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EnaResearch
Use case

Run several studies in one organization, with separate teams

In organizations whose departments, clinics and research groups run several studies at once, EnaResearch keeps each study apart with its own team, protocol and approval flow. If the same person joins two studies, the records are linked by a researcher’s decision; each study sees only its own data.

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Approved lifecycle

Draft, pending approval, scheduled, active, closing, closed; the organization owner approves studies.

Per-study access

Researchers work only in studies whose team they are on; permissions are checked on the server.

Confirmed person matching

The person identifier is not stored in clear; candidates are shown masked and linking is the researcher’s decision.

Common challenges

  • Every study runs in a different tool, and nobody can trace who accesses which data.
  • When the same patient joins two studies, records are duplicated or merged wrongly.
  • Changes made after a protocol is approved leave no trace.
  • Nobody knows who downloaded a dataset, or when.

Data kept together

  • Study protocol and lifecycle
  • Study teams: principal and co-investigators
  • The person identifier your organization chooses (as a keyed hash)
  • Participation statuses and pseudonyms
  • Change requests and approvals
  • Chained audit trail

Each study with its own team and approval

The user who creates a study is its principal investigator; co-investigators join the team. The researcher role does not grant access to every study. The organization owner approves studies and is responsible for governance; in studies whose team they are not on, owners do not see participant data.

After approval, date shifts beyond 30 days in total or changes to the data scope need a request with a reason and the owner’s approval. Every status change is written to the study’s timeline and to a chained audit trail.

The same person in several studies

The organization chooses the person identifier: a national ID or health number depending on the country (Turkish ID, NHS number, BSN, AHV/AVS, KVNR) or its own number. The identifier is not stored in clear; a keyed hash and the last 4 characters are kept for matching.

When a new participant is registered, the organization is searched for an existing person; a candidate is shown masked and nothing is merged automatically. Each study sees only its own data, and deleting one participation never touches data in other studies.

Exports and audit

Generating and downloading datasets requires the export permission and membership of the study team. Generation and every download are written to the audit trail; in the output, participants appear only under a pseudonym.

Each organization runs in a separate environment with its own application stack, database and database user. The optional AI assistant is off by default and, if turned on, sends only aggregated context to the provider.

Multi-study research programs

Owner approval, per-study access, person matching and an audit trail.

  1. 01

    Create the study

    Write the protocol and add the team.

  2. 02

    Submit for approval

    The organization owner approves or requests changes.

  3. 03

    Register participants

    Existing people are linked through confirmed matching.

  4. 04

    Work with an audit trail

    Every sensitive action is written to a chained audit trail.

Related pages

Related use cases

Frequently asked questions

Does the organization owner see every study’s data?
No. The organization owner approves studies and is responsible for governance; in studies whose team they are not on, owners do not see participant data.
Can different institutions share one environment?
Each organization runs in a separate environment, and its data is not stored together with other organizations’ data. For a study run jointly by several institutions, we look at the deployment model together in the demo conversation.
What if the same person is registered in two studies?
The system searches for an existing record, first by the person identifier and otherwise by name and date-of-birth similarity. The candidate is shown masked; linking is the researcher’s decision and is audited.

Let's talk about your research data

We'll show what EnaResearch does today, share the roadmap openly and look at your studies and data sources together.

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A separate deployment for every organization · Turkish and English