RWE and HEOR teams face increasing pressure to produce evidence that holds up beyond internal analysis. As real-world data informs decisions subject to external review, expectations for data quality have changed.
That is why the strongest RFPs go beyond surface metrics and get specific about provenance, stability and reproducibility. What matters more is whether data can support defensible analysis over time.
Use the questions below to pressure test whether a data vendor can support study-grade analysis across the full lifecycle of your work.
1. Where did the data originate and how transparent is its provenance?
Provenance is the starting point for trust. If you cannot clearly explain where the data came from, how it was collected and what changed along the way, you will struggle to defend findings later.
In an RFP, ask for clarity on:
- Original sources and collection context
- How data is transformed, standardized or deduplicated?
- What metadata is available to verify lineage?
You are not looking for a marketing overview. You are looking for a traceable chain of custody that holds up when someone asks, “Where did this come from?”
2. How will you ensure the signal remains stable and reliable during our study?
Even high-quality data can drift. Upstream sources change, coding practices shift and refresh cycles can introduce subtle differences that affect measures and cohorts.
Ask how the provider monitors for drift during an active study, including:
- Ongoing data quality checks tied to specific study signals
- Alerting or reporting when upstream changes affect key fields
- A defined process for resolving anomalies during the study window
3. How will you enable reproducibility and ensure data-source consistency for future replication?
RWE and HEOR teams often need to replicate analyses later, whether for internal validation or external review. That is only possible when the underlying inputs can be held consistent.
Ask what the data provider can lock down for replication, such as:
- The exact data extract or refresh used for the original run
- A consistent method to rebuild the same cohort in the future
- Clear documentation of what changed between versions
If the vendor cannot support replication, you should assume you will be re-explaining and reworking results later.
4. Are variables, code sets and clinical definitions clearly documented, versioned and reproducible?
This is where many RFPs stay too high level. Definitions drive outcomes. If variable logic is unclear, your findings become harder to interpret and harder to defend.
Ask whether the data vendor can deliver:
- Data dictionaries with field-level definitions and limitations
- Code sets with version history and change logs
- A reproducible definition package for key clinical concepts
You want to avoid “tribal knowledge” that lives in a Slack thread or a single analyst’s notebook.
5. Can records be connected across sources and is patient continuity preserved for longitudinal analyses?
Longitudinal work depends on continuity. If patient identity shifts over time, you lose follow-up, distort treatment lines and risk bias in time-to-event measures.
Ask how the data vendor supports longitudinal continuity, including:
- How record connectivity is measured and validated
- How continuity is preserved across updates and refreshes
- How the vendor evaluates false matches and missed matches
The key question is whether adding sources strengthens the patient journey or introduces new uncertainty.
Stronger questions set a higher bar
An RFP does more than select a data vendor. It defines how evidence will be built and how confidently it can be defended over time.
By asking more precise questions about rigor, identity resolution and longitudinal behavior, RWE and HEOR teams put themselves in a better position to generate evidence that holds up when scrutiny increases. In a landscape where real-world evidence carries real consequences, disciplined questions are one of the most effective safeguards available.
Explore how HealthVerity helps RWE and HEOR teams evaluate study-grade data in our 2 part series: part 1, part 2.
