Orthopedic Robotics

Precision is easy to measure,
value is harder to prove

The endpoints that actually sell Orthopedic robotics: A practical guide to building evidence that wins real decisions.

NICE spelled this out clearly: robot assisted surgery often shows better alignment, but evidence does not suggest this reliably results in better PROMs or clinical outcomes. Overall outcomes remain broadly non-inferior.

In NHS PROMs data, only 64.5% of knee replacement and 77.6% of hip replacement patients rated satisfaction as excellent. That is the gap robotics has to move, not just an alignment plot.

The Key Question:

How do you pick endpoints that move decisions, not just publications?

Dr Nikhil Khadabadi, MD, MS, MRCS

Chief Medical Officer

Ex TÜV SÜD Clinical Reviewer MDR PMCF Expert Robotics Advisor

"With over 15 years across orthopedic surgery and notified bodies, I bring frontline insight to every program. At Eclevar MedTech we focus on clinical data that is clear, defendable and ready for review."

Step 1

Know who you are trying to convince

Different stakeholders read endpoints in different languages.

Regulators

They ask: Is it safe? Does it perform as intended? NICE's message is clear: if your promise is patient benefit, you must measure it directly.

Safety events and device-related harms
Performance matching intended purpose
Usability and training evidence

Surgeons

Adoption is tied to what happens after the theatre doors close: complications, early recovery, and system workflow fit.

Consistency and outlier reduction
Complications and reoperation rates
Early recovery (pain and function)

Hospital Leadership

Rollouts fail when the business case is fragile. Procurement asks: Does it slow lists down? What is the total cost of ownership?

OR time broken into meaningful parts
Length of stay and discharge timing
Staffing and training impact

Payers & HTA

They read value through durability and economics. NICE highlights the need for more utility data to make economics "decision-ready".

QALYs and Budget Impact
Durability through registry follow-up
Resource use for economic modeling

Step 2

Choose endpoints that prove the promise

Headline: Pick one value endpoint. Everything else is supporting proof.

1. Primary Value

Forgotten Joint Score at 12 months is a strong anchor because it is patient-centred and decision-makers recognize it.

2. Technical Mechanism

Component position accuracy versus plan (cup inclination, anteversion, tibial slope).

3. Safety Scrutiny

Consistency in capturing AE/SAEs related to new risks like fixation pin issues or workflow errors.

4. Theatre Reality

Measure setup time, skin-to-skin time, and turnaround impact across the learning curve.

5. Registry First

Plan durability layers: short-term reoperations in trials, long-term survival via registry linkage.

6. Software Drift

Define what changes require bridging validation so evidence stays valid as AI/algorithms update.

Step 3

Three traps to avoid

Trap 1: Precision ≠ Value

Alignment is clean but NICE says it doesn't reliably show better PROMs. Lead with value, support with precision.

Trap 2: Ignoring Experts

Don't only study "super-users". Evidence must show it works across full teams on a busy list.

Trap 3: Version Breakage

Software moves fast. Without tracking versions, evidence becomes undefendable. Track every update.

Manufacturer Checklist

Write the promise in plain language (Patient/Surgeon/Hospital benefit).

Choose one primary value endpoint (e.g., PROMs at 12 months).

Add a focused technical performance set (Alignment accuracy).

Pre-specify safety endpoints and reporting rules.

Measure workflow and learning curve explicitly.

Build a registry and real-world pipeline from day one.

Lock a versioning and change control approach.

Need a Robotics Endpoint Map?

I help teams turn a promise into a decision-ready endpoint plan, then deliver it through lean clinical studies plus PMCF. Message me your platform type (knee, hip, spine) for a tailored map.

Contact ECLEVAR MedTech

Precision is easy to measure, value is harder to prove