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EHR AI Tools & AI Software for Healthcare Reporting & Compliance
- Quality AI tools can provide a more accurate alternative to manual EHR reporting.
- These tools can also help healthcare facilities improve efficiency when handling various other repetitive tasks.
- Working with HPG’s experts can help your team leverage AI resources to gather more data and use it more effectively.
AI’s role in the healthcare industry is evolving quickly, and this technology is transforming how many facilities manage EHR. Here are some of the most important things to know about how HPG’s EHR experts can help your healthcare facility leverage AI tools to better understand your data and accomplish more.
The Role of AI Software for Healthcare in Data Management
Quality AI software can help healthcare facilities manage a wide range of everyday reporting tasks more efficiently. These tools can help your team overcome data silos, reduce manual data mining, and eliminate various other manual tasks to free up more time to focus on what really matters: providing the best possible experience for your patients. Many AI tools that are frequently used in the healthcare industry have strong natural language processing capabilities, which means that they can automatically transform clinician notes into structured data that gives your team an abundance of information.
Improving Accuracy With Automated EHR Reporting
Automating certain aspects of your facility’s EHR creation process eliminates human error and significantly reduces other potential discrepancies found in manual EHRs. Ensuring these records are as precise as possible is critical to delivering the best possible patient care and keeping your facility in compliance with industry regulations.
AI tools can also simplify the process of reporting and reviewing records in real time, and having easy access to what is happening right now instead of at some point in the past makes it easier to identify and mitigate potential problems as early as possible. These tools can also provide a wide range of specialized metrics that give your team data-driven insights into resource utilization, patient throughput, readmission rates, and other important KPIs.
Streamlining Compliance and Regulatory Standards
AI automation can quickly identify potential concerns, helping your facility maintain continuous compliance as much as possible. Tools that have an in-depth understanding of HIPAA, CMS, and Joint Commission standards can immediately flag concerning data, gaps in documentation, inaccurate coding, and other potential errors, enabling a team member to identify the source of these problems and resolve them as early as possible, drastically reducing liability. These tools can also verify data to ensure it is accurate, helping mitigate many potential risk categories.
Navigating Implementation With HPG EHR Experts
Every healthcare organization runs clinical workflows a bit differently, which means that successfully implementing AI EHR technology will likely require spending some time teaching these tools exactly how your system works. Quality AI tools can generally adapt to the needs of a variety of businesses, but making sure that they know what to look for in the first place requires sufficient setup from a person who is familiar with every detail of the workflows your facility is already using.
While this setup process is initially more involved than simply installing software, the extra effort of aligning tools with your organization’s practices and goals can go a long way toward accomplishing more over time. HPG’s EHR experts are here to help your organization optimize these systems and make intentional AI-driven strategic planning decisions throughout every step of this process.
Embracing the Future of Healthcare Technology
Making the most of quality EHR AI tools can play a key role in keeping your healthcare facility competitive and compliant. Working with one of our EHR experts to optimize these resources also makes a significant difference in making a wide range of essential but repetitive tasks that do not require constant human input as efficient as possible. We know that learning how to get the most out of complex new technology is not always easy, and we are here to help you understand everything there is to know about making your new AI tools work for you. Explore our service page to learn more about how we can help your healthcare facility leverage the latest technology!
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Common EHR Problems & Implementation Challenges: Workflow Solutions
- EHR can help many healthcare facilities that are still using manual health records improve efficiency and accuracy
- Implementing this software can involve several challenges, such as data overload, interoperability gaps, and user interface friction
- HPG’s EHR experts can help users quickly and easily solve these problems
Implementing EHR (Electronic Health Record) technology can play a major role in using your healthcare facility’s resources more efficiently, but getting used to this software often involves several potential challenges. At times, these problems can be significant enough to decrease efficiency and even make your team reconsider whether using it is worthwhile altogether. Fortunately, working with an expert like HPG can make implementing this type of software much easier to navigate. Here are some of the most important things to know about how HPG’s EHR experts can help you solve nearly any issue your team encounters to make sure that your new software meets the unique needs of your facility.
Identifying Common EHR Problems in Clinical Settings
EHR can be a much more convenient and accurate alternative to manual health records, but implementing any new technology can come with challenges. Some common problems that new EHR users encounter include:
- Data overload caused by too much information, which can lead to burnout for employees who struggle to filter which data is actually needed
- Interoperability gaps that lead to data silos by limiting your new software’s ability to seamlessly integrate with other programs your facility is already using
- UI friction, which can lead to too many unnecessary clicks, navigation issues, or other inconveniences that slow users down
Navigating EHR Implementation Challenges
Although the EHR implementation process may not always go as smoothly as healthcare facilities would like it to, there are several intentional steps your team can take to make this adjustment more successful.
In many cases, your employees may simply not have the knowledge or skills they need to use emerging technology without running into problems. Relying on a one-size-fits-all approach to training often results in significant gaps, and it is generally better to spend a bit more time considering the specific information your team members need to succeed and creating custom training plans that address the specific areas you are most likely to struggle with. Your team may also need to reconsider the most effective ways to allocate limited resources if you find that wasted money or downtime is holding your implementation back.
In some cases, your team may simply not want to make a change that is as significant as switching to EHR for cultural or other reasons. A general lack of interest in the latest technology or a preference for what has worked well for your facility in the past may make your team less committed to the work that goes into learning how to use new technology and giving it a chance. If your facility is experiencing a high level of resistance to change, taking time to listen to why and share your view of how it will benefit your facility and your patients over time can help your team feel more comfortable with the idea.
Optimizing EHR Workflow Issues to Improve Efficiency
Understanding the details of how your EHR workflow functions is an important step in getting the most out of your software.
Customization vs. Standardization
Putting enough thought into determining how to make EHR work for your organization is an important step in choosing software that offers the right level of customization. Programs that are too standardized are less likely to offer the flexibility facilities need to address elements that do not quite work right for them, while the most customizable programs include too many unnecessary features and tend to be too complicated for many users to optimize. Finding the sweet spot between these extremes is an important step in choosing EHR software that is likely to be a good fit for your organization.
Streamlining Documentation
Quality EHR software includes an abundance of innovative features that can help to reduce the amount of time that your team members need to spend on manual data entry and other basic tasks, such as voice recognition, templates, and macros.
Continuous Process Improvement
Simply setting up your new EHR software and forgetting about it is likely to result in missing out on new features and capabilities over time. Instead, being intentional about auditing how well your current setup is working and learning about updates regularly can go a long way toward getting the most out of your investment.
How HPG’s Experts Solve Complex EHR Problems
HPG’s EHR experts are here to help your team better understand everything there is to know about choosing and implementing new technology at your healthcare facility. We can help you learn more about system optimization, project management, and other key aspects of getting the most out of your EHR software and troubleshooting any issues you encounter along the way. By working with us, your team can spend less time trying to fix software problems yourself and more time improving your patient care strategies.
Master EHR Solutions to Boost Your Healthcare Facility’s Efficiency
Many healthcare facilities encounter challenges throughout the EHR implementation process, but most of these issues are easy to resolve when working with HPG’s EHR experts. Contact us today to learn more about how the latest EHR technology might fit into your current workflow and how we can help you make sure your investment reaches its full potential!
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HPG Joins the Oracle PartnerNetwork to Expand Healthcare Technology and Cloud Services Capabilities
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.
Oracle Health vs. Epic: An Unbiased Look at Their AI Roadmaps
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.
Analyzing the Vendor Landscape (2026 Update)
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 Health’s Approach: The “Built-In” AI Ecosystem
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.
Pros:
This system offers a number of advantages.
- Massive scalability
- A unified data layer/semantic database
- Enterprise-grade security
Cons:
On the other hand, it does come with potential disadvantages for some users.
- Significant transition pains for legacy Cerner users
- The complexity of moving to a full-cloud environment
Epic’s Approach: The “Walled Garden” Curated Integration
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.
Pros:
Epic offers a number of potential advantages that medical providers should consider when selecting their EHR system.
- Higher (and smoother) user adoption due to familiar UI
- “Cosmos” data set that provides massive training potential for predictive analytics, streamlining decision-making and offering deeper insights
Cons:
Before you choose your EHR, make sure you’re prepared for Epic’s potential downsides.
- A closed ecosystem can make it more difficult to integrate both third-party and proprietary AI models
Beyond the Platform—Universal CIO Priorities for 2026
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.
1. Prioritize Data Utility over Data Volume
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.”
2. Focus on “Agentic” Outcomes
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.
3. Ensure Human-in-the-Loop Governance
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.
Strategy Over Software
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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AI, Security, and HIPAA: A Healthcare Leader’s Guide to Compliance
- With AI and LLMs, it’s time to reassess security and the framework protections for HIPAA.
- You need an ethics and AI review board.
- Use Business Associate Agreements (BAA) to protect your patients from third-party vendors.
AI, Security, and HIPAA: A Healthcare Leader’s Guide to Compliance
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.
Understanding the New Threat Landscape
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:
- Data Poisoning: The data you use for training can be compromised and cause issues with the model’s integrity.
- Model Inversion: You run the risk of “reverse-engineering” a model to extract sensitive training data (PHI).
- Privacy Leaks: The danger of Large Language Models (LLMs) “remembering” and inadvertently sharing patient details.
A Strategic Framework for Healthcare AI Governance
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:
Principle 1: Establish an AI Ethics & Review Board
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.
Principle 2: Ensure Data Provenance and Anonymization
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.
Principle 3: Implement “Explainable AI” (XAI)
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.
Principle 4: Rigorous Third-Party Vendor Vetting
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.
AI and HIPAA: Navigating Compliance in the Age of LLMs
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.
Governance as an Innovation Accelerator
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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How to Leverage AI for Predictive Analytics and Smarter Healthcare
- AI uses predictive analytics to take your healthcare business to the next level.
- Historical data predicts future outcomes.
- Using AI can reduce readmission rates.
How to Leverage AI for Predictive Analytics and Smarter Healthcare
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.
Beyond Reporting—How AI Turns Historical Data into Future Insight.
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.
Clinical Predictive Analytics:
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:
Case 1: Early Sepsis Detection
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.
Case 2: Reducing Readmission Rates
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.
Operational Predictive Analytics: Optimizing the Business of Care
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:
Case 3: Forecasting Patient Flow.
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.
Case 4: Revenue Cycle Management (RCM)
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.
The Foundation: Why Clean Data and Strong Governance Are Essential
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.
Conclusion: Solving Tomorrow’s Problems Today
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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AI in Healthcare IT: Innovation & Strategy Blueprint
Demystifying AI in Healthcare: Moving Beyond the Hype
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.
The Spectrum of AI: From Machine Learning to Generative AI
“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.
Use Case #1: Enhancing Clinical Intelligence and Decision Support
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:
- Predictively analyze patients’ or individuals’ conditions over time to predict outcomes and interventions
- Analyze millions of data points to make initial diagnoses or verify staff’s decisions
- Develop personalized treatment plans for physician approval
- Take notes, summarize information, and send data to next-stage destinations
- Verify staff decisions against the latest available information regarding guidelines, drug databases, and regulatory information
Use Case #2: Streamlining Operations and Automating Administrative Tasks
The second, corporate-level use case is just as important (though just as perilous without proper implementation). Consider applications like:
- Generating, storing, and synthesizing notes
- Managing EHRs by verifying and cleaning data
- Verifying billing and coding accuracy
- Identifying trends in patient care and expenses
- Supporting efficient procurement and inventory management
The Governance Imperative: AI Security, Ethics, and Compliance
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 Platform Question: How AI Is Reshaping Core Systems (EHRs)
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.
Building Your Organization’s AI Roadmap
Build your organization’s roadmap to incorporating AI with these broad steps:
- Establish the goals of AI implementation.
- Conduct a readiness assessment that evaluates data and technical infrastructure, as well as stakeholder buy-in.
- Identify low-risk use cases for limited exploration and pilot programs.
- Select secure vendors and implement tools more broadly.
- Continue to scale and implement a concrete governance framework.
Take the Next Step in Your AI Journey
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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The 3 Biggest Risks in a Cerner-to-Epic Migration (And How to Solve Them)
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.
Navigating the Most Complex Move in Healthcare IT
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.
Risk #1: Catastrophic Data Migration Failure
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.
Resolution and Proof: Ensuring Data Integrity at Boston Children’s Hospital
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.
Risk #2: Physician Revolt and Poor System Adoption
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.
Resolution and Proof: Clinician-Led Change at the Hospital of Central Connecticut
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.
Risk #3: Massive Business and Clinical Disruption
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.
Resolution and Proof: The HPG “Command Center” and Hypercare Model
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.
Proven Expertise for Your Most Critical IT Initiative
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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The Ultimate EHR Transition Checklist for Project Managers
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.
Phase 1: Pre-Transition Checklist (Months -12 to -6)
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.
Phase 2: Design & Data EHR Data Migration Checklist (Months -6 to -3)
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:
- Organize a mapping plan for proper migration
- Determine what needs to be migrated to the new system
- Determine what data needs to be shared between the new EHR system and existing systems
- Decide where the data that will not be migrated should be stored
- Schedule your training sessions
Phase 3: Testing & Training Checklist (Months -3 to -1)
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.
Phase 4: Go-Live & Hypercare Checklist
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:
- Review the post-go-live checklist
- Obtain feedback from staff and patient users on the functionality of the program
- Determine the most critical issues and address them according to severity
- Transition to a regular IT support team with their healthcare IT project checklist
- Monitor the system daily and check for overlays, duplication, and other data discrepancies
Navigating the Transition with Confidence
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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