Q1 2026 · Asia-Pacific
2 healthcare startups based in Asia-Pacific ranked by GitHub engineering acceleration. Filtered from our broader Healthcare sector rankings.
| # | Company | Stage | Geo | Commits (14d) | Change | Contributors | Contrib. Growth | New Repos | Signal |
|---|---|---|---|---|---|---|---|---|---|
| 1 | ohcnetwork Opensource Public Utility | Open Healthcare Network View signal profile → | Growth | APAC | 129 | +90% | 100 | +0% | 8 | Infrastructure buildout |
| 2 | earthians earthians Health Informatics Pvt. Ltd. View signal profile → | Growth | APAC | 6 | -65% | 83 | +0% | 0 | Framework migration |
Sorted by commit velocity change (14-day window, descending). Data last updated Q1 2026. Geography from GitHub org profiles.
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In Q1 2026, ohcnetwork leads healthcare startups in Asia-Pacific with 129 commits over a 14-day window (+90% change) and 100 active contributors. Across all 2 tracked Asia-Pacific-based startups in this sector, the average 14-day commit velocity is 68 commits. The dominant signal pattern is "Infrastructure buildout", which typically indicates significant new infrastructure investment, often preceding a major product milestone.
Asia-Pacific accounts for 2 of the healthcare startups in our tracking dataset for Q1 2026. This geographic view filters the broader sector rankings to help investors focused on Asia-Pacific-based deal flow identify engineering acceleration patterns within their target geography. Regional concentrations often reflect local regulatory environments, talent pools, and investor networks that shape startup trajectories differently from global averages.
We derive startup geography primarily from the GitHub organization profile location field, supplemented by a manually curated enrichment database of known startup headquarters. This means startups without a public GitHub location may appear in our global sector rankings but not in geographic filters. The geographic classification uses broad regions (Asia-Pacific, etc.) rather than city-level granularity to provide meaningful sample sizes for comparison.