RWD and Grants | 9/4/2026 | Tsuyoshi Hadano

Japan MHLW Real-World Data Support Program: What Clinics Should Check First

A practical overview of Japan’s MHLW real-world data support program and the governance, quality, and operational checks clinics should prepare.

日本語版を読む →

Japan’s Ministry of Health, Labour and Welfare has published requirements for its real-world data support program. The program is aimed at organizations that already operate registries or medical-information databases and are considering the use of those data for regulatory purposes.

What the official requirements emphasize

The official material focuses on governance, documented operating procedures, data reliability, and the ability to explain how a registry or database is managed. For clinics and healthcare organizations, this means that collecting data is not enough. The process for collecting, reviewing, storing, and providing data must also be clear.

Practical implications for clinics

A clinic considering participation should first define the purpose of the data project, the population to be included, the data fields to be collected, and who is responsible for quality control. Consent, privacy, access control, and retention rules also need to be documented before the project expands.

Data quality comes before scale

A large volume of data does not compensate for unclear provenance or inconsistent collection. It is more useful to begin with a smaller dataset that can be explained and audited than to build a large repository whose quality cannot be verified.

Governance and collaboration

If the project involves universities, manufacturers, or other institutions, the roles of each party should be documented in advance. Data-sharing conditions, responsibility for corrections, and the process for responding to questions should not be left until after collection begins.

Practical takeaway

The value of the program is not simply the possibility of financial support. It is a useful checklist for understanding what a credible real-world data operation requires: purpose, governance, quality, documentation, and accountability.