HPG has joined the Oracle PartnerNetwork to expand its healthcare technology capabilities, offering hospitals comprehensive sales, implementation, and certified support for Oracle Health and cloud-based solutions.
In 2026, the question is no longer, “Does your EHR have AI?” but rather, “How deeply is that AI integrated into the clinical and financial core?” Increasingly, healthcare CIOs are shifting from pilot projects to enterprise-wide scaling–and in many cases, it’s difficult to get away from it. As a vendor-agnostic partner, HPG analyzes how the two giants are executing their 2026 visions to help you align your healthcare IT roadmap.
In order to choose the right EHR for your needs, it’s important to take a look at how your options are changing and what solutions are available.
Oracle leverages the Oracle Cloud Infrastructure (OCI) to move toward an “AI-native” EHR rather than a legacy system with add-ons. In many cases, this means a smoother integration and better overall functionality. For 2026, the biggest upgrade and capability is the Clinical Digital Assistant. This new tool uses Voice-First workflows and agentic AI. For users, that means a shift from passive scribing to active task execution, including drafting orders and referrals and clinical trial matching.
This system offers a number of advantages.
On the other hand, it does come with potential disadvantages for some users.
Epic uses a different approach. This platform utilizes deeply embedded LLMs via the Microsoft/Azure partnership. It focuses on three AI personas: “Art” (the Clinician), “Penny” (the Revenue Cycle), and “Emmie” (the Patient) Its key focus for 2026 is in-basket automation and note summarization: a focus on reducing “pajama time” by having the AI draft complex responses and synthesize longitudinal records into specialty-specific “snapshots.” This provides higher-level data focused on individual specialties, which means more actionable insights for your unique focus areas.
Epic offers a number of potential advantages that medical providers should consider when selecting their EHR system.
Before you choose your EHR, make sure you’re prepared for Epic’s potential downsides.
Regardless of whether you are an “Epic shop” or “Oracle house,” there are several healthcare CIO AI priorities that you may need to keep front of mind.
You want a functional system that allows you to make use of the data you have collected, not just continue to collect it. Shift from a FHIR exchange to truly usable data that feeds AI models without manual “cleaning.”
Move beyond simple “scribes” to AI that can actually queue orders and perform administrative tasks safely and efficiently. This strategy helps actually save employees time and energy, rather than simply collecting data. It’s not just about what the AI can record for you; it’s about what tasks it can take off your plate.
Ensure that every AI output, including both clinical and financial tasks, has a clear, audited path for human sign-off to maintain compliance. The human element is still key in ensuring trustworthy data and effective functionality.
The 2026 roadmaps show that while the tech is converging, the execution differs. The best AI strategy isn’t just about the vendor—it’s about your organization’s internal readiness to govern and deploy these tools. HPG is here to bridge that gap. Navigating these complex roadmaps shouldn’t be a solo journey. Schedule a Strategic AI Roadmap Session with an HPG advisor to discuss your organization’s unique 2026 priorities and ensure your platform is working for you, not the other way around.
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As a healthcare provider, you know that your practice revolves around the Health Insurance Portability and Accountability Act of 1996 (HIPAA). Over the years, you’ve adapted to the requirements of HIPAA and added layers of security to protect your patients. However, AI is changing the landscape, and you want to ensure that you continue to have extensive security to protect your patients and maintain compliance with HIPAA.
With AI, new threats and vulnerabilities are being revealed, and new solutions must be found. In many cases, there are issues that the current firewalls can’t catch. Some of these new risks include:
You can use AI for healthcare governance and remain compliant with HIPAA regulations. To start, you need to create a strategic framework. This needs to include:
You need someone or an established group of people who oversee AI usage in your healthcare facility and ensure it’s being used appropriately. You also need to know that it isn’t sharing patient information. It should be a cross-functional oversight committee. AI is used in many areas of your business, and you want each department to have a hand in oversight, including clinical, legal, and IT stakeholders, to evaluate the “why” and “how” of every AI implementation.
Create and maintain rigorous standards for where your data comes from. You want de-identification to happen before it becomes a part of the training area for AI. Knowing the origin of the data ensures that incorrect information doesn’t become part of the learning by your AI.
People are moving away from “Black Box” models, where you have transparency for information being used and a conclusion reached, but no idea of the logic used to reach the conclusion. More and more clinical settings want to know how and why the AI has reached a particular conclusion to make sure it’s safe, and the system is accountable for any issues.
You have to have third-party vendors to get supplies and services that your healthcare facility needs to thrive. However, they can introduce AI modeling issues into your own framework. You need miminize these risks with a Business Associate Agreements (BAA) that specifically address AI data usage and model training rights. You want these agreements in place before you start doing business together.
With AI and HIPAA, we need a new model of business as usual. It needs to be better than it has been in the past. While the tech is new, HIPAA still applies, and you’re responsible for that security. Public vs. private LLMs bring two major challenges. These are data leakages and inputting sensitive data, such as pasting a person’s name, age, and other information into a chat.
When you’re ready to start working on AI projects, an AI framework allows you to take that step into the future. With security, your adoption of AI is sure to follow. You walk a fine line between “Cutting Edge” and “Compliant.” HPG stands ready to guide you as you bridge this gap. Ready to build your strategy? See how this applies to your core platforms: Contact us now.
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When you’re in a leadership position in the healthcare industry, you always want to make the right moves. From scheduling enough workers to ensuring that you have all the needed supplies on hand, you feel like you need to be able to see the future to prepare for it. In some ways, this is the case. When you leverage the skills of artificial intelligence (AI) for predictive analytics, you can make better choices in a range of areas for your healthcare practice, making you more effective for patients and staff while maximizing profits.
AI is better able to see patterns in the data than a person or a more simplistic algorithm. It can look at historical data, identity patterns, and determine how these patterns will continue into the future. It might be the number of patients or types of cases you might see.
Patient Risk Stratification AI focuses on a patient in a new way. It looks at the patient’s past and current medical conditions to determine the best course of action with the given information and possible outcomes. AI is able to process more information and possibilities than a doctor can on their own. Check out these case studies:
Sepsis is life-threatening, and early detection can make a real difference in the overall outcome. AI can monitor real-time EHR data. This includes vitals, lab results, and nursing notes. AI can use this information to intervene hours before the doctor starts to see the clinic deterioration.
You want to keep your readmission rates low, and AI can help. It looks at models of social determinants of health (SDoH) and post-discharge support and analyzes them to see a pattern of readmissions. The model can help patients who might struggle receive more support after leaving your facility and avoid readmissions. This can minimize CMS penalties.
AI can also be used for operational predictive analytics to see how the past has affected the current care and predict where your healthcare organization might be in a year or two. Review these cases to learn more:
AI can take advantage of your hospital’s operational analytics to predict ED surges. This can be based on seasonality, local health trends, and historical volume. You can use these forecasts for dynamic staffing models that reduce burnout among employees and minimize wait times for patients.
You want the insurance companies to pay the claim the first time your facility submits the paperwork. AI can predict claim denials by identifying coding anomalies. When you use this to your advantage, you get a “clean claim” rate and liquid cash flow.
If there are problems with the data you use, there will be issues with the information you get from it. This is the theory of “Garbage In, Garbage Out.” Your AI model is only as accurate as the information you feed it. You should start by breaking down data silos. Data integrity ensures the accuracy and a lack of bias in all of your information. You can’t have a black box approach in a regulated environment. A black box approach ensures that you know the AI input and output, but you don’t have access to the internal logic. The approach lacks transparency for governance.
If you want to be a leader instead of a follower, you should be an early adopter of predictive analytics in the healthcare industry. HPG wants to partner with you to help you take AI from theory to practice. We can help you effectively manage your predictive models that require extensive security measures to protect them. Contact us now to get started. Learn how to manage the risks and maintain HIPAA compliance in our comprehensive guide.
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New tech companies are developing specialized tools for every industry, and each one touts surges in productivity, capabilities, and efficient workflows. But there’s a lot of hype behind these claims, and buying in on the wrong tools can balloon your IT spending without significant ROI. Even worse, AI in healthcare can pose real risks that are more dangerous than unfilled promises. Data security breaches, “black box” methodologies that can’t tell you how AI tools arrived at different decisions, and unpredictable biases. Whether you’re exploring machine learning or generative AI, the only way forward is to balance innovation with a heavy dose of ethics and compliance efforts.
“AI” is a very broad category that encapsulates a wide range of potential tools. From image processing AI that can spot the first warning signs of cancer in millions of different patient images to smart AI assistants that help doctors and nurses intake patient data, “on the ground” tools can perform many different functions. Billing, procurement, and administrative tools can also help organizations trim waste, stay on top of inventory, and manage large swathes of personnel, patient, and medical data.
While it’s important to be cautious, it’s also important not shy away from AI adoption or to ignore the transformative capabilities it already houses. Both the cautious and the innovative can both agree on a fundamental starting point: building out the technical and governance infrastructure to make AI exploration safer and more implementation-ready.
The first use case is those “on the ground” applications. AI tools can manage dozens of small, repetitive tasks that bog down operations at clinics, hospitals, and doctors’ offices. For example, different tech platforms and programs could:
The second, corporate-level use case is just as important (though just as perilous without proper implementation). Consider applications like:
Governance protects your organization, your staff, and your patients from the potential hazards AI tools can bring—not just in erroneous “hallucinations” and widespread errors, but also in ensuring the data each tool accesses complies with HIPAA and other constraints. The four core tenets for good governance are accountability—ensuring there’s a clear line of responsibility, transparency—ensuring stakeholders can see how AI makes its decisions and what its activities are, fairness—that the AI doesn’t use algorithmic biases or bad training data that worsens outcomes, and safety through continuous monitoring and stringent screening of AI tools before they’re adopted. Humans must remain in the loop and in charge at all times.
The first place where your organization might adopt AI is in your EHR. AI tools integrated into your preferred EHR—whether they’re native or secure third-party tools—can automate routine tasks, support documentation like a scribe, and summarize patient data based on a physician’s focus. AI can even generate smart checklists and prompt physicians if it notices a crucial step is incomplete. From small, optional nudges to active partnership, AI can transform EHRs from databases into assistants.
Build your organization’s roadmap to incorporating AI with these broad steps:
Virtually every healthcare organization benefits from adopting AI, especially in their EHRs and administrative functions, where repetitive tasks and large amounts of data open the door to human error. But it’s important to start with a partner that can help you identify your goals, build out your technical infrastructure, and ensure continuity of care as you experiment. Reach out to HPG to see how we’ve helped organizations like yours get started.
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A Cerner to Epic transition can radically improve your healthcare operations. But like all large projects, there are healthcare IT project risks to account for. By considering the most common challenges and proactively creating measures to both reduce those risks and quickly resolve any problems, you can de-risk your EHR migration and have a smoother transition that helps your company, staff, and patients see improved outcomes faster.
There are numerous risks that can plague EHR migrations, and rip-and-replace transitions are one of the riskiest changes an organization can make. At HPG, we have a long history of creating smooth migration plans, proactively addressing and resolving risks, and giving our clients a solid start to improved operations with Epic. Here are the three biggest risks we frequently see in a Cerner to Epic transition and the steps we take to de-risk EHR migration efforts.
The biggest risk is that the data fails to migrate properly. It may refuse to connect, data may go missing, or data may go to the wrong fields or show up as information full of errors. Data is complex, and healthcare professionals can’t operate safely in an environment where data is wrong, missing, or untrustworthy.
HPG has developed a detailed, thoroughly tested data integrity methodology that’s specifically designed for managing and preserving complex medical data. During our Cerner to Epic transition project for Boston Children’s Hospital, our team protected large volumes of pediatric data. Our methodology ensured a reliable continuity of care, so there was no interruption to services. The data was preserved in the prior system and cleanly duplicated and migrated to the new ecosystem for seamless operations during Boston Children’s Epic implementation.
Technical data complications aren’t the only challenge in large-scale Cerner to Epic transition projects. Your staff may also be uncomfortable with the new platform, reluctant to make the switch when it leads to delays, frustration, and added tasks for their teams.
At the Hospital of Central Connecticut, HPG put together a plan to encourage proactive support and adoption, making sure the human element saw excellent results. We started by engaging physicians early, giving clinicians leadership roles in developing change management strategies that authentically accounted for their concerns and expected challenges. By involving clinicians early in the project, all stakeholders could work together to resolve anticipated difficulties and provide resources where clinicians needed them. Physicians were also able to see the clear benefits their organization was striving for through the Cerner to Epic transition, further improving engagement and adoption, so productivity stayed more consistent and there was much less frustration.
Any change causes disruption, and the bigger the change, the bigger the potential disruption. Staff won’t be able to reflexively navigate the platform as easily as one they are familiar with. Your IT teams may also be overwhelmed with managing aspects of the migration, causing delays in responding to typical IT needs. Even when the data is secure and accurate, users often struggle to complete tasks and keep clinical operations on track with their usual speed and confidence.
HPG manages business and clinical disruption by knowing how it commonly manifests and what responses keep the chaos contained to manageable levels. For every hospital project, we organize a Command Center and implement a hypercare support model, so support is instantaneously available for the ED, OR, and revenue cycle systems. We stay active before, during, and after the cutover so each disruption is handled quickly and efficiently.
HPG manages Cerner to Epic transitions, Oracle Health to Epic transitions, and other major data transfer projects. Our team is committed to de-risking EHR migration, and that starts by developing plans that address common and organization-specific healthcare IT project risks. Learn more about why healthcare organizations trust HPG by reviewing our EHR replacement case studies, or reach out today to start planning your migration project.
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Before you embark on purchasing a new electronic health record (EHR) system, develop transition checklists, one for each phase. will help ensure proper choice, transition, and implementation. Each EHR transition checklist plays a critical role in a successful transition for staff and patients.
Developing an EHR migration project plan before you get to work on a new EHR system will save you many headaches down the line. Even while you are in the process of choosing a system, you can develop the rest of your pretransition checklist.
Understanding Your Needs and Preferences: Map out workflow and determine where troubles begin, where staff may be wasting time, and where you see errors. Additionally, document any special or irregular requirements, the complexity of the data, and your patient volume.
Knowing What Existing Systems the New ERH will be Connected to: Consider other software you are and will continue to use, such as imaging, billing software, and management tools, to ensure your choice of an ERH system will flow seamlessly with them.
Choosing an EHR Champion and the Rest of Your EHR Team: Your project manager, or “EHR Champion,” needs an understanding of IT and healthcare. Other team members need to understand the technical requirements and clinical workflows while specializing in specific responsibilities. The team will include IT staff, trainers, clinical specialists who will use the system daily, and compliance personnel who ensure proper regulations are met during the transition.
Choosing Your Software: Determine what you must have, should have, and really don’t need in your new EHR system. Reanalyze the pros and cons of each and how the cost factors into your budget.
Once you have chosen and purchased your new software, it is time to work with the EHR consultation team to install it and integrate your existing systems with it.
Together you should:
Now it is time for everyone to become familiar with the new EHR system and begin to collaborate with it. Your health information management training team members should have gained an understanding of the new system during Phase 2 and be ready to train others.
First, test the new software and ensure it is performing properly. Follow the training schedule as already prepared and determine a working transition plan for when use of the new system will officially start. Tell patients about the new system.
Develop contingency plans for any issues that may develop on go-live day. Create a process for reporting and documenting them. Be prepared for the start day with dedicated troubleshooting personnel ready to manage issues for staff and patients.
You are there; launch day. Ensure you are properly staffed and ready for triage. Be sure to document issues for post-live follow-up through another comprehensive checklist.
You will want to:
Purchasing and implementing a new EHR program is a huge step, and while it may seem daunting at times, preparing an organized EHR transition checklist at the onset is crucial for mitigation and your success. So is choosing the right EHR consulting partner. Reach out to our team at HPG to get started today!
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Every EHR implementation faces predictable challenges. How you prepare for those challenges will determine if your implementation succeeds or fails. Whether you’re migrating legacy data into Epic or standing up a new Oracle Health environment, the technical build is rarely the hardest part. What many organizations overlook is the importance of aligning people, processes, and data behind a shared goal.
At HPG Resources, we’ve seen how even well-funded projects falter when stakeholder buy-in lags or data migration gets messy. Below, we break down the most common challenges and show how HPG’s proven methodologies help organizations move from resistance to adoption.
The Problem: Legacy EHR data often spans decades. This means it’s pulled from multiple systems that were never designed to talk to one another. Inconsistent formats, missing values, and outdated records make it difficult to migrate data cleanly into Epic or Oracle Health. When errors slip through, clinicians lose confidence in the new system before go-live.
HPG’s Solution: HPG deploys a proprietary data validation methodology built around automated mapping, multi-tiered cleansing, and clinical review loops. Our team ensures that every patient record transferred is complete, accurate, and ready for the care team to use on day one.
Proof: For a regional healthcare network implementing Epic, HPG migrated over 15 years of patient data with 99.8% accuracy. By involving clinical champions early in the validation process, we were able to preserve data integrity and strengthen trust in the new system before go-live.
The Problem: Clinicians accustomed to long-established workflows may view new technology as an obstacle to patient care rather than a tool for efficiency. When adoption stalls, timelines slip, and ROI fades.
HPG’s Solution: Our clinician-led change management programs translate technology into practical, workflow-level benefits. We embed clinical subject-matter experts within implementation teams to co-design training, address pain points, and champion peer-to-peer engagement.
Proof: During an Oracle Health implementation for a large community hospital, HPG achieved 92% physician adoption within 60 days of go-live. With a focus on user-centric training and active communication, we turned skepticism into advocacy and accelerated performance benchmarks that the hospital expected to take six months.
The Problem: As EHR projects evolve, competing priorities emerge, such as new modules, custom integrations, or “nice-to-have” reports. Without disciplined governance, scope creep can drain budgets and erode confidence.
HPG’s Solution: HPG’s governance framework aligns every project milestone to measurable business outcomes. Through structured steering-committee cadence and proactive risk tracking, we help clients say “no” to distractions that jeopardize timelines.
Proof: When a multi-hospital system expanded its Epic footprint, HPG’s project governance model kept the rollout on schedule and under budget by 11%. By maintaining clear scope boundaries, the client delivered a phased implementation that scaled predictably.
The Problem: Testing is often the first task compressed when deadlines tighten. Incomplete scenario coverage or insufficient end-user participation can expose serious issues only after go-live, when they’re most expensive to fix.
HPG’s Solution: HPG integrates testing throughout every implementation phase—from unit and integrated testing to full user acceptance cycles. We simulate real-world clinical workflows to ensure that every interface, alert, and report performs reliably before launch.
Proof: For a children’s hospital transitioning to Oracle Health, HPG’s early test-case automation identified over 350 integration defects before go-live, preventing costly downtime and accelerating post-launch stabilization.
The success of EHR implementation depends on people, process, and preparation. HPG Resources partners with healthcare organizations to turn predictable roadblocks into measurable wins.
Don’t just plan for success; prepare for challenges. With the right strategy and support, even the most complex Epic or Oracle Health project can become a showcase for digital transformation done right.
Worried about these challenges? Let’s talk. Schedule a 30-minute strategy session with an HPG implementation expert and discover how we can help your organization move from resistance to adoption.
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Oracle’s vision certainly is bold, but that’s exactly what the industry needs to overcome the challenges that make healthcare so difficult to navigate…
Oracle’s vision certainly is bold, but that’s exactly what the industry needs to overcome the challenges that make healthcare so difficult to navigate…