers can model disease progression, forecast therapeutic responses, and personalize treatment protocols for specific patients.

Advantages of Digital Twins in Healthcare

Digital twins come with many benefits that impact multiple segments of the healthcare ecosystem.

Improved Patient Outcomes

Digital twins create a simulated model of a patient that reflects his or her individual conditions and responses, allowing for personalized treatments and a move away from the trial-and-error practice that can dominate medicine. This enhances success rates and reduces adverse effects.

Improved Operational Efficiency

Immediate supervision and expected improvements allow clinicians to optimize workflows, assets deployment, and machinery preservation. And all this translates into savings and better service delivery.

Proactive Disease Prevention

By leveraging both historical and real-time data, digital twins can also help identify potential health risks at an early stage. This gives clinicians the opportunity to enlist preventative measures and address complications before they worsen.

Support for Medical Training

As medical professionals can practice procedures, trial and error, and gain visual insight into complex medical conditions in virtual environments, they can sharpen their skills and build their confidence.

Challenges and Future Directions

While the potential of digital twins is transformative, they struggle to achieve broad adoption. Benefits of EHRs Most healthcare industry experts agree that the benefits of EHRs far outweigh the drawbacks. It should be noted that the high cost of implementing and maintaining modern digital twin technologies is a hurdle as well, especially for less wealthy service providers.

Another hurdle is interoperability. Digital twins must integrate smoothly within current healthcare systems, which tend to run across multiple platforms. To overcome these challenges, there will be a need for collaboration between technology providers, healthcare institutions, and regulatory bodies alike.

Digital twins show great promise in healthcare and there is certainly more to come in the future. Researchers foresee integrating digital twins with novel technologies such as blockchain to secure data and augmented reality (AR) to provide immersive experiences to patients. With development, these systems will have the possibility to change the very way in which healthcare is delivered, making it more efficient, personalized, and accessible.

Conclusion

Healthcare digital twins break new ground in the way medical care is envisioned and provided. These virtual models also facilitate personalized patient care, improved operational efficiency, and accelerated innovation in medical research, all made possible through real-time data and advanced analytics utilization. Though there are obstacles ahead, the acceleration in developing technology and increasing funding in this space is a positive omen for things to come. Digital twins are revolutionizing not just patient care — but also the future of healthcare.

Medical devices, such as pacemakers, insulin pumps, cochlear implants, neurostimulators and more have improved patient health outcomes worldwide. However, their reliance on software and connectivity opens them up to unique cybersecurity risks. For hospital executives and clinic managers, understanding these vulnerabilities and adopting security best practices is critical to safeguarding patient safety, data and medical operations.

Implanted Medical Device Cybersecurity Risks

Cyberattacks on implanted devices can have grave repercussions, including exposing sensitive health information or direct harm to patients, such as by compromising insulin dosages or pacemaker settings to cause severe medical reactions.

Implanted medical devices come with default passwords set by manufacturers, which are rarely changed before use. This creates an easy entry point for cybercriminals, who can find these passwords in public databases. Updating device firmware is also key to maintaining security, but regulatory hurdles can cause delays in patch deployment. Regulatory bodies like the FDA and similar organizations worldwide often require lengthy patch approval processes. This gap can leave patients exposed to known cyber threats.

These medical devices can also connect to hospital and healthcare networks, potentially allowing direct access or lateral movement within databases and web servers and exposing valuable patient, healthcare, and/or financial data. Misconfigured network settings can also create vulnerabilities, offering attackers a way in. It is essential for healthcare leaders to understand and mitigate these risks.

Regulatory Approaches and Challenges

Healthcare systems also face several global regulatory challenges for medical device cybersecurity. By staying informed of regulatory frameworks and aligning with security strategies for each operating region, organizations can maintain compliance while improving safety and strengthening defenses.

Here is an overview of the primary regulatory approaches and some of their challenges:

• United States: The Food and Drug Administration’s (FDA) approach mandates that cybersecurity is built into devices from design through ongoing updates. This ensures that manufacturers consider security early on, but the FDA’s patch approval process can bottleneck cybersecurity improvements and deployment. Manufacturers must still go through their lengthy approval process for patch updates, even when the vulnerability poses an immediate security risk. To mitigate this issue, it is essential that the FDA adopts a more agile regulatory framework that balances patient safety with the need for timely cybersecurity updates.
• European Union: The EU’s Medical Device Regulation also requires manufacturers to address cybersecurity risks through stringent risk assessments and ongoing monitoring. While comprehensive, the MDR framework also slows the deployment of security updates. This has led some to call for a more streamlined patch approval process.
• APAC Region (Japan, China, and Others): Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) and China’s National Medical Products Administration (NMPA) both emphasize cybersecurity risk management. Japan requires detailed security documentation and flexibility for emerging threats, while China’s standards focus on encryption and data protection. APAC regulatory bodies emphasize alignment with global best practices, prioritizing patient safety and data security.

Key Strategies for Healthcare Executives to Mitigate Risks

To effectively manage the cybersecurity risks associated with implanted medical devices, healthcare executives should consider adopting the following strategies:

1. Mandatory Security by Design: Device manufacturers should be required to implement cybersecurity features during the design phase, including using encrypted communications, multi-factor authentication, and built-in update mechanisms that allow for timely patching.
2. Change Default Settings: Hospitals and clinics must enforce policies that require passwords and configuration settings to be changed as soon as devices are deployed. This step alone could eliminate a significant number of vulnerabilities.
3. Strengthening FDA and Global Regulatory Oversight: The FDA and other global bodies should develop more streamlined processes for security patches, allowing for expediting the review of updates that address critical vulnerabilities.
4. Ongoing Training and Awareness: Healthcare providers also need to invest in cybersecurity training programs to ensure that staff are aware of the risks associated with medical devices. This includes developing protocols for responding to cyber incidents involving medical devices.
5. Collaboration Between Government and Industry: Finally, global government agencies and the private sector should collaborate more closely to share threat intelligence and best practices for securing medical devices. By having the latest information on medical device threats and potential attack techniques, healthcare organizations can improve their security.

A Global Call to Action for Healthcare Executives

As healthcare organizations remain a top cyberattack target, it’s clear that no corner of the sector is safe—not even implanted medical devices. The risks go beyond data breaches; cyberattacks on these devices can directly threaten patient safety.

For healthcare leaders, staying ahead of these risks means adopting strong, proactive cybersecurity practices. This starts with ensuring devices have secure configurations and up-to-date firmware, despite the regulatory hurdles that can slow patch deployment. Understanding global regulatory frameworks is also crucial for keeping security aligned with requirements. By pushing for security-by-design, advocating for faster patch approvals, and fostering public and private sector collaboration, healthcare executives can protect their patients and their systems from escalating threats.