HEALTHTECH & HEALTHCARE MVP
Enter post-deploy error counts, workflow failures, integration issues, and support tickets for each release and see a real risk score — connecting releases to the problems they caused.
Releases
For each release, provide hours since deploy, error counts since deploy vs. a baseline, workflow failures, integration issues, support tickets, and whether it touches a critical path like billing, auth, or patient records.
Error delta vs. baseline, workflow failures, integration issues, and support tickets are each weighted and summed, with a freshness multiplier for issues surfacing within 24–72 hours of deploy and a flat addition for critical-path releases — all recalculated live as you edit any field.
Each release gets a 0–100 risk score and a Low/Elevated/High/Critical label, sorted highest-risk first, with the specific signals listed (e.g. "Errors up 42% vs. baseline") so the score is never a black box.
Continue learning: How CI/CD reduces deployment risk for MVP teams · What makes a software partner reliable during incidents
It combines the percentage change in errors since deploy vs. a baseline window, the count of workflow failures and integration issues, related support tickets, and whether the release touches a critical path (billing, auth, patient records), with recently-deployed releases weighted higher since issues surfacing immediately after deploy are more likely release-caused.
No. The scoring formula runs entirely in your browser — nothing is uploaded or stored on a server.
A freshness multiplier (1.3x within 24 hours, 1.1x within 72 hours) amplifies the same error/failure signals, because problems appearing right after a deploy are statistically more likely to be caused by that release than issues that surface days later.
This demo uses a transparent, fixed weighting to show the mechanism clearly. A production Release Guard Health workflow would let teams tune the weights (and pull error/workflow/support data automatically from existing monitoring and ticketing tools) to match their own risk tolerance.
Treat it as a rollback/hotfix signal, not an automatic action — the score surfaces which release most likely caused the surrounding problems so a team can investigate and decide, rather than sifting through error logs and support tickets manually to make that connection.
This demo ships with four representative sample releases to edit live. A production version would connect to your actual deployment history and let you add releases without a fixed limit.
| Feature | MVPHub | Manual correlation across monitoring/ticketing tools | General-purpose APM/error-tracking dashboards |
|---|---|---|---|
| Connects releases to errors, workflow failures, and tickets | Included | Limited | Limited |
| Healthcare critical-path weighting (billing/auth/records) | Included | Not included | Not included |
| Transparent, inspectable risk formula | Included | Not included | Limited |
| Runs instantly with no setup | Included | Not included | Not included |
Manually correlating monitoring and ticketing data works but takes time and misses recency effects; general-purpose APM dashboards surface errors well but do not connect them back to a specific release with a healthcare-aware risk score. MVPHub demonstrates the risk-scoring mechanism directly, with results in seconds.
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