Epic and Cerner automation: hyperautomation explored
Epic and Cerner ready to leverage hyperautomation capabilities – RPA, IDE, AI, ML, and other key elements – and automate end-to-end flows – cutting scrolling and clicks, eliminating handoffs, and fueling daily efficiency. Epic and Cerner do not have to be those scary, messy systems.
For executives, the payoff is measurable operational change: less hours, lower cost, fewer denials, more results.
Epic & Cerner systems are the very center of nearly every hospital and its clinical and administrative workflows. They hold the rules, the information, and the daily friction that steals clinician time.
But what about using hyperautomation in healthcare workflows – are we there yet?
Epic vs Cerner unraveled: quick dive-in
Epic and Cerner are the two most popular electronic health record providers across the healthcare landscape – both provide information management and exchange, clinical documentation, mobile access, decision support, and revenue cycle management.
But how are these providers different?
Epic places an emphasis on improving patient engagement and facilitating the delivery of remote patient care. It serves large-scale facilities by integrating ambulatory services and comprehensive revenue management.
Cerner provides secure access to up-to-date patient information and serves a range of different scale clients. Those include specialty practices, urgent care centers, group medical clinics, and others.
In the table below, you’ll find an overview of the main characteristics in a side-by-side comparison:
| Epic EHR system | Cerner EHR system | |
| Client base | Large-scale facilities | A range of different scale clients |
| Market share | A 39% market share by beds in the United States | A 29% market share by beds in the United States |
| User interface | More intuitive, requires training | Less intuitive, requires training |
| Implementation complexity | Very complex and resource-intense | Situationally complex |
| Customization level | Custom templates and workflows | Custom dashboards |
| Third-party integration | Not available | Over 50 |
Epic vs Cerner unraveled: market insights
EHR software has revolutionized healthcare operations, eliminating traditional paper-based documentation. EHR systems are deployed for seamless information management and exchange, clinical documentation, decision support, and other mission-critical processes within larger healthcare facilities.
Talking about electronic health record systems, Epic and Cerner corporations are undeniable market leaders and capture over 50% of the market share.
As to the most recent report by Market.US, Epic is the leader among today’s technology providers:
- Epic corporation serves over 250 million patients worldwide
- Their system is trusted by over 250 thousand healthcare professionals
- The company has reported a robust annual revenue of about $3.4 billion, which highlights its sizable market share in the healthcare market
- The platform is used to manage over 60% of records in the United States
As to the same 2024 report by Market.US, Cerner is not too far behind:
- Cerner corporation is used by over 27 thousand healthcare facilities all over the world
- Their system is trusted by over 125 thousand healthcare professionals
- The organization has reported a substantial annual revenue of around $5.5 billion, which emphasizes its strong market presence and great financial stability
- The platform is deployed in about 35 countries – an extensive global reach
Healthcare automation – work smarter, not harder
Healthcare operations performed day-by-day are monotonous, usually resource-intense, and reduplicative. Patient registration, appointment scheduling, data entry and update, prescription management, and others require hours of retrieving and re-entering patient information.
Healthcare leaders refusing innovation are missing promising opportunities and losing competitive strength. Increased time and cost, decreased accuracy, employee burnout, and non-compliance are just some problems to expect when counting on traditional manual execution.
Hyperautomation solutions can introduce greater performance without disrupting mission-critical operations.
No wonder the global healthcare automation market size was estimated at impressive $35.2 billion in 2022. The numbers speak volumes.
Healthcare automation gaining momentum
As to an older yet depictive McKinsey study, about 50% of global work activities can be successfully automated. More specifically, those tasks most susceptible to automation are activities in highly structured environments – the operations in the healthcare industry, government, manufacturing, and others.
As to an illustrative Fact.MR report of 2023:
- The global healthcare automation solutions market was valued $38.6 million in 2023
- The demand is estimated to reach $94 billion by 2033
What is hyperautomation in healthcare operations?
Hyperautomation as an approach is expanding healthcare automation and emphasizing cognitive capabilities. In essence, acting as a conductor, it coordinates specialized technologies – RPA, LCNC, AI, ML, and orchestration – to perform end-to-end processes as one well-adjusted mechanism.
Hyperautomation holds the promise of stitching the components – from native to advanced – neatly together.
In the healthcare industry, that means to connect intake forms, scanned documents, and other data sources into one process sequence that eliminates routine repetition, reveals problems, and supports better decisions. The goal isn’t replacing the clinician but removing the burden.
What about electronic health record systems?
What is hyperautomation in EHR systems?
EHR software is both the score and stage for everyday clinical operations: they hold the records and processes. That makes them ideal to start the stitching of automation into workflows.
EHR software, at least the heavyweights, already provides the foundation: AI helpers, CDS systems, and more. Each block either supplies the triggers, the data (structured fields and documents), or destination for results.
What does it look like when these blocks are composed into interconnected, (hyper)automated flows?
Here’s a before/after snapshot: same goals, but far fewer clicks and handoffs:
| Manual process | Hyperautomated process | |
| Patient registration | Front-desk staff manually enter data from paper/portal forms and call insurance companies to verify insurance coverage – any mismatch and typo cause delays | Form ingestion, patient matching, insurer queries, and flagging of mismatches for review are automated, which means faster check-in, fewer errors, and reliable audit trail |
| Clinical documentation | Medical staff either type or paste their notes after appointments – the documentation is resource-intense, often delayed, and inconsistent, typically affecting patient throughput | The clinician will receive a summary with auto-populated, structured fields and lists, as well as suggested medical codes, which means less work, faster completion, and consistency |
| Prior authorization | Medical staff manually fill the forms, fax/email payers, and make patient follow-ups – long turnaround, lost requests, and lots of resources spent wastefully | The system will initiate intelligent extraction, automated submission, and tracking to speed up authorization and eliminate lost requests |
| Claims management | Medical staff manually batch claims submitted – frequent denials then require manual appeals and rework, which means great expenses | The system will initiate preliminary validation, automated resubmission, and analytics to minimize potential denials and facilitate faster reimbursement |
Epic & Cerner systems: hyperautomation explored
Epic & Cerner systems – the heart and archive of hospitals – are running a bulk of everyday hospital workflows. That role makes them the most critical places for innovation – an improvement in charting or coding can scale across departments and have large-scale impact on efficiency.
Epic & Cerner ship the core building blocks for automation – AI features, CDS systems and analytics, and more. End-to-end automation, however, presents an engineering composition problem of stitching native capabilities with technologies that cover complex tasks (RPA, IDP, AI, ML) and the binding layer of orchestration.
Hyperautomation remains a phased, governed effort rather than an instant flip-of-a-switch solution.
Let’s get into detail.
Epic and Cerner systems’ building blocks: hyperautomation foundation
Epic and Cerner automation is achievable through tooling these platforms already provide.
Epic automation – all inside and around clinical workflows
Epic automation is focused on minimizing clinician workload and error inside existing healthcare workflows rather than entire processes.
Epic automation already provides:
- Clinician-facing helpers – specialized features to minimize mouse clicks and facilitate a wrap-up:
- Draft-note generation
- Note summarization
- Assisted charting
- Order suggestions, and more
- Clinical decision support system – built-in rules to minimize human error and drive better delivery
- Third-party integration – a supported API set and marketplace for certified third-party applications
- Data sharing & interoperability – built-in tools for secure data exchange and connection
Cerner automation – similar approach, different emphasis
Cerner system workflow automation is more about prioritizing data aggregation and analytics, and extensibility, not providing end-to-end assistance.
Cerner automation already provides:
- Population-health platform – a cloud/native data layer for aggregating EHR and non-EHR records
- CDS system & analytics – built-in modules that surface care gaps, quality measures, risk stratification, and other critical analytics
- Third-party integration – FHIR/SMART-on-FHIR support and ecosystem for applications that automate data writing/reading
- AI agent & voice – Oracle Health has promised the newer EHR releases to embed AI-first features
Hyperautomation stack’s core components
The layer of orchestration – the conductor
The so-called rules engine that listens for events (API events, scheduled jobs, and alerts) and issues the tasks. This enforced the order of steps and retries.
Data sources and triggers
CDS & FHIR/SMART endpoints – the blocks that provide the competent patient context and trigger the events. These are the signals the system will consume.
Robotic process automation (RPA) – the adapter
The component that simulates human interaction with screens or forms to perform a routine, rule-based task. It’s an engineering shortcut, not the long-term plan.
Intelligent document processing (IDP) – the parser of inputs
The component that converts scanned forms, referral letters, and notes into records the system can consume. It closes the gap where discrete APIs do not exist.
AI/ML & NLP algorithms
AI/ML models – the layer of prediction and augmentation that produces note summaries, risk scores, and more. NLP model – the layer that extracts the meaning from text and drives further decisions.
The middleware
Translation layers, message busses, and adapters – the bridge between layers that normalizes data formats. They keep the integrations robust across all customizations and upgrades.
Hyperautomation roadmap step-by-step rollout
1. The pilot
First, inventory and score end-to-end workflows to pick one high-value, low-risk workflow and start the pilot.
2. Success metrics and baseline
Second, choose 3-4 metrics (saved time and cost, error rate) and capture the baseline from measurements.
3. Minimal architecture
Further, develop minimal architecture: an orchestrator, CDS, APIs, and simple human-in-the-loop interfaces and document data flows, audit logging, and where the decisions are managed by system versus human.
4. Minimal viable stack (MVS)
Next, implement end-to-end chain that validates the concept and keep minimal viable stack clean for iteration.
5. Silent validation
Run automation in parallel without altering live decisions to compare the results of system versus human (measure accuracy, false positives/negatives, and more).
6. Controlled testing
Move to controlled testing where automation suggests actions but employees must review every decision.
7. Last steps before go/no-go
Hold sign-off with leads, compliance, legal, and security to review the metrics and plans for training and rollback.
8. Controlled launch
Enable automation for production but with limited scope and explicit real-time dashboards for monitoring.
How far are we bringing in hyperautomation to Epic & Cerner users?
Let’s explore some examples of products and frameworks already introduced to bring hyperautomation closer. These solve specific problems, but none of them can deliver hyperautomation directly.
The elements that indicate hyperautomation components are already being applied within these are present:
- Hyperscience’s “Hypercell” – a product that integrates with both to handle:
- Document processing
- Data extraction
- Claims processing
- Onboarding workflows
- Stanford’s “DEPLOYR” – a framework to deploy AI/ML models when triggered by events
All these are about partial automation – useful components – but not end-to-end automation by themselves.
So what’s to expect?
Hyperautomation prospects: Epic and Cerner workflows will change?
Epic automation? Quite doable.
Cerner automation? Yes, sure.
Hyperautomation for these systems? Good question.
Hyperautomation full-blown in the next years – rather not, a layered, modular growth – quite so:
- Orchestration platforms will become the differentiator: those organizations that master rules engines will scale end-to-end automation beyond pilots
- Data standardization and interoperability will matter more than ever before
- Tighter requirements and expectations will force healthcare organizations to bake regulatory compliance into pipelines from the very beginning
- Human factors will remain most important: trust, training, and intuitive human-in-the-loop experience will decide whether automation is welcomed
Hyperautomation attempts will arrive as small, careful stitches, not single rip-and-replace solutions right away. Industry-leading vendors will continue to provide building blocks while integrators will stitch them together.
The question is whether you’re in for it or not.
How we can help
Epic and Cerner provide the instruments, hyperautomation orchestrates the performance – the difference between ordinary, bustling rehearsals and polished, harmonious concerts is how the pieces sound together. Epic and Cerner have required potential, hyperautomation thereby is about correctly combining the parts.
Let’s turn low-hanging automation into sustainable, hyperautomation solutions to transform clinical workflows.
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FAQ
Large providers do offer custom stacks that are explicitly built to integrate with both Epic and Cerner platforms. In practice, these enterprise hyperautomation deployments are assemblies of components specifically tuned for local workflows only.
Hyperautomation for Epic and Cerner platforms does exist in the wild somewhere, but it’s commonly delivered as an engineered program, not a comprehensive product.
In brief, HIPAA compliance will depend on implementation and governance but not the technology in question. To introduce hyperautomation securely, you’ll need to enforce data encryption, privacy/security controls, incident response, least-privilege access, and other HIPAA-mandatory controls.
Without those, hyperautomation would definitely cause serious damage.
Talking about hyperautomation specifically, the main Epic and Cerner business automation gains would be:
- An increased data quality and consistency by extracting and writing normalized fields
- A reduced clerical burden by automating end-to-end workflows (charting, coding, and more)
- A shorter turnaround time for administration through orchestration of everyday routine tasks
- A faster revenue cycle through streamlined claims validation and submission (among others)
Talking about hyperautomation specifically, the main Epic and Cerner workflow automation barriers would be:
- Data inconsistency, which reduce the model’s overall accuracy and reliability
- Heavy customizations, which increase the per-site engineering efforts
- Orchestration complexity (exceptions, retries) that demands operational maturity and tooling
- Regulatory/privacy/audit requirements that cause additional overhead
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