From search to CER – How to make your literature review process smarter

This blog post explains why the literature review process is the often underestimated foundation of every clinical evaluation – and how to set it up systematically, transparently, and in an audit-proof manner. You'll learn how to screen publications without compromising quality, where AI support can be used safely within regulatory frameworks, how structured extraction improves quality, which errors regularly lead to deviations, and how to correctly incorporate evidence into the benefit-risk assessment.

Abbreviations

Underlying regulations, standards and guidelines

EU Regulation 2017/745 (MDR)

MEDDEV 2.7/1 Rev. 4 – Clinical evaluation: A guide for manufacturers and notified bodies

1 Introduction

Literature research is one of the most time-consuming and error-prone tasks in a clinical evaluation according to the MDR. And yet, or perhaps precisely because of this, in practice it is often started in the wrong place, insufficiently documented, or not consistently linked to the clinical evaluation report (CER).

Too often it is understood as a preparatory step – as a means to “collect relevant studies.” But under the MDR, it is far more than that.

The literature forms the basis for key elements of clinical evaluation:

• the State of the Art (SotA)
• the derivation of clinical claims and measurable parameters
• the substantiation of clinical claims via measurable parameters
• the benefit-risk analysis
• the identification of clinical data gaps

If the literature review is unsystematic, this has direct consequences:

Clinical evaluation becomes selective rather than comprehensive, descriptive rather than analytical – and in the worst case, vulnerable to regulatory challenges.

Typical weaknesses repeatedly become apparent, especially here:

• missing or unclear search strategy
• non-reproducible search runs
• no agreed Clinical Evaluation Plan (CEP) as a starting point
• unstructured or inconsistent screening

These problems are rarely the result of a lack of literature. They arise from a lack of structure.

This article shows you how to change exactly that – and turn your literature review from a weakness into a solid foundation for your clinical evaluation.

2. The right start: Why the CEP comes before the search

One of the most frequent – ​​and at the same time most consequential – errors in clinical evaluation lies not in the execution of the literature search, but before its actual start: starting without a defined Clinical Evaluation Plan.

In practice, the research often starts operationally – databases are opened, initial search terms are entered, and results come in. Weeks go by. And then it turns out: the search was not aligned with the scope, purpose, or clinical parameters of the CEP.

What seems intuitive is problematic from a regulatory perspective. Because under the MDR, the following applies:

A literature review must be systematic and reproducible. And that is only possible if the strategy is defined in advance.

An MDR-compliant, efficient literature review process therefore follows a clear sequence:

1. Clinical Evaluation Plan (CEP)

→ First define the scope, purpose, target population, clinical claims, and parameters

2. Search strategy

→ Build databases, search terms, inclusion and exclusion criteria based on the CEP

3. Literature review

→ Perform systematically and fully document each step

4. Screening

→ Check title, abstract and full text against predefined criteria

5. Critical evaluation

→ Assess the methodology, quality, and clinical relevance of included studies

6. CER

→ Synthesize evidence for a report

Each step depends on the previous one. If a phase is skipped or completed too quickly, the entire process becomes difficult to defend.

3. High volume, tight deadlines – how to maintain screening quality

You've developed a solid search strategy and run the search across all relevant databases. And now you're faced with 100, 500, sometimes over 1,000 hits.

To put that into perspective: 1,000 abstracts at only 2 minutes per abstract means over 33 hours of work – before the full-text review has even begun.

Under the MDR, this process must be systematic, transparent, and reproducible. There is no way around this requirement. The question is how quality can be guaranteed at high volumes within realistic project timeframes.

Five principles for robust screening

• Screening protocol first: Inclusion and exclusion criteria must be defined and documented before the first match – they must not be adjusted during the screening.
• Proceed in stages: Title screening → Abstract screening → Full-text review. Each phase reduces the volume before the next one begins.
• Structured screening log: Every decision must be traceable – who reviewed the paper, what decision was made, why a paper was excluded.
• Incorporate quality control: A second person reviews a sample of the exclusion decisions – identify inconsistencies early.
• Using AI in a targeted way: AI can provide support at the title and abstract level – more on this in the next section.

The goal is not just speed, but a screening process that is fully transparent with every decision.

4. AI in literature screening – where it really helps and where it doesn't

AI tools are increasingly becoming part of the workflow of clinical evaluation teams. The crucial question is not whether AI should be used, but where it actually adds value and where its limitations lie.

Where AI brings real benefits

• Title screening: AI can identify potentially relevant titles and add short contextual notes – this significantly speeds up the first viewing round.
• Extraction: Structured extraction of study design, population, endpoints, and results.
• Consistency: AI applies the same logic to hundreds of entries, reducing variability between reviewers.

Where AI has clear limits

• Critical assessment: Assessing methodological quality requires clinical judgment, which AI cannot reliably provide.
• Clinical interpretation: Linking evidence to your specific product and target population is expert work.
• Inclusion decisions: Every final decision must be made and accounted for by a qualified evaluator.
• Risk of hallucinations: AI can generate plausible-sounding content that is not in the source. Critical data points must always be verified.

The core requirement remains unchanged: your screening process must be reproducible, transparent, and fully justifiable. MEDDEV 2.7/1 Rev. 4 explicitly addresses this.

Three questions to assess compliance readiness:

• Can your process be reproduced? Could another evaluator follow your methodology and arrive at the same results?
• Is the role of AI explicitly defined? AI as a supporting tool can be defended. AI as a decision-maker cannot.
• Is human supervision documented at every step?

In short: AI can handle the preparation, selection, and scanning – the time-consuming part. Experts make the decision.

5. From abstract to decision – structured extraction as a quality lever

Abstract screening is the phase in which cognitive fatigue sets in most quickly. You read densely written, inconsistently structured paragraphs – each containing the information you need to make an inclusion decision, but rarely in the same format.

The concentration decreases after 50 abstracts. After 150, inconsistency creeps in.

The solution isn't to screen faster. It's to screen smarter.

Structured extraction means extracting the same key elements from every abstract – regardless of how the original is formulated:

• Study design: RCT, observational study, registry, case series?
• Patient population: Who was examined? How many? What was the indication?
• Product or intervention: Is this truly comparable to your product?
• Primary endpoints: What was the study designed for?
• Key findings: What were the results?

When this information is consistently prepared across all abstracts – whether manually, using a template, or extracted with AI support – reviewers work faster, make better decisions, and create a more defensible screening log.

The principle applies regardless of the method: consistency drives quality.

6. Full-text evaluation in the CER – what matters and how it is documented

The full-text review is where the real clinical work takes place. Here, it's no longer just about relevance – but about the more challenging questions:

What is the design of the study?

Are the results valid?

Is this evidence applicable to my product and my patient population?

It is also the phase in which documentation requirements are highest – and in which incomplete or unclear assessments most frequently lead to deviations.

What a complete full-text review of product-relevant literature must cover:

• Study methodology: Is the design suitable for the research question?
• Patient population: How closely does the study population match your target population? Are there any relevant differences that affect generalizability?
• Endpoints and results: Are the endpoints clinically meaningful? Do they align with the safety and clinical performance claims in your CEP?
• Results and statistical validity: Are the results clearly presented? Is the statistical analysis appropriate?
• Limitations: What does the study itself acknowledge? What additional limitations do you, as a reviewer, identify?
• Clinical relevance to your product: What specific contribution does this evidence make to your clinical evaluation?

7. From literature to benefit-risk assessment – ​​how evidence supports the CER

Conducting a literature review and screening publications is necessary, but not sufficient. The crucial question is: What does this evidence specifically mean for your product?

In practice, many CERs lose their coherence precisely at this point. The literature exists within the document, but it does not drive the document forward.

How evidence should be incorporated into clinical evaluation

• From literature to clinical safety: What does the evidence say about known risks of this type of product? What complication rates are reported? What adverse events appear across studies?
• From literature to clinical performance: Which clinical outcomes does the evidence support?
• From literature to state of the art: What does the literature say about the current treatment standard? How does your product position itself in this regard?
• From all this, let's turn to the benefit-risk ratio: Only when clinical safety, clinical performance, clinical benefit and state of the art are clearly established on an evidence-based basis can a meaningful benefit-risk analysis be carried out.

Literature is not a section of the CER. It is the foundation of the entire document.

If your evidence synthesis and your benefit-risk analysis could have been written independently, that is a sign that the connection and relationship need to be strengthened.

8. The three most common errors that lead to deviations

Project experience repeatedly reveals the same weaknesses, which trigger inquiries from notified bodies. Here are the three most common ones – and what to do instead.

Error 1: The search strategy is insufficiently documented

The search was conducted. The results were reviewed. But the strategy itself – the exact search terms, the databases used, the execution date – was not fully recorded.

An auditor from a notified body cannot assess a search they cannot see. And a search that is not reproducible fails the fundamental MDR requirement of transparency.

What we do instead: Every search is documented with complete search terms per database, execution date, and number of hits. The strategy is included as an appendix (literature search plan and literature search log) in the CER – not just referenced.

Error 2: Inclusion and exclusion criteria were applied inconsistently

The criteria existed – but were interpreted differently by reviewers or informally adjusted during the screening process. This problem is difficult to rectify retrospectively because the inconsistency often only becomes apparent when decisions are compared side by side.

What we do instead: Criteria are defined and documented before the screening begins. The evaluation of the publications is documented in the literature search protocol.

Error 3: The link between literature and clinical conclusions is not explicit

The studies are listed. The benefit-risk analysis has been written. But the path from evidence to conclusion is not clearly outlined.

What we do instead: Every clinical conclusion in the CER is explicitly linked to the evidence that supports it. The literature doesn't simply exist in the document—it drives the document.

9. CER Update vs. New CER – How to Scale Search Correctly

Not every literature review needs to be rebuilt from scratch.

New CER – building a complete evidence base

For a newly created CER, the literature search process must be established from scratch. Its search scope should be broad enough to encompass the entire spectrum of available evidence. The time period typically covers at least the last 10 years for the state of the art and the time since the product's market launch. If it is not yet on the market, similar products serve as a guide.

The goal: a complete, defensible evidence base that supports every clinical conclusion in the CER.

CE Update – Close the gap, don't repeat work

A CER update has a different goal: to identify new evidence that has emerged since the last search and to check whether it changes existing conclusions.

• Search from the date of the last search until today
• The same databases and search terms as the original – consistency is crucial for comparability
• Explicitly document that this is an update search, with reference to the original

When an update is not enough

There are situations where an update search is not enough:

• Significant change in purpose or indication
• New security signals that require broader evidence review
• Significant changes to the product design

10. Conclusion

This is something that is repeatedly demonstrated in practice:

The problem is not the availability of literature, but how it is used.

A reliable literature review is characterized not by the quantity of studies found, but by clear principles:

• Strategy before execution – the search is planned, not exploratory
• Structured documentation – from plan to minutes to report
• Clear separation of evidence levels – state-of-the-art and product-specific data fulfill different functions
• Objective study selection – based on defined criteria and transparent screening
• Critical evaluation instead of description – data is weighted, not just summarized
• Clean linkage with the CER – every statement is evidence-based and traceable
• Continuous updating – as an integral component

Ultimately, this very system determines whether your clinical assessment:

• appears defensive or argues convincingly
• is vulnerable or can withstand an audit

Or to put it another way:

A good literature review does not only answer the question of what is known.

It clearly and comprehensibly demonstrates:

Why you arrive at your conclusions – and why they are sound.

11. How we can help you

An MDR-compliant literature review process is complex – and a critical bottleneck in many projects. We support you in setting up this process in a structured, efficient, and audit-proof manner.

Our focus is on:

• Strategy & Planning: clear definition of search strategy and criteria based on your CEP
• Structure & Documentation: Creation of plan, protocol and report – reproducible and MDR-compliant
• Screening & Evaluation: Systematic study selection and thorough critical appraisal
• Linking to your CER: Clear traceability of claims for evidence and identification of data gaps
• Lifecycle & Updates: Building sustainable processes for continuous updates

The aim is a literature review that is not only complete, but also methodologically sound and regulatory compliant.

Want to know more? Contact us for a free initial consultation!

You can get a free initial consultation here: free initial consultation

Literature review for clinical evaluation – Best practices

This blog post will show you how to systematically, transparently, and audit-proof conduct an MDR-compliant literature search. You will learn how to develop a robust search strategy, define clear inclusion and exclusion criteria, avoid typical bias risks in screening, and critically evaluate studies. Furthermore, you will learn how to effectively link literature to your clinical claims, systematically identify data gaps, and keep your research up-to-date through continuous lifecycle management. 

Abbreviations

Underlying regulations, standards and guidelines

EU Regulation 2017/745 (MDR)

MEDDEV 2.7/1 Rev. 4

1 Introduction

 Under the MDR, clinical evaluation is no longer a formal obligation – it is the central evidence for the safety, performance, and clinical benefit of a medical device. The evaluation must be systematic, transparent, and evidence-based (Article 61, Annex XIV).

And this is precisely where quality is often decided, at a point that is still underestimated in practice: literature research.

Too often it is understood as a preparatory step – as a means to "collect relevant studies".
But under the MDR, it is far more than that.

The literature forms the basis for key elements of clinical evaluation:

• the State of the Art (SotA)
• the derivation and safeguarding of clinical claims
• the benefit-risk assessment
• as well as the identification of clinical data gaps

If the literature review is unsystematic, this has direct consequences:
The clinical evaluation becomes selective instead of complete, descriptive instead of analytical – and in the worst case, vulnerable to regulatory challenges.

Typical weaknesses repeatedly emerge:

• missing or unclear search strategy
• non-reproducible searches
• Mixed objectives (e.g., state of the art vs. product-specific evidence)
• unstructured or subjective study selection

These problems are rarely the result of a lack of literature. They arise from a lack of structure. An MDR-compliant literature search therefore does not follow gut feeling, but a clearly defined, documented, and traceable process. It is not retrospective ("What did we find?") but prospectively planned ("What do we need to show – and how do we find the appropriate evidence?").

This article shows you exactly how to implement this – and turn your literature review from a weakness into a solid foundation for your clinical evaluation.

2. Strategy before search: The most common weakness

One of the most frequent – ​​and at the same time most consequential – errors in clinical evaluation lies not in the execution of the literature search, but before its actual beginning: the lack of a clearly defined search strategy.

In practice, research is often started operationally – databases are opened, initial search terms are entered, and the results "develop along the way."
What seems intuitive is problematic from a regulatory perspective.

Because under the MDR (Medical Review Guidelines), a literature review must systematic and reproducible .
And that is only possible if the strategy is defined in advance.

Without this structure, typical weaknesses arise:

• The selection of studies is situational rather than rule-based
• Search terms are not documented
• Relevant studies are overlooked or found by chance
• Traceability in the audit is not given

The consequence: The literature base has a selective effect – even if this was not intended.

A reliable literature review therefore always begins with a clear strategic definition.

Before the first search is performed, at least the following elements should be defined:

• Objective of the research
  (e.g., state of the art vs. product-specific evidence)
• Selected databases
• Inclusion and exclusion criteria
  (clear, structured and predefined)
• Search terms
  (including synonyms, controlled terms and logical connectives)

This preliminary work is not an additional bureaucratic burden – it is a prerequisite for quality.

3. Structure & Reproducibility: From Plan to Report

A literature review is only considered reliable if a third party can understand and, ideally, reproduce identicallyhow the results were obtained.

This is precisely where a crucial weakness becomes apparent in many clinical evaluations:
The research has been carried out – but not documented in a way that would allow it to be verified.

The key to reproducibility lies in a clear structure.
Best practice is to separate the process into three sequential documents, each fulfilling a specific function:

3.1 Literature Search Plan – The strategic foundation

The plan is before the research and defines the methodological framework.

He answers key questions such as:

• What is the goal of the research?
• What questions need to be answered?
• What are the inclusion and exclusion criteria?
• Which databases are used?
• Which search strings are used?
• What time restrictions apply?

3.2 Literature Search Protocol – Documented Implementation

The protocol describes what was actually done.

It contains:

• the specific databases used
• the complete search strings (copy-paste reproducible)
• Search data and time periods
• the number of hits per database

Here, the planned strategy is translated into a comprehensible implementation .

3.3 Literature Search Report – Evaluation and Selection

The report documents how the results were handled.

This includes:

• the screening process (Title/Abstract → Full Text)
• Number of included and excluded studies
• Reasons for exclusions
• Methodology of critical appraisal
• Summary of relevant results
• Identified data gaps

The report makes transparent how data becomes reliable evidence.

This tripartite structure is more than just a formal structure – it creates clarity throughout the entire process:

• Plan = Strategy
• Protocol = Implementation
• Report = Evaluation

When these levels are clearly separated and consistently documented, the result is a literature review that not only appears complete, but is also verifiable, comprehensible, and defensible .

4. State-of-the-art vs. product-specific literature 

One of the most frequent – ​​and at the same time conceptually most critical – errors in clinical evaluation is the mixing of State of the Art (SotA) and product-specific literature.

What seems efficient at first glance ("everything in one research") leads in practice to a vague argumentation – and thus to a weakened evaluation logic.

The reason:

Both types of literature pursue different goals and answer fundamentally different questions.

4.1 State of the Art (SotA) – The Reference Framework

The state-of-the-art literature answers the central question:
What is currently considered the medical and technical standard?

It defines the context in which your product is evaluated and provides the basis for:

• available treatment options or diagnostic options

• established technologies

• Typical complication rates

• expected clinical outcomes

• Benchmark values ​​(measurable parameters) for clinical safety and performance

The SotA establishes the objective frame of reference. Without this framework, an evaluation is not possible – because clinical safety, performance, and benefit can only be assessed in comparison to an established standard.

4.2 Product-specific literature – Proof of performance

Product-specific literature answers a different question:
How does the specific product perform within this reference framework? How effective and safe is it, and what benefits does it provide?

It serves to:

· to substantiate clinical claims

· to characterize the security profile

• to classify performance in comparison to the state of the art

This evidence shows whether the product meets the requirements – or ideally exceeds them.

Why separation is crucial

If the two levels are not clearly separated, typical problems arise:

Benchmarks are unclear or implicit

Comparability becomes more difficult

Arguments appear circular ("the product is good because your own study shows it is")

· The benefit-risk assessment is losing objectivity

5. Bias-free study selection: Criteria & screening

A systematic literature review doesn't end with the search itself –
that's where the real critical work begins. The true quality of your evidence base is determined in a step that is often underestimated: the selection of studies.

This is where – consciously or unconsciously – the greatest influence on the outcome of your clinical assessment arises. And this is precisely where the greatest risk of bias also lies.

5.1 Why clear criteria are crucial

The selection of relevant studies must not be based on individual assessment.
It must be based on predefined, structured criteria.

If these are missing or too vague, the following happens:

Decisions are made situationally

· Similar studies are evaluated differently

The selection process becomes inconsistent and difficult to understand

The risk of selective evidence increases

5.2 Best Practice: Structured Inclusion & Exclusion Criteria

Well-defined criteria are not based on "perceived relevance" but on clear parameters such as:

• Compliance with the intended purpose

• Appropriate indication and target population

• Suitable study design

• Sufficient data quality and transparency

• Relevant endpoints

5.3 The screening process: Step by step to an evidence base

A structured study selection process typically takes place in three stages:

1) Title and abstract screening
→ Initial filtering based on basic criteria

2) Full-text evaluation
→ Detailed suitability assessment

3) Final inclusion decision
→ Based on full evaluation and defined criteria

In each of these phases, studies are excluded – and this must be consistently and transparently documented.

5.4 Where bias typically arises

Even with formally defined criteria, weaknesses often creep in during practice:

Decisions are based on interpretation rather than criteria

Inclusion and exclusion rules are not applied consistently

Reasons for exclusion are not documented

Studies with positive results will be given preferential consideration

5.5 Objectivity is not a coincidence – but the result of structure

A reliable selection of studies is characterized by the fact that it:

· rule-based rather than intuitive

· is applied consistently across all studies

· is fully documented and traceable

6. From Evidence to Statement: Claims, Data Gaps & Lifecycle

Literature research provides data. However, the added regulatory value only arises when this data is transformed into reliable statements.

This is precisely where the maturity of a clinical evaluation becomes apparent:
How consistently is evidence linked to the author's own clinical statements – and how transparently are limitations identified?

6.1 Traceability: When claims become traceable

One of the key requirements under the MDR is the complete traceability between clinical statements and the underlying evidence.

A reliable correlation always follows a clear logic:

Clinical claim/endpoint → measurable parameter → study → outcome → CER conclusion

Specifically, this means:

Every claim must be clearly defined

The underlying measurable parameters must be defined

Relevant studies must be clearly identifiable

Results must be presented transparently

6.2 Typical weaknesses in practice

Many clinical reviews reveal a break precisely at this point:

Claims are broadly formulated, but the evidence is narrow or specific

• Study populations are not suitable for the intended purpose

Positive results are highlighted, contradictory data are downplayed

• A clear link between the claim and the study is lacking

6.3 Identifying and actively using data gaps

A thorough literature review does not always lead to complete evidence.
And that's perfectly fine – as long as it's addressed transparently.

Typical situations:

· small case numbers

· non-comparable populations

• contradictory results

• missing data for specific indications or subgroups

The crucial factor is not whether data gaps exist, but how systematically they are dealt with.

6.4 From gap to measure

Identified gaps should have direct consequences:

• Planning of PMCF activities

• Adaptation or clarification of claims

• Assessment of the need for further clinical data (e.g. studies)

Data gaps are therefore not a deficit, but a steering instrument for clinical strategy.

6.5 Lifecycle Management: Evidence is not a static state

Clinical evidence is constantly evolving, and your literature search must follow this principle.

An MDR-compliant assessment therefore takes into account:

1) Periodic updates (risk-based)

Higher risk → more frequent updates

• Class III and implantable products → annually

2) Event-based updates (trigger-based)

• new PMS signals

Security alerts

• relevant PMCF results

• Changes to intended purpose or claims

A literature review that is only updated "before the audit" is reactive – not compliant.

7. Conclusion

An MDR-compliant literature search is far more than a methodological intermediate step in the CER. It is the foundation on which the entire clinical evaluation is built – and therefore crucial for its quality, reproducibility and regulatory acceptance.

This is something that is repeatedly demonstrated in practice:

The problem is not the availability of literature, but how it is used.

A reliable literature review is therefore not characterized by the number of studies found, but by clear principles:

Strategy before execution – the search is planned, not exploratory

• Structured documentation – from plan to minutes to report

• Clear separation of evidence levels – state-of-the-art and product-specific data fulfill different functions

• Objective study selection – based on defined criteria and transparent screening

• Critical evaluation instead of description – data is weighted, not just summarized

• Clean linking with claims – every statement is evidence-based and traceable

• Actively addressing data gaps – as a starting point for PMCF and further development

• Continuous updating – as an integral part of lifecycle management

Ultimately, this very system determines whether your clinical assessment:

· appears defensive or argues convincingly

· is vulnerable or withstands audit

Or to put it another way:
A good literature review does not only answer the question of what is known.

She clearly and comprehensibly shows
why you arrive at your conclusions – and why these are sound.

8. How we can help you

Conducting a literature search in compliance with the MDR (Medical Device Regulation) is complex – and a critical bottleneck in many projects. We support you in setting up this process in a structured, efficient, and audit-proof manner.

Our focus is on:

• Strategy & Planning:
  Clear definition of search strategy and criteria
• Structure & Documentation:
  Creation of plan, protocol and report – reproducible and MDR-compliant
• Screening & Evaluation:
  Systematic study selection and sound appraisal
• Linking to your CER:
  Clean traceability of claims for evidence and identification of data gaps
• Lifecycle & Updates:
  Building sustainable processes for continuous updates

The aim is a literature review that is not only complete, but also methodologically sound and regulatory compliant.

Want to know more? Contact us for a free initial consultation!

You can get a free initial consultation here: free initial consultation

Clinical evaluation without pitfalls — the practice-oriented checklist for MDR

This blog post will explain the key questions that an MDR-compliant clinical evaluation must answer, from the precise definition of the intended purpose to the design of a robust Clinical Evaluation Plan (CEP), the selection and evaluation of suitable clinical data sources and the choice of the right evaluation route, to the systematic integration of PMS/PMCF data, reproducible literature research, measurable formulation of claims and active lifecycle management of the CER. 

Abbreviations

Underlying regulations, standards and guidelines

EU Regulation 2017/745 (MDR)

MDCG 2020-1

MDCG 2020-5

MDCG 2020-6

MEDDEV Guideline 2.7/1 Revision 4

Draft of ISO/DIS 18969

1 Introduction

Under the MDR, clinical evaluation is not simply an item on a to-do list to be completed and filed away. It is the central, dynamic instrument for ensuring the safety, performance, and clinical benefit of a product throughout its entire life cycle, and therefore a key point of review for notified bodies and regulatory authorities.

This blog post provides you with a manageable structure: a concise overview of the most important review and decision-making areas, linked to practical tips, quick checks, and requirements. Use it as a work guide: review, document, and fill in any gaps.

2. Practical guide to clinical evaluation

2.1 Everything begins with the intended purpose

Every clinical evaluation begins with a precise definition of the intended purpose. This is not just a formal slogan, but the benchmark against which the entire evidence strategy is measured: What clinical data do you need? Which patient group is affected? In what application context (indications, users, setting, duration of use) is the product used? Only when the intended purpose, information requirements (IFU), marketing materials, and clinical evaluation guidelines (CER) speak the same, unambiguous language can claims or endpoints be meaningfully substantiated. A common mistake is an overly broad or inconsistent intended purpose: this leads directly to contradictory data requirements, vague claims/endpoints, and avoidable audit findings. Therefore, first check whether the intended purpose is formulated identically in the CEP, CER, IFU, and other documents, and consistently correct any discrepancies.

2.2 The CEP is the timetable

Clinical evaluation doesn't begin with the Clinical Evaluation Report (CER), but with the Clinical Evaluation Plan (CEP). A robust CEP defines how you intend to demonstrate clinical safety and performance: It includes a description of the product and its intended purpose, precisely formulated claims and measurable endpoints, the planned data sources (in-house studies, literature, PMS/PMCF, equivalence data), the evaluation and analysis methodology, and an update and trigger strategy for CER revisions. Without this guidance, your CER will be reactive, incomplete, and difficult to defend.

2.3 What really counts as a clinical data basis?

“We have clinical data” is not a sufficient statement. The quality, relevance, evidence, and origin of the data are crucial.

Under the MDR, this primarily includes clinical trials with the product itself, systematically evaluated scientific literature on the product, structured PMS and PMCF data, and, only under strict conditions, equivalence data. Crucially, you must clearly document for each data source used why it is suitable for answering your claims.

2.4 Choosing the right route — strategically and with justification

One of the most important strategic decisions is the choice of evidence route: proprietary clinical data, equivalence, or performance/verification-based argumentation according to Article 61(10). Proprietary clinical data often provide the strongest methodological foundation. The equivalence route remains possible but has become considerably more difficult: technical, biological, and clinical similarity must be demonstrated in detail and verifiably; furthermore, you need access to the underlying data. The performance route can be appropriate for less risky, non-invasive products but requires a sound, rational justification for why clinical data are not necessary. Therefore, specify the chosen route in the CEP (Commissioned Evaluation Process).

2.5 State of the Art as a benchmark

The State-of-the-Art (SotA) chapter in the CER must not simply be a compilation of studies. It must function as a benchmark: medical SotA (guidelines, best available therapies, expected clinical outcomes) and technical SotA (comparable technologies and performance standards) must be analyzed separately. From this analysis, you derive quantifiable reference values, complication rates, measurement accuracies, and performance ranges against which your product is positioned. In this way, the SotA analysis becomes the basis for realistic, verifiable claims and simultaneously reveals where evidence gaps exist and which PMCF (Product Life Cycle Criteria) questions should be prioritized.

2.6 Literature review: systematic, reproducible, verifiable

A proper literature search is both plannable and reproducible. A predefined search plan with inclusion and exclusion criteria, databases, search strings, and timeframes is essential. Subsequently, the screening process must be transparently documented in a search log (title/abstract → full text), and the critical evaluation (bias, relevance of endpoints) must be traceable. Without this systematic approach, the literature search is not verifiable.

2.7 Claims: measurable, traceable, linked

Clinical claims are not advertising slogans; they must be objectively measurable. Categorize claims into safety, performance, and benefit claims, and for each claim, provide a claim ID, a precise formulation, and a measurable endpoint. Link claims in a traceability matrix to their corresponding endpoints and supporting evidence. This is the only way to prevent discrepancies between IFU statements, marketing messages, and CER claims—a classic reason for audit findings.

2.8 PMS and PMCF: the engines of the CER lifecycle

PMS and PMCF are two sides of the same evidence loop: PMS continuously collects feedback from the field (vigilance, complaints, trend data), PMCF specifically provides clinical answers to open questions and fills evidence gaps that may have existed at the time of CE marking or that may have arisen over time.

Together they keep the CER “alive”, PMCF data confirm or refine claims, provide reliable incidence rates and drive benefit-risk reassessments; PMS trends show where PMCF is needed at all.

In short: PMS shows what happens; PMCF explains why and how strongly; the CER is where these findings are versioned, justified and translated into action.

2.9 CERR lifecycle: check regularly, update immediately if necessary

The MDR does not require a rigid review schedule for all products, but it does stipulate that CERs must be actively maintained. For Class III and implantable products, an annual update is explicitly required; for other classes, a risk-based approach with regular reviews applies (often every 2–3 years for Class IIa/IIb, and for Class I at least at an appropriate interval, e.g., up to 5 years, or sooner if relevant signals emerge). Crucially, in addition to periodic reviews, clearly defined triggers must exist: PMS trends, changes to the product or product family, or new risks.

2.10 Consistency in documentation: CER, IFU, RMF and marketing in harmony

Inconsistencies between the CER, IFU, Risk Management File, PMS, and marketing are among the most frequent audit findings. Therefore, conduct systematic consistency checks: Verdict and consistency of purpose across all documents; agreement between the risks identified as clinically relevant in the RMF and the risks discussed in the CER; and a clear mapping of all IFU references to data supported by the CER. Marketing claims may only be used if they are linked to an explicit, documented chain of evidence.

2.11 Practical Quick Checks

Before you release a document, you should keep the following points in mind:

✔ Is the intended purpose consistent?

✔ Does a final CEP exist with clear claims and measurable parameters?

✔ Is a reproducible literature search protocol available?

✔ And lastly: Is every revision versioned, justified, and technically approved?

3. Conclusion

A robust, MDR-compliant clinical evaluation is not achieved solely through lengthy reports, but through rigorous planning (CEP), methodological diligence (reproducible literature search, clean data specification), ongoing real-world evidence (PMS/PMCF) and complete traceability.

4. How we can help you

We provide pragmatic support throughout the entire clinical evaluation lifecycle—from strategic planning to audit-ready documentation. Our goal is to design your CER processes to be MDR-compliant, methodologically sound, and practically applicable. We combine regulatory expertise with practical project and study know-how to transform requirements into genuine, defensible evidence.

Specifically, we can support you with, for example:

• Gap analyses of your existing CEP/CER/PMCF documentation, including prioritized action planning.
• Creation and review of CEPs and CERs.
• Methodology decision & study design: We define endpoints, populations, statistical plans and monitoring concepts for clinical trials or PMCF studies.
• Literature review & evidence appraisal: systematic search, quality assessment and integration into the benefit-risk analysis.
• PMCF conception and implementation.

Want to know more? Contact us for a free initial consultation!

You can get a free initial consultation here: free initial consultation

Clinical evaluation without pitfalls — Common errors in CER and how to avoid them

This blog post provides a concise and practical overview of the typical errors in clinical evaluations (CERs) under the MDR that repeatedly lead to audit findings and requests for further information, how these weaken the evidence for claims and the benefit-risk profile — and, most importantly, what concrete measures and priorities you can use to quickly and sustainably eliminate these pitfalls.

Abbreviations

Underlying regulations, standards and guidelines

EU Regulation 2017/745 (MDR)

MDCG 2020-1

MDCG 2020-5

MDCG 2020-6

MEDDEV Guideline 2.7/1 Revision 4

Draft of ISO/DIS 18969

1 Introduction

Errors in clinical evaluations (CERs) are not mere formalities; they have immediate regulatory and operational consequences. Inadequate methodology, lack of traceability, or outdated data regularly lead to audit findings, can trigger follow-up requirements, and, in the case of initial clinical evaluations, delay approval processes. Against the backdrop of the MDR requirements (especially Article 61 and the PMCF/PMS requirements), the clinical evaluation is therefore not just a "document," but a dynamic, evidence-based management tool that must be maintained throughout the entire product lifecycle.

In this article, we systematically summarize the most common errors we encounter in reviews and gap analyses: from unclearly formulated claims/endpoints and measurable parameters and missing CEPs, to unclear literature searches and insufficiently substantiated equivalence claims, to a lack of traceability between CER, IFU, and label, as well as unstructured benefit-risk analyses. For each identified problem, we provide practical countermeasures, concrete steps, template recommendations, and prioritizations for rapid benefit.

2. Error Overview — The Top Pitfalls at a Glance

Here is a concise summary of the most common pitfalls in clinical assessments — each with a brief description and a direct countermeasure, so you know immediately what to do.

This overview helps to set priorities: It is best to start with the points that have the greatest audit risk and the highest impact on claims — typically traceability, claims definition, SoTA (derive measurable parameters!) and PMS integration.

3. Detailed error analysis & countermeasures

Below, we'll go through each top pitfall individually: briefly outlining the problem, explaining why it's critical, providing concrete, immediately actionable countermeasures, and concluding each section with a short checklist of 3-5 items that you can quickly tick off. The recommendations are pragmatic—the goal is audit-proofness, traceability, and practical implementation.

3.1 Treat CER as a one-off document

Problem & Impact: The CER is only created for approval purposes and then "filed away." This results in the loss of new insights from PMS/PMCF or the literature; claims may become outdated, and auditors may criticize the lack of lifecycle processes.
Countermeasures (specific):

• Define a CER lifecycle in the CEP: Review intervals (e.g., annually for Class III/implantable, risk-based for other classes) and ad-hoc triggers (e.g., significant PSUR findings, new guidelines).
• Implement a change log: versioning, date, trigger, responsible party, brief description of the change.

Mini-check:

• Review interval documented? ✔
• Is a trigger list available? ✔
• Change log/versioning available? ✔

3.2. Unclear or missing clinical claims/endpoints

Problem & Impact: Without clear, measurable claims, you cannot gather targeted evidence or meaningfully define endpoints.

Countermeasures (specifically):

• Formulate claims using the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound). Example: "Reduces A-rate by X% within 30 days vs. standard.".
• Assign specific measurable parameters, metrics (numerator/denominator) and acceptance criteria to each claim/endpoint.
• Define the claim formulations in the CEP and maintain a claim/endpoint register file (claim/endpoint ID, formulation, measurable parameter, document list, status).

Mini-check:

• Are all claims/endpoints formulated in the SMART way? ✔
• Does each claim/endpoint have an associated measurable parameter? ✔

3.3 Weak State-of-the-Art (SotA) Analysis

Problem & Impact: If SotA remains merely descriptive, the benchmark for substantiating claims or improvements is lacking.
Countermeasures (specific):

• Derive measurable benchmarks (parameters) from SotA (e.g., mean complication rate, measurement deviation). These benchmarks define your target variables.

Mini-check:

• Benchmarks derived and documented? ✔
• SotA → CER/CEP issues linked? ✔

3.4. Excessive reliance on equivalence

Problem & Effect: Insufficiently documented equivalence leads to requests for further information from notified bodies; clinical data are then unusable.
Countermeasures (specifically):

• Create a complete equivalence dossier with three building blocks: technical equivalence (design, dimensions, functions), biological equivalence (materials, surfaces, contacts), clinical equivalence (intended purpose, indication, population, users).
• Systematically evaluate differences: small/neutral vs. relevant → if relevant, plan your own data (study or PMCF).

Mini-check:

• Is the equivalence dossier (tech/biol/clin) complete? ✔
• Differences assessed & documented? ✔

3.5 Literature search without reproducibility / traceability

Problem & Impact: Missing search protocols and undocumented selection criteria make results unreproducible — auditors demand reproducibility.
Countermeasures (specific):

• Use a written search log (databases, search terms, time period, date of search).
• Use digital tools that make it easier for you to document your literature search.

Mini-check:

• Search log available? ✔

3.6. PMS / PMCF data are not integrated

Problem & Impact: When field data remains isolated, benefit-risk arguments become obsolete.
Countermeasures (specific):

• Define a clear process in SOPs for how PMS and PMCF results are regularly reviewed, evaluated, and fed back into the clinical assessment.
• Include sections for evaluating the PMS and PMCF results in the CER template.
• Establish a fixed sequence: First, evaluate PMS/PMCF → then update the CER so that the new data can be integrated consistently.

Mini-check:

• PSUR/PMS events are documented in the CER. ✔
• PMCF results are used to confirm or adjust clinical statements.✔
• The benefit-risk assessment is regularly updated based on current field data. ✔

3.7. No structured benefit-risk analysis

Problem & Impact: Narrative perspectives are subjective; a lack of data makes decisions difficult.
Countermeasures (specific):

• Define a fixed methodology for benefit-risk assessment, e.g., using structured tables, scoring models, or qualitative categories with clear evaluation criteria.
• Explicitly link benefit and risk parameters to clinical data, including clinical trials, literature, PMS and PMCF results.
• Document assumptions and weightings in a transparent manner so that decisions remain consistent even with updates to the CER.

Mini-check:

• Benefits and risks are clearly defined and presented in a structured comparison. ✔
• Clinical data and PMS/PMCF results are demonstrably included in the assessment. ✔
• The benefit-risk assessment is reproducible and comprehensibly reasoned. ✔

3.8. Inconsistent documents (CER vs. IFU vs. Claims/Endpoints)

Problem & Impact: Discrepancies between IFU/Marketing and CER lead to audit findings.
Countermeasures (specifically):

• Establish a systematic reconciliation process that ensures all clinical claims/endpoints in IFU, marketing materials and technical documentation are supported by the clinical evaluation.
• In the CER, define a “claim reference” that explicitly lists all essential clinical claims and links them to the underlying data.

Mini-check:

• All clinical claims/endpoints in IFU and marketing are documented and substantiated in the CER.✔
• Indication, target population, and purpose are consistent across all documents.✔

3.9. No clear clinical evaluation strategy (route missing)

Problem & Impact: Arbitrary data collection without a goal leads to gaps and unnecessary effort.
Countermeasures (specific):

• Establish the assessment route in the CEP, justify the choice with risk and data situation, and define milestones.

Mini-check:

• Assessment route documented in the CEP? ✔
• Is there a fallback plan? ✔

 4. Conclusion

Under the MDR, clinical evaluation is no longer a static final document, but rather the central, evidence-based management tool for the safety, performance, and clinical benefit of a product throughout its entire lifecycle. Errors in CEP/CER processes—such as unclear claims/endpoints, lack of reproducibility of the literature review, insufficiently substantiated equivalence assumptions, or the failure to integrate PMS/PMCF data—regularly lead to audit findings and requests for further information, weakening the defense of your claims. Many of these deficiencies can be avoided through clear processes, transparent methodology, and consistent documentation.

Key recommendations for action can be summarized thematically:

Planning & Claims/Endpoints: Begin the clinical evaluation with a complete, finalized Clinical Evaluation Plan (CEP). Define claims early and precisely (SMART: specific, measurable, traceable) and link each claim/endpoint to concrete endpoints, data sources, and acceptance criteria. The CEP phase establishes the data route (own studies, equivalence, performance data) and determines if and when a clinical investigation is required. Early involvement of the clinical lead, biostatisticians, regulatory affairs, and quality assurance prevents later plan changes and reduces regulatory risks.

Evidence Building & SoTA: Conduct a systematic, reproducible literature search and document the search protocol, inclusion/exclusion criteria, and screening process (PRISMA style). Structure the state-of-the-art analysis along measurable, clinically relevant parameters and derive benchmarks and gaps from them. Use an extracted dataset as the source of truth (numerator/denominator, follow-up, limitations), not just narrative summaries.

Methodology & Equivalence: Critically assess the quality of each data source (bias, follow-up, endpoint coherence). If you intend to use equivalence data, provide a complete, verifiable equivalence matrix covering technical, biological, and clinical comparison points; document differences and their relevance. If robust equivalence is lacking, plan an alternative evidence route (e.g., prospective cohort, PMCF).

Benefit-Risk & Traceability: Work with a data-driven benefit-risk matrix in which benefits and risks are quantified, weighted, and supported by concrete data sources. Establish a traceability matrix that links claims, endpoints, the associated studies/data, and the relevant CER, IFU, and marketing sections. This is the only way to avoid inconsistencies and quickly answer auditor questions.

PMS/PMCF Integration: PMCF and PMS are not separate "reports" but rather permanent sources of evidence for the CER. Define the workflow "PMS/PMCF → CER Update" in your SOPs. Ensure that PSUR/PMS/PMCF results automatically trigger a CER review task.

5. How we can help you

We provide pragmatic support throughout the entire clinical evaluation lifecycle—from strategic planning to audit-ready documentation. Our goal is to design your CER processes to be MDR-compliant, methodologically sound, and practically applicable. We combine regulatory expertise with practical project and study know-how to transform requirements into genuine, defensible evidence.

Specifically, we can support you with, for example:

• Gap analyses of your existing CEP/CER/PMCF documentation, including prioritized action planning.
• Creation and review of CEPs and CERs.
• Methodology decision & study design: We define endpoints, populations, statistical plans and monitoring concepts for clinical trials or PMCF studies.
• Literature review & evidence appraisal: systematic search, quality assessment and integration into the benefit-risk analysis.
• PMCF conception and implementation.

Want to know more? Contact us for a free initial consultation!

You can get a free initial consultation here: free initial consultation