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Beyond Reimbursement Analysis: How to Build BETTER DRG Success

by Odelle Technology

Introduction Why Reimbursement Analysis Fails to Give You an Accurate Picture of MedTech Funding

In every DRG-based health system — whether Germany’s G-DRG, France’s GHS, the UK’s NHS Payment Scheme, Italy’s DRG nazionale, the Nordics’ NordDRG, or Belgium’s evolving case-mix framework — a reimbursement analysis provides only a thin snapshot of the present.

It does not represent clinical reality,
it does not represent hospital economics,
and it certainly does not represent future funding potential.

Almost anyone can produce a report showing:

  • the current DRG assignment
  • a list of procedure codes (CCAM, OPS, NCSP, OPCS-4)
  • national tariff tables
  • a simplistic “gap analysis”

But none of this tells you whether the DRG is correct.
And almost always — it isn’t.

The reality is this:

A reimbursement analysis is descriptive.

DRG economics are predictive.
Strategy is transformative.**

What MedTech companies actually need is not a report, but an understanding of how DRGs are scientifically built:

  • Cost-weight modelling (LOSR, MCC, PCCL rules)
  • National cost-matrix sampling from hundreds of hospitals
  • Allocation of direct vs indirect cost centres
  • Microcosting vs macrocosting evidence
  • Operating room minutes, consumables, and ICU impact
  • Length-of-stay differentials
  • Surgical technique variation and resource consumption
  • Case-mix index differentials across hospitals

These are the variables that determine:
how national DRG institutes calculate your tariff.

A static reimbursement report cannot:

  • show resource-weight distortion
  • identify incorrect DRG placement
  • quantify economic misalignment
  • highlight microcosting drivers for tariff uplift
  • reveal hidden add-on or supplemental funding pathways
  • show how surgeons can build a new DRG application
  • or explain how coding teams actually increase hospital revenue

This is why reimbursement analyses frequently mislead MedTech companies into thinking they have a pathway when they don’t — or believing a low tariff is a dead end when it is actually fixable.

The economic truth is simple:

Your funding depends not on a report, but on how well you influence:

– the code,
– the DRG,
– the cost-weight,
– and the national tariff-setting process.**

This requires a completely different skill set:

  • negotiating with InEK (Germany), ATIH (France), NHS England, TLV (Sweden), Nye Metoder (Norway), AOTMiT (Poland)
  • designing multi-centre real-world costing studies
  • generating surgeon-led justification notes
  • aligning with clinical societies for official endorsement
  • preparing DRG uplift and innovation payment applications (e.g., NUB, RIHN, RIHP, Add-Ons)
  • coordinating coding corrections at hospital level
  • proving resource-intensity differentials with empirical data

This is the work that hospitals do not have time to do.
And it is the work that most “reimbursement consultancies” simply do not know how to do.
They analyse — but they do not build.

In this blog, we go far beyond reimbursement analysis to explain:

  • why current DRGs rarely reflect real clinical practice
  • how tariff algorithms and cost-weight models function inside each country
  • the role of microcosting in DRG redesign
  • hidden add-on and supplemental funding pathways
  • how to work with surgeons, coders, and DRG committees
  • what national DRG bodies actually require
  • how MedTech companies can shape DRG evolution, not just map it

Because without DRG engineering, even the strongest clinical evidence and the best reimbursement report will leave your innovation unfunded, unused, and unknown.

Why Reimbursement Analysis Fails:

The Missing Science of DRG Construction

Most MedTech companies assume that a reimbursement analysis gives them a full, accurate view of the funding landscape.
It does not.
In fact, it often distorts the picture.

A report can tell you what tariff exists, but not:

  • why that tariff exists,
  • how it was calculated,
  • what data is missing,
  • where the errors are, or
  • how to change it.

To understand why reimbursement analysis fails, you must understand the science behind DRG construction.

DRGs Are Built on Cost-Weight Models Not on Clinical Evidence

A DRG is not determined by:

  • how effective your technology is
  • how clinically important it is
  • or how innovative it is

A DRG is determined by a cost-weight model, built from:

  • sampled hospital cost data (Germany’s InEK-Kalkulation)
  • cost-centre allocation (France’s ENCC / ATIH cost-matrix)
  • national reference costs (UK’s NHS Payment Scheme)
  • direct and indirect cost grouping (NordDRG costing methodology)
  • real-world resource consumption (ICU hours, OR minutes, implants, length of stay)

This means:

If hospitals do not yet use your technology, the DRG has no way to fund it.
Your innovation is invisible inside the cost model.

A reimbursement report cannot show this.
But DRG engineering can.

DRGs Are Based on Past Data — Not Future Need

Every DRG in Europe is built using retrospective costing.

  • Germany: cost data from 18+ months earlier
  • France: GHS tariffs based on historical ENCC datasets
  • UK: HRG weights based on prior-year reference cost submissions
  • Nordics: NordDRG using multiyear cost aggregates

Your device will never appear in the cost model if:

  • it is new
  • poorly adopted
  • inconsistently coded
  • used only in pilot sites
  • or embedded inside an older surgical technique

This creates a structural economic flaw:

**Innovation cannot be reimbursed until it is used,

and it cannot be used until it is reimbursed.

A reimbursement report does not solve this paradox.
Only DRG uplift strategies and innovation payments (e.g., NUB, RIHN, NHS High Cost Device options) can.

DRGs Are Coded Wrong in Most Hospitals — And Reports Don’t Show That

A reimbursement analysis assumes that coding is correct.
But hospital coding audits consistently show:

  • 25–40% of cases are miscoded for complex surgeries
  • implant usage is not captured in OPS/CCAM codes
  • device-related costs are not allocated to the correct cost centre
  • surgeons write incomplete operative notes
  • coders are not trained in new procedure classifications
  • new technology often gets grouped under an old DRG

This means the DRG “assigned” to a technology in a report is often:

  • clinically incorrect
  • economically distorted
  • inaccurate in real-world settings

A reimbursement report presents a simplified, artificial universe.
DRG engineering deals with the real one.

Reimbursement Analyses Ignore the Algorithms That Actually Group Cases

Every DRG system uses grouping logic:

  • Germany: G-DRG Grouper (Diagnosis, Procedure, PCCL, LOS trims)
  • France: GHM / GHS algorithm
  • UK: HRG4+ Grouper
  • Nordics: NordDRG logic tables

These algorithms include:

  • hierarchy rules
  • split logics (complexity, comorbidity, age)
  • procedure-dominant logic
  • surgical technique mapping
  • ICU weight adjustments
  • trim points and outlier rules

A reimbursement report simply “states” the DRG.

It does not analyse:

  • whether your device should be in a different DRG
  • whether it deserves a higher cost-weight
  • whether it triggers complexity split rules
  • whether LOS differences warrant an uplift
  • whether the algorithm misclassifies your procedure

Only DRG engineering handles this.
Reports do not.

Reimbursement Analyses Don’t Include Micro costing. The One Thing DRG Bodies Care About

National DRG institutes (InEK, ATIH, NHS, TLV) rely on micro costing evidence for tariff adjustments:

  • OR time (cost per minute)
  • consumable cost lines
  • staffing ratios
  • ICU days
  • ward nursing intensity
  • sterilisation labour
  • recovery time
  • postoperative imaging or labs
  • complication rate economics

None of this appears in a reimbursement report.

Without microcosting:

  • a DRG uplift request cannot be defended
  • an add-on payment will be rejected
  • a supplemental tariff cannot be justified

This is the scientific core missing from competitor analyses.

Reimbursement Analyses Do Not Include Surgeon Notes, Society Endorsements, or Applications

Every country requires clinician-driven submissions:

  • Germany: NUB + OPS proposals + DRG uplift
  • France: CCAM petitions + ENCC costings + RIHN
  • UK: HRG coding evidence + device cost transparency
  • Nordics: NordDRG change requests + TLV evidence packs

These require:

✔ surgeon-written justification

✔ multi-centre utilisation evidence

✔ coding department alignment

✔ support from national clinical societies

✔ payer negotiation

✔ formal applications

A reimbursement analysis gives none of this.
It is economically inert.

A reimbursement report describes the system.

DRG engineering changes it.

Only the latter brings MedTech technologies to national adoption.

The Real Mechanics of DRG Building: Codes, Costs, and Classification

Most MedTech companies — and almost all reimbursement consultancies — underestimate how technically complex DRG construction really is.
DRGs are not static reimbursement buckets.
They are living classification systems, recalibrated annually and governed by:

  • coding rules
  • cost models
  • case-mix weights
  • clinical hierarchy logic
  • multi-centre resource data
  • national statistical sampling frameworks

Understanding this process is the difference between reimbursement analysis and actual DRG success.

Coding: The First Gatekeeper of DRG Classification

Every DRG begins with a procedure code, typically one of:

  • Germany: OPS (Operationen- und Prozedurenschlüssel)
  • France: CCAM (Classification Commune des Actes Médicaux)
  • Nordics: NCSP (Nordic Classification of Surgical Procedures)
  • UK: OPCS-4 (Office of Population Censuses and Surveys Classification)

Why coding matters:

  • If the code does not exist, your device is invisible.
  • If the code is wrong, it groups to the wrong DRG.
  • If surgeons write short or incomplete notes, coders assign legacy codes.
  • If hospitals don’t code the consumable, its cost never appears in the DRG dataset.

This is why DRG engineering often starts with:

  • surgeon note templates
  • coding department workshops
  • new OPS/CCAM/OPCS proposals
  • coding accuracy audits
  • classification rule corrections

A reimbursement analysis never includes this foundational work.

DRG Groupers: The Algorithms That Decide Your Tariff

Once the procedure is coded, national DRG groupers classify the patient episode.

Examples:

  • Germany: G-DRG Grouper (primary diagnosis, OPS hierarchy, PCCL, LOS trim points)
  • France: GHM/GHS (procedure dominance logic, age splits, severity classes)
  • UK: HRG4+ (dominant procedure, CC scores, complication bands)
  • Nordics: NordDRG (procedure-based and diagnosis-based logic modules) https://odelletechnology.com/within-swedens-obscured-regulatory-authority-the-dynamics-behind-the-adoption-or-rejection-of-new-medical-procedures-for-kva-coding/

These algorithms:

  • determine the case-mix index
  • apply complexity splits (comorbidities, complications, age)
  • set dominant procedure rules
  • establish weight multipliers for ICU or high-risk cases
  • apply trim point rules for outliers

Why this matters:
A new MedTech procedure often gets grouped under an old, low-cost DRG, because:

  • the grouping logic has not been updated
  • the procedure code is not mapped correctly
  • there is no recognisable resource signature in the dataset

This is where DRG-building starts:
mapping the new procedure into the correct grouping logic or proposing revisions to the grouper itself.

National Cost-Weight Modelling: How DRG Tariffs Are Calculated

Every DRG has a financial value generated through a national costing system:

Germany (InEK)

  • 300+ hospitals submit detailed cost data
  • All cost centres (OR, ICU, personnel, consumables, implants, overhead) are allocated
  • InEK generates relative cost weights for each DRG https://odelletechnology.com/the-g-ba-scientific-governance-and-reimbursement-authority-in-the-german-healthcare-system/

France (ATIH / ENCC)

  • National cost study (ENCC)
  • Case costs analysed by procedure type
  • Tariffs adjusted to real resource use across hospitals

UK (NHS England)

  • National Reference Costs
  • Cost curves for HRGs (HRG4+)
  • Efficiency factors and outlier adjustments

Nordics (NordDRG)

  • Local cost data fed into the common NordDRG model
  • Adjusted for country-specific case-mix variations

Why this matters:
If hospitals do not record your device’s cost or time properly, it does not enter the national cost matrix.
Then the DRG tariff is artificially low.

This is why DRG-building requires:

  • micro costing studies
  • OR time measurement
  • resource-use logs
  • multi-centre costing data
  • surgeon questionnaires
  • real-world utilisation extraction from hospital EHRs

A reimbursement report will never include this economic backbone.

Innovation Payments and Add-Ons: The Temporary Bridge to a Future DRG

Because DRGs lag behind innovation, most countries have temporary innovation funding mechanisms:

Germany: NUB (Neue Untersuchungs- und Behandlungsmethoden)

  • Hospital-level application
  • Negotiated supplemental payment
  • Valid until DRG is updated

France: RIHN/RIHP

  • Innovation funding outside GHS tariffs
  • Supports expensive or novel procedures

UK: High-Cost Device Uplifts

  • Carve-outs reviewed annually
  • Procedural add-ons for new technology

These pathways require:

  • economic justification
  • clinical rationale
  • surgeon support
  • coding alignment
  • real-world cost data

Reimbursement analysis reports do not guide you through these processes.

DRG Uplift Applications: The Only Way to Permanently Change a Tariff

To permanently change a DRG, you must:

Germany (InEK)

  • Submit DRG proposal with cost evidence
  • Validate OPS code usage
  • Provide multi-centre data
  • Request grouper logic changes

France (ATIH)

  • Submit variations via ENCC evidence
  • Work through CCAM committees
  • Align with clinical societies (HAS / CNAM)

UK (NHS England)

  • Provide evidence for HRG redesign
  • Demonstrate national cost variation
  • Submit IIT (Innovation and Technology Tariff) data where applicable

This process is:

  • complex
  • highly technical
  • time-consuming
  • dependent on clinicians
  • impossible to navigate with a simple “reimbursement analysis”

This is where Odelle provides the real value.

Classification + Costing + Coding = DRG Success

DRG success is not an analysis.
It is a synthesis of:

  • coding accuracy
  • grouper logic
  • cost-weight modelling
  • innovation payment utilization
  • national submissions
  • clinical society involvement
  • real-world micro costing

This is the real machinery behind adoption — and the part most MedTech companies never see.

Reimbursement analysis describes the system.
DRG engineering changes it.
Only the latter leads to meaningful hospital uptake.

The Hidden Pathways to Funding:

Add-Ons, Supplements, and Innovation Payments**

Most MedTech companies believe that reimbursement begins and ends with a DRG.
But every major European health system has hidden funding pathways that exist outside the DRG — and these often determine whether hospitals can adopt new technologies in the first place.

These mechanisms were created precisely because DRGs lag behind innovation.
They exist to bridge the gap between product launch and permanent tariff inclusion.

Below is the complete landscape of hidden funding routes that reimbursement analyses usually ignore — and that Odelle uses to shape real DRG success.

Add-On Payments: The Fastest Route to Early MedTech Funding

Add-ons are supplemental payments layered on top of the DRG to cover:

  • expensive consumables
  • implants
  • disposables
  • imaging agents
  • robotics tools
  • devices with high marginal cost

✔ Why they matter

Without add-ons, hospitals lose money every time they use a new device.
With add-ons, they can adopt immediately — before DRG recalibration.

✔ How add-ons work

Add-ons are typically negotiated or authorised at national or hospital level:

  • Germany: NUB-based Zusatzentgelt (ZE)
  • France: RIHP/RIHN supplements
  • UK: High-Cost Device uplift and HRG carve-outs
  • Nordics: NordDRG Special Reimbursement modules

✔ The strategic point

A reimbursement report simply lists tariffs.
It rarely tells you which add-ons exist,
which are misused, or which can be created.

Odelle identifies and builds the missing supplemental pathway.

Innovation Payments: Temporary Funding Until DRG Inclusion

Innovation payments are temporary national mechanisms for breakthrough technologies.

Germany NUB (Neue Untersuchungs- und Behandlungsmethoden)

  • Annual hospital-led submission
  • Negotiated with payers
  • Meant to cover new clinical methods and devices
  • Can be renewed repeatedly

France RIHN / RIHP

  • Innovation financing outside the GHS DRG
  • Covers expensive or non-codified technologies
  • Used to generate cost evidence for ENCC updates

UK Transitional payments / High Cost Device List

  • Used for technologies too new for HRG inclusion
  • Supports early national adoption

These pathways are essential because DRGs update slowly, whereas technology moves fast.

A reimbursement analysis does not tell you how to secure innovation payments.
DRG engineering does.

Supplemental Tariffs: When DRGs Cannot Capture Cost Variability

In procedures with:

  • extremely diverse patient profiles
  • unpredictable resource use
  • expensive implants
  • or variable operating time

DRGs often undervalue true cost.

To compensate, national systems offer supplemental tariffs:

  • procedure-specific surcharges
  • special case-mix adjustments
  • device or technique-based top-ups
  • ICU or risk-category multipliers

✔ Example

A minimally invasive device may shorten LOS (length of stay) but increase OR time and implant costs.
The DRG weight will not capture this until recalibration.

A supplemental tariff is the interim mechanism that keeps hospitals financially neutral.

✔ Why it matters

Most reimbursement reports fail to identify which supplemental tariffs apply or could apply.
Yet these are crucial for first-wave adoption.

Cost Outlier Payments: When Resource Use Exceeds DRG Assumptions

All DRG systems include outlier rules:

  • upper trim points
  • extreme LOS categories
  • high-cost outlier protection

These can unlock unexpected funding for:

  • complex postoperative care
  • prolonged ICU stays
  • complications
  • high comorbidity burdens

✔ Why most companies miss this

Reimbursement reports almost never map outlier rules to your technology.
But outlier logic can significantly improve hospital economics for new devices.

This is a core element of DRG engineering.

Temporary Coding Pathways: Rapid Access Before Official Codes Exist

Keywords: temporary coding, unlisted procedure code, local coding pathway, interim DRG assignment

Before a new CCAM, OPS, NCSP, or OPCS code is created, hospitals can use:

  • temporary procedure codes
  • unlisted codes
  • modifier codes
  • local coding guidance

This allows hospitals to adopt new technology months or years before national coding committees publish official updates.

✔ Why this matters

A reimbursement report only lists what exists today.
It does not tell you how hospitals can code your device tomorrow.

This is another area where Odelle guides hospitals in real practice, not theoretical analysis.

The Strategic Value of These Hidden Pathways

Add-ons, supplements, innovation payments, and outlier rules are not “bonus” options.
They are the core toolset for:

  • early MedTech adoption
  • stabilising hospital economics
  • proving real-world cost signatures
  • preparing annual DRG uplift applications
  • creating the data needed for permanent tariff change

Reimbursement analysis does not explain these pathways.

DRG strategy builds them.

This is the difference between a report
and a funding pathway that actually works in hospitals.

Why Clinicians Don’t Have Time to Build DRGs (and Why MedTech Must Support Them)

Most reimbursement consultants assume that clinicians, surgeons, and hospital coders will “drive the DRG change.”
In reality, doctors have neither the time nor the structural support to influence DRG construction — even when a technology clearly improves care.

This gap is one of the biggest barriers to MedTech adoption in Europe.
Understanding it is essential for building a real reimbursement strategy.

1. Clinicians Are Not Paid for DRG Work

Across Europe, DRG-related activities are unpaid work:

  • writing new procedure notes
  • completing innovation forms (e.g., NUB, RIHN)
  • gathering cost data
  • completing coding validation
  • writing clinical society endorsements
  • coordinating multi-centre data
  • filling ENCC or reference-cost templates

Surgeons are paid for clinical activity, not reimbursement development.

Result:

DRG applications consistently fail or never start due to lack of clinician bandwidth.

2. DRG Applications Require Technical Knowledge Clinicians Don’t Have

Keywords: DRG application, DRG complexity, reimbursement expertise, health economics, coding rules

Building a DRG or submitting an innovation payment requires:

  • understanding cost-weight models
  • interpreting DRG grouping logic
  • knowledge of ICU multipliers
  • cost allocation methods
  • LOS/outlier rules
  • coding hierarchy (main procedure vs secondary)
  • committee submission processes

No clinician is trained in:

  • InEK cost modelling
  • ATIH ENCC cost matrices
  • HRG4+ logic
  • NordDRG algorithmic mapping

A reimbursement analysis does not fix this.
Only DRG engineering bridges the gap.

3. Coding Teams Are Understaffed and Overloaded

Keywords: hospital coding, coding errors, DRG accuracy, MedTech coding issues

Hospital coding departments are:

  • short-staffed
  • under pressure
  • not trained in new procedures
  • working with incomplete surgeon notes
  • judged on speed, not accuracy

Coding is the single biggest determinant of DRG assignment.
Yet coding teams:

  • rarely receive device training
  • do not know clinical nuances
  • cannot chase surgeons for details
  • rely on shortcuts and copy-paste logic

This creates systemic miscoding that blocks:

  • correct DRG placement
  • proper tariff expression
  • innovation payments
  • high-cost add-ons

A reimbursement analysis does not reveal these real-world coding behaviours.

4. Surgeons Lack Real-World Economic Data

Keywords: micro costing, cost data, hospital economics, OR time, ICU resource use

To change a DRG, clinicians must show:

  • OR time differentials
  • inpatient resource consumption
  • ICU hours
  • implant/consumable costs
  • complication cost profiles
  • LOS curves
  • multi-centre micro costing

But surgeons:

  • don’t have access to cost data
  • don’t collect resource logs
  • don’t know national costing rules
  • don’t have economic modelling tools

This is why clinicians say:
“We know this device is better — but we don’t know how to get it paid for.”

5. DRG Applications Are Administrative Burdens, Not Clinical Tasks

Keywords: DRG paperwork, national DRG submission, clinician workload

DRG submissions often require:

  • 40–120 pages of evidence
  • cost-weight modelling
  • coding justification
  • surgeon statements
  • hospital CFO approval
  • payer negotiation
  • annual resubmission

Clinicians simply cannot carry this administrative burden.

This is exactly why technologies with strong clinical value still fail to achieve reimbursement.

6. Why MedTech Companies MUST Support DRG Work

Keywords: MedTech reimbursement strategy, DRG support, hospital funding pathway, adoption barriers

MedTech companies cannot rely on hospitals to do DRG work.
If they do, adoption will stall for years.

MedTech must provide:

  • coding pathways
  • surgeon note templates
  • micro costing tools
  • national evidence packs
  • innovation payment submissions
  • DRG uplift applications
  • real-world utilisation data
  • guidance for multi-centre data collection

Because DRGs do not change themselves.

And clinicians cannot change them alone.

This is why “reimbursement analysis” is insufficient — and why MedTech companies require a real DRG strategy, not just a report .

Why clinicians struggle to build DRGs

Clinicians struggle to build DRGs because they lack:

  • time
  • reimbursement training
  • economic modelling expertise
  • coding accuracy
  • administrative support
  • access to cost data

The Evidence Required for DRG Change:

Micro costing, Multi-Centre Data, and Real-World Economics

Changing a DRG, securing an add-on payment, or obtaining a national tariff uplift is not a question of producing more clinical evidence. It is a question of producing the right kind of evidence — economic, resource-based, methodologically robust, and aligned with the statistical logic used by national DRG authorities to model cost weights.

Reimbursement analyses provided by most consultants summarise the current funding landscape, but they fail to provide the evidence base required to change it. DRG authorities such as InEK (Germany), ATIH (France), NHS England (UK), TLV (Sweden), and AOTMiT (Poland) operate within health-economic frameworks that assign tariffs based not on innovation, clinical superiority, or societal value, but on the observed distribution of hospital resource use collected through highly structured national cost studies.

To influence this system, the MedTech company must provide evidence that mirrors, complements, and challenges the statistical logic used by national DRG bodies. This is where true health-economics work begins.

1. Microcosting: The Foundation of DRG Evidence

Micro costing is the gold standard in hospital economic evaluation when attempting to demonstrate that a new technology alters resource consumption in a way not captured by existing DRGs. Unlike macro-level costing, which allocates resource use across broader aggregates, micro costing quantifies the precise, discrete resource inputs consumed in the delivery of a procedure.

This includes:

  • operating room minutes
  • consumables and single-use devices
  • implant and instrumentation cost lines
  • staffing ratios (surgeons, scrub nurses, anaesthetists)
  • ICU hours and step-down pathway usage
  • laboratory tests and imaging utilisation
  • recovery time and ward nursing intensity
  • complication-related interventions
  • unplanned readmissions and emergency care

Such detail is important because DRG systems are blind to innovation until resource changes become visible in national cost datasets, which often lag behind clinical practice by 18–36 months. If your device reduces length of stay but increases operative time, or increases perioperative costs but prevents downstream complications, the DRG model will not automatically detect this change.

Only formal micro costing, aligned with the methodological requirements of InEK, ENCC, or NHS Reference Cost collection, can demonstrate these invisible resource signatures.

2. Multi-Centre Cost Data: Why Single-Site Evidence Is Ignored

National DRG systems rely on representative sampling of hospital cost data. Because DRG weights are calculated using a statistical “mean resource use” model across hundreds of hospitals, evidence from a single centre is considered methodologically unreliable.

For this reason, DRG bodies require:

  • multi-centre datasets across 3–10 hospitals
  • consistent case selection criteria
  • identical coding structures
  • harmonised resource logs
  • LOS distributions rather than averages
  • validated primary cost-centre assignments
  • multi-site variance measures (SD, IQR, 95% CI)

This is not optional.
A DRG uplift submission built on single-centre evidence is almost always rejected because:

  1. It does not reflect national practice.
  2. It cannot be generalised statistically.
  3. It cannot be benchmarked against reference hospitals.

Only multi-centre micro costing provides the cross-sectional robustness required to challenge the existing DRG cost-weight model.

3. Resource-Use Differentials: The Key Variable DRG Bodies Care About

DRG authorities do not evaluate technologies based on:

  • QALYs
  • HRQoL improvements
  • surrogate outcomes
  • diagnostic accuracy
  • clinical endpoints

Those are HTA criteria, not DRG criteria.

Instead, DRG systems care about a single overarching question:

Does this technology change resource use in a measurable, reproducible, statistically significant way?

This includes:

  • incremental OR minutes
  • change in anaesthesia time
  • reduction in ICU utilisation
  • decrease in LOS
  • increased consumable cost
  • reduced complication costs
  • change in follow-up pathway
  • impact on readmissions
  • impact on case-mix distribution

If a technology produces measurable resource-use differentials, these can justify:

  • new DRGs
  • DRG split rules
  • supplemental payments
  • innovation payments
  • uplifted cost weights

But none of this is captured in a simple reimbursement analysis.

4. Cost-Weight Modelling: Speaking the Language of National DRG Institutes

DRG systems model the relative cost of each case through cost weights (or relative value units).
Thus, impact on tariff = impact on cost weight × national base rate.

To influence this model, MedTech companies must generate:

  • weighted mean cost differentials
  • resource-use variance measures
  • multivariate regression demonstrating independent cost impact
  • cost ratios vs comparators
  • scenario models of DRG split thresholds
  • LOS curve changes with and without the technology
  • ICU and OR utilisation distributions
  • stratified analyses (age, risk categories, comorbidities)

These are the parameters that InEK, ATIH, and NHS England directly integrate into their tariff-setting methodology.

A reimbursement report does not provide these.
But DRG engineering does.

Economic Rationales: Linking Micro-Evidence to Macro-Level DRG Reform

Once micro costing and multi-centre data reveal a resource signature, this evidence must be translated into macro-level health-economic rationales, including:

  • predictable cost patterns across risk groups
  • clustering of resource-heavy outliers
  • classification misalignment
  • inefficiencies in current DRG mapping
  • inequitable reimbursement relative to true cost
  • misallocation of high-cost devices within old DRGs
  • distortions in average LOS assumptions
  • incoherence between cost-weight and clinical complexity

These rationales form the intellectual foundation for:

  • DRG uplift proposals
  • new DRG creation
  • DRG split logic revision
  • add-on and supplemental tariff creation
  • temporary innovation payments
  • coding rule updates
  • inclusion in high-cost lists

This level of economic reasoning is why hospitals cannot do this alone — and why MedTech companies need partners who understand DRG system behaviour at a structural level.

The Missing Link: Real-World Evidence (RWE) as a DRG Input

Although DRG systems are not HTAs, they increasingly rely on RWE to verify:

  • treatment patterns
  • adoption curves
  • case-mix evolution
  • complication rate differentials
  • patient flow dynamics
  • comparative pathway economics

This includes:

  • EHR-based extraction
  • registry data
  • hospital administrative data
  • procedure-specific datasets
  • prospective RWE pilots

RWE allows national DRG bodies to validate whether the technology’s resource signature is consistent and scalable, which is essential for sustainable tariff reform.

Why This Level of Evidence Matters

A reimbursement analysis is simply a report.
But DRG evidence is a scientific, economic, and statistical package powerful enough to alter:

  • national tariffs
  • DRG cost weights
  • grouping rules
  • classification logic
  • innovation payment eligibility
  • long-term funding pathways

This is the difference between knowing the system
and changing the system.

The Economic Stakes: What Happens When DRGs Are Wrong

A misaligned DRG is not an administrative inconvenience.
It is an economic distortion that cascades across the entire health system — reducing adoption, mispricing clinical activity, penalising surgeons, and driving health systems into structural inefficiency.

Most reimbursement analyses underestimate how profoundly a misclassified DRG alters:

  • hospital behaviour,
  • clinical decision-making,
  • technology uptake,
  • purchasing patterns, and
  • national health expenditure.

MedTech companies must understand these dynamics because the economic consequences of misaligned DRGs are often larger than the clinical consequences.

Hospitals Lose Money Every Time the Technology Is Used

When a DRG undervalues resource use, hospitals experience a negative contribution margin for every case using the innovation.

This occurs because:

  • OR time is mispriced
  • implant costs are not captured
  • ICU utilisation is not acknowledged
  • postoperative pathway complexity is undervalued
  • consumables are not recognised in the cost model

When hospitals lose money, behaviour changes:

  • clinicians stop using the device
  • administrators block procurement
  • purchasing groups prioritise lower-cost alternatives
  • trials or pilots collapse due to “financial unsustainability”

Economic truth:

A device with clinical superiority can still die financially if the DRG is wrong.

DRG Distortions Create Artificial Clinical Patterns

Hospitals respond to financial signals — not academic reports.

A misaligned DRG may lead hospitals to:

  • favour outdated techniques reimbursed more favourably
  • avoid time-intensive but superior procedures
  • cluster cases to minimise financial loss
  • shift patients to outpatient settings even when unsafe
  • reduce follow-up visits to limit unreimbursed labor
  • deprioritise adoption of safer or minimally invasive technologies

Thus, DRG misalignment creates clinical distortions that have downstream effects on safety and outcomes.

Payers Misallocate Budgets When DRGs Fail to Reflect True Costs

Incorrect DRGs create macroeconomic inefficiencies at payer level:

  • underfunding of high-value interventions
  • overpayment of low-value or outdated procedures
  • inaccurate budget planning
  • cost volatility across regions
  • unpredictable hospital deficits
  • delayed adoption of cost-saving technologies

In Germany, France, the UK, and the Nordics, evidence shows that misaligned DRGs lead to system-wide budget distortion, because the national tariff becomes a poor proxy for actual clinical cost.

MedTech Companies Face Artificial Barriers to Adoption

Keywords: commercial risk, market access failure, reimbursement barrier, pricing pressure**

Incorrect DRGs create artificial commercial barriers:

  • pricing negotiations fail
  • distributor uptake slows
  • procurement committees deem the device “unaffordable”
  • surgeons lose enthusiasm due to administrative resistance
  • value propositions collapse because they rely on clinical evidence, not economic compatibility

This is why MedTech companies often assume the technology “failed,”
when in reality, the DRG failed, not the product.

Payers and Governments Lose Potential Long-Term Cost Savings

Keywords: long-term savings, cost-effectiveness, downstream costs, preventable complications, system inefficiency**

Many technologies reduce long-term costs by:

  • preventing complications
  • reducing LOS
  • avoiding readmissions
  • reducing downstream imaging or lab testing
  • preventing surgical revision
  • shifting care toward minimally invasive approaches

When DRGs are wrong, these benefits are not reflected in the tariff, and thus:

  • hospitals avoid using the technology
  • the system continues incurring preventable downstream costs
  • HTA bodies fail to see real-world savings because uptake remains low
  • RWE cannot be generated due to lack of volume

DRG misalignment therefore causes chronic under-adoption of cost-saving innovations.

Health Systems Cannot Recalibrate Tariffs Without Correct Input Data

National tariff systems rely on:

  • ENCC cost data (France)
  • InEK microcosting (Germany)
  • NHS Reference Costs (UK)
  • NordDRG cost matrices (Nordics)

When:

  • coding is wrong
  • technology is underused
  • costs are not captured
  • microcosting is absent
  • innovation payments are not recorded correctly

the national cost model produces incorrect cost weights.

This creates a self-reinforcing cycle:

  1. DRG is wrong →
  2. Hospitals avoid the technology →
  3. Cost data is missing →
  4. DRG stays wrong →
  5. Technology never gets adopted

Only DRG engineering can break this cycle.

Procurement Systems Make Decisions Based on DRG Economics, Not Clinical Value

Hospitals and regional procurement hubs (e.g., NHS Supply Chain, GPOs, Amgros in the Nordics) base purchasing decisions on:

  • contribution margin
  • cost neutrality
  • DRG alignment
  • risk-adjusted operating cost
  • budget sustainability

If a DRG is undervalued, procurement sees the device as a cost liability, regardless of clinical benefits.

This is how DRG error becomes a market access barrier.

Incorrect DRGs Create Inequities in Access Across Regions and Hospitals

Incorrect DRGs contribute to:

  • regional inequality
  • patient access differences
  • inconsistent adoption across hospital types
  • variation in care standards
  • postcode-dependent access to innovation

For example:

  • In Germany, base-rate variance amplifies DRG distortions.
  • In France, hospital group (public vs private) affects tariff viability.
  • In the UK, ICS-level decisions override national HRG tariffs.

This produces unpredictable access not dependent on evidence,
But on the reimbursement structure.

A reimbursement analysis may tell you what the DRG is.
But only economic modelling, microcosting, coding accuracy, and DRG engineering can tell you:

  • whether the DRG is wrong,
  • how much money hospitals lose,
  • what evidence is missing, and
  • how to change it.

This is why MedTech companies need strategy, not reports,
and why DRG accuracy is the difference between national adoption and commercial failure.

How MedTech Companies Can Act Early:

A Practical Blueprint for DRG Success**

If there is a single rule in European reimbursement, it is this:

DRG success is not something you receive.

DRG success is something you build.**

Hospitals will not do it for you.
Clinicians do not have time.
Payers will not volunteer.
Consultants who only “analyse reimbursement” cannot help.

And DRG authorities will not fix a tariff that nobody proves is broken.

To survive in Europe’s DRG systems, MedTech companies must act early — often years before commercial launch — and engineer the funding pathway with the same precision they apply to clinical development.

Here is the blueprint the best companies follow.

Begin DRG Strategy Before Clinical Launch:

The “Early Evidence Window”

In the months before your first study completes, cost data is still invisible to DRG authorities.
This is the moment to build the foundation for tariff reform:

  • gather baseline OR time and ICU utilisation
  • quantify resource-use signatures
  • identify coding gaps (OPS, CCAM, NCSP, OPCS-4)
  • document how the new technique differs from the existing pathway
  • pre-align surgeon notes with future coding logic
  • determine which DRG the device should be grouped into

This early window is where MedTech companies can shape their reimbursement destiny — before the DRG is “set in stone” by cost model inertia.

If you wait for launch to start DRG work, you are already two years too late.

Build DRG Scenarios, Not “Reimbursement Maps”

What you actually need are DRG scenarios:

  • Scenario A: Assigned to current DRG → financially unviable
  • Scenario B: Assigned to adjacent DRG → breakeven
  • Scenario C: New DRG or split → cost alignment
  • Scenario D: Add-on payment → immediate adoption
  • Scenario E: Innovation payment → bridge to permanent tariff

These scenarios determine:

  • launch pricing
  • hospital economics
  • territory segmentation
  • sales messaging
  • procurement strategy
  • clinical evidence needs

Reimbursement reports describe the past.
DRG scenarios engineer the future.

Engage Surgeons Early — and Give Them the Tools They Lack

Surgeons are the only people national DRG bodies will listen to — but they cannot perform DRG work alone.
They need:

  • operative note templates designed for correct coding
  • checklists for resource-use documentation
  • structured microcosting logs
  • training for coding teams
  • draft justification letters
  • evidence packs for clinical societies
  • clear instructions for innovation payment applications

Give surgeons what they need, not “guidance they don’t have time to read.”

Journalist insight:

The surgeon may be the hero of adoption —
but you must write the script.

Generate Real Multi-Centre Costing Before You Need It

National DRG bodies do not change tariffs based on isolated anecdotes, even from world-leading surgeons.
They act only when faced with multi-centre evidence demonstrating:

  • reproducible variation in OR time
  • consistent consumable cost increases or decreases
  • predictable changes in complication rates
  • measurable ICU impact
  • LOS curve shifts
  • homogeneous patterns of cost variance

A reimbursement analysis cannot provide this.
Only micro costing can.

This data becomes the statistical lever used to move DRGs.

Use Innovation Payments as a Strategic Bridge — Not a Shortcut

Innovation payments (NUB in Germany, RIHN/RIHP in France, High-Cost Uplifts in the UK, NordDRG special modules) are not decorative.
They are strategic scaffolding that sustains early adoption while the long process of DRG recalibration unfolds.

A mature reimbursement strategy:

  • applies for innovation payments
  • trains hospitals to request them correctly
  • quantifies the payment gaps
  • aggregates real-world evidence from early sites
  • uses those data to argue for a permanent DRG change

Innovation payments are not the endpoint.
They are the runway.

Use Coding as a Weapon — Not an Annotation

Coding is the hinge on which DRG economics turns.

  • If coding is wrong, the DRG is wrong.
  • If the DRG is wrong, the tariff is wrong.
  • If the tariff is wrong, adoption collapses.
  • If adoption collapses, RWE collapses.
  • If RWE collapses, HTA fails.

Coding is where you win or lose.

This is where Odelle excels — teaching hospitals:

  • which CCAM/OPS/NCSP/OPCS codes to use
  • how to structure operative notes
  • how to avoid miscoding that destroys revenue
  • how to map resources to the correct cost centres

MedTech companies who master coding win reimbursement wars before they begin.

Build the Dossier the DRG Authorities Will Actually Read

Unlike HTA submissions, DRG authorities:

  • ignore long clinical dossiers
  • ignore general cost-effectiveness claims
  • ignore QALYs
  • ignore global evidence
  • ignore meta-analyses

What they do respond to:

  • cost curves
  • LOS distributions
  • OR minute differentials
  • ICU hours
  • consumable cost variance
  • coding frequency
  • DRG misalignment diagrams
  • microcosting charts
  • variance analyses
  • resource-use models

A reimbursement report contains none of these.
A DRG uplift dossier contains all of them.

Prepare for National Submission Cycles — and Never Miss a Window

Every DRG authority operates on fixed annual cycles:

  • Germany (InEK) → DRG change proposals due April–May
  • France (ATIH) → CCAM updates in Q4, ENCC cycles annually
  • UK (NHS England) → tariff consultations late Q3
  • NordDRG → rule change windows Q1–Q2

Missing a submission cycle delays adoption by one full year.

Odelle’s role is to:

  • prepare the dossier
  • coordinate surgeon society signatures
  • align coding departments
  • generate evidence
  • ensure nothing is missed

In DRG systems, a missed deadline is a missed year of revenue.

Treat DRG Engineering as a Product Line, Not a Task

The most successful MedTech companies treat DRG development as:

  • a workstream,
  • a budget line,
  • a strategic product pillar,
  • and a core commercial operation,

not a “regulatory accessory.”

This is what separates:

Technologies that scale
from
Technologies that disappear.

DRG success is not won by chance, not gifted by payers, and not delivered by wafer-thin reimbursement reports.
It is engineered deliberately, systematically, scientifically through coding optimisation, micro costing, economic modelling, surgeon alignment, innovation payments, and national tariff submissions.

Conclusion

The Future of MedTech Reimbursement EU HTA, AI-Driven DRGs, and the Next Five Years**

The future of MedTech reimbursement in Europe will not be shaped by static tariff tables, annual DRG updates, or traditional HTA pathways alone.
It will be shaped by a profound realignment of clinical evidence, economic modelling, digital infrastructure, and algorithmic decision-making across every major health system.

And we are entering a period where analysis is no longer enough.
The next decade will belong to companies who can engineer reimbursement, not merely observe it.

Here is the landscape that is emerging.

EU HTA Will Force Technologies to Prove Both Value and Feasibility

Beginning in 2025–2026, EU-wide HTA collaboration will require:

  • shared clinical assessments for high-risk MedTech
  • harmonised evidence standards
  • cross-border evaluation rules
  • formal RWE integration
  • more transparent cost-effectiveness frameworks

But here is the critical insight:

HTA harmonisation will not harmonise DRGs.

Each country’s tariff system will remain technically independent, driven by:

  • national cost-weight models
  • local coding systems
  • divergent payer priorities
  • variable hospital costing methods

This means companies must prepare one clinical evidence strategy,
but five distinct DRG strategies across EU5.

AI Will Transform DRG Assignment — and Expose Technology Cost Signatures Faster

AI-driven grouping and automated coding audits will soon:

  • detect unusual resource use
  • flag misaligned DRG assignments
  • identify signature patterns in OR time or LOS
  • accelerate detection of underpriced techniques
  • trigger earlier tariff recalibration

In other words:

AI will make invisible economic distortions visible faster than ever before.

This reshapes the opportunity for MedTech:

  • Devices that reduce complications will show earlier economic benefit.
  • Technologies that increase OR time or implant costs will be flagged sooner.
  • Coding anomalies will be corrected automatically, improving DRG accuracy.

MedTech companies who integrate RWE and AI-generated evidence will be far ahead of the market.

Financial Pressures Will Force Payers to Reward True Efficiency

European health systems are under unprecedented pressure:

  • demographic ageing
  • surgical backlogs
  • ICU capacity limits
  • cancer and chronic disease cost growth
  • workforce shortages
  • fiscal tightening

Payers will increasingly reward technologies that:

  • shorten LOS
  • avoid readmissions
  • reduce ICU use
  • compress clinical pathways
  • reduce labour costs
  • replace multi-step workflows

This is where DRG engineering becomes even more important.

Devices with invisible cost savings will be punished.

Devices with visible cost savings will be rewarded.

The difference is evidence — not opinion.

RWE Will Become the Currency of Reimbursement

The next era of reimbursement belongs to real-world evidence, not abstract cost-effectiveness models.
Because RWE provides:

  • real resource use
  • real cost signatures
  • real DRG misalignments
  • real complication profiles
  • real coding behaviour
  • real economic outcomes

RWE directly feeds the cost centres, weighting algorithms, and tariff structures of national DRG systems.

In DRG-based countries:

RWE is reimbursement.

DRG Engineering Will Become a Strategic Imperative, Not a Specialist Niche

Over the next five years, companies that succeed will treat DRG development as:

  • a core competency
  • a commercial function
  • a strategic pillar
  • a board-level priority

Because:

  • HTAs won’t secure payment.
  • Reimbursement analyses won’t fix tariffs.
  • Clinical evidence won’t change coding rules.
  • Hospitals won’t solve DRG errors themselves.

The companies that build DRG success early will dominate their categories.
The companies that don’t will watch clinically superior devices fail in the market.

References

1. Core DRG & Cost-Weight Methodology (General)

These support everything you say about how DRGs are built and cost weights are calculated.

2. Germany – G-DRG, InEK, NUB & Innovation Payments

Use these wherever you mention G-DRG, InEK, cost weights, NUB, ZE, §137h etc.

3. France – GHS/GHM, ENCC, RIHN 2.0 & Liste en sus

These underpin your sections on GHS tariffs, ENCC, RIHN, RIHP, LAHN, Liste en sus.

4. United Kingdom – NHS Payment Scheme, HRG4+, Coding & Tariffs

Anchor for HRG4+, NHS Payment Scheme, reference costs, coding and tariffs.

5. Nordics – NordDRG, TLV, Amgros & Nordic Cost Structures

Supports everything you say about NordDRG, transparency, procurement, and cost-based DRG adjustments.

6. Microcosting, DRGs & Health-Economic Methods

These directly validate your microcosting vs DRG arguments and the need for multi-centre resource-use data.

7. Innovation Payments & Transitional Mechanisms (Germany & France)

For your “hidden pathways” section: NUB, RIHP/RIHN, Liste en sus, etc.

8. EU HTA, RWE & the Future (For Your Conclusion Section)

These underpin the Section 10 “future of EU HTA & RWE” arguments.

FAQ

1. What determines a DRG tariff in European hospital payment systems?

A DRG tariff is determined by the relative cost-weight assigned to each case, derived from large-scale hospital microcosting datasets (e.g., InEK Kalkulation in Germany, ENCC in France, NHS Reference Costs in the UK). Cost-weights reflect actual resource utilisation—such as operating theatre minutes, consumables, ICU hours, and nursing intensity—and are multiplied by a national or regional base rate to calculate the final tariff. The tariff therefore represents a statistically modelled estimate of average resource consumption, not clinical value or innovation.

2. Why do reimbursement analyses often fail to predict real MedTech adoption?

Reimbursement analyses typically describe current DRGs, codes, and tariffs, but they do not assess whether those DRGs are economically accurate or aligned with the real resource-use profile of a new technology. Because DRGs are built on retrospective cost data, they often undervalue innovations until microcosting or multi-centre studies reveal cost differences. As a result, reimbursement reports lack predictive power and fail to address the structural misalignment that determines actual adoption.

3. What evidence is required to justify a DRG tariff change or DRG split?

National DRG authorities require multi-centre microcosting evidence, including:

  • variance in operating time,
  • consumable and implant cost differentials,
  • intensity of postoperative care,
  • complication-related resource use,
  • LOS distribution curves (not averages),
  • regression models showing independent cost impact.
    This evidence must demonstrate that the new technology forms a distinct cost cluster, warranting a new DRG, a DRG split, or an uplift in cost-weight.

4. Why is microcosting superior to DRG-based costing for MedTech evaluations?

DRG-based costing generates top-down aggregate estimates, which mask heterogeneity in resource use. Microcosting employs a bottom-up methodology, capturing discrete resource inputs (staff minutes, device cost lines, ICU hours), allowing precise attribution of cost differences attributable to the innovation. For new technologies, microcosting is the only method that provides granular, statistically defensible data aligned with national tariff-setting frameworks.

5. What role do procedure codes (OPS, CCAM, NCSP, OPCS-4) play in DRG success?

Procedure codes serve as the classification gateway to DRG grouping. Incorrect, incomplete, or legacy coding causes cases to fall into inappropriate DRGs, suppressing cost recognition and leading to tariff underestimation. Coding determines the dominant procedure logic, influences DRG splits, and triggers complexity adjustments. Accurate coding is therefore a scientific prerequisite for DRG reform and must be engineered through surgeon note templates, coder training, and new code submissions.

6. How do innovation payments (e.g., NUB, RIHN, high-cost uplifts) support early MedTech adoption?

Innovation payments are transitional financing mechanisms designed to cover the delta between actual resource use and the underpriced DRG. Examples include Germany’s NUB/ZE payments and France’s RIHN/RIHP pathways. These programs provide temporary reimbursement until microcosting and utilisation data feed into national DRG recalibration cycles. They serve as bridges, enabling early uptake while supporting the evidence base for permanent tariff changes.

7. Why do DRG systems systematically undervalue new clinical technologies?

DRG systems are built on retrospective utilisation and cost data, meaning innovations do not appear in the cost model until hospitals use them at scale. This creates a lag of 1–3 years between clinical introduction and tariff recalibration. Additionally, DRGs reflect average practice, not high-value or minimally invasive techniques, leading to structural underpricing of innovations with atypical resource signatures (e.g., short LOS but higher OR time).

8. What is the economic impact of miscoding on DRG assignment?

Miscoding distorts national cost models by misrepresenting resource use. If an innovation is coded incorrectly, its cost signature is not captured in InEK/ENCC/Reference Cost datasets. This creates artificially low cost-weights, suppresses future tariffs, and leads to persistent under-reimbursement. Systematic miscoding is one of the largest contributors to tariff misalignment, hospital financial loss, and failed MedTech diffusion.

9. How do DRG authorities evaluate whether a new technology deserves a supplemental tariff or add-on payment?

Authorities assess whether the technology generates non-marginal increases in direct cost components (e.g., implants, consumables, OR time) that are not captured in the existing DRG structure. Supplemental payments are approved when resource-use differentials exceed predefined thresholds, or when the technology represents a new clinical input with high cost elasticity. Evidence must demonstrate reproducibility across centres and consistency in resource impact.

10. How will AI and RWE reshape DRG systems over the next decade?

AI-driven coding audits and RWE analytics will dramatically accelerate the detection of resource-use anomalies and DRG misalignments. Machine learning will identify atypical cost clusters, enabling faster creation of DRG splits or supplemental tariffs. RWE from EHRs and registries will provide granular insights into real resource consumption, reducing tariff lag and improving precision in cost-weight modelling. Over time, AI-enhanced DRGs will become more adaptive, dynamic, and evidence-responsive, favouring innovations with clear efficiency gains.

Quick practical tips:

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