Health Cloud
AI pipelines for
Healthcare & Life Sciences
Accelerate prior authorizations, reach every patient with a care gap, and match eligible patients to clinical trials — all within Health Cloud's compliance boundaries.
Prior Authorization Automation
The Problem
Prior auth requests require clinical staff to gather patient history, match against payer criteria, draft clinical justification letters, and portal-submit to payers. Each request takes 45–90 minutes. Denial rates run 15–25% due to missing documentation. Delays in auth delay care — patients wait.
Why not DIY?
The payer criteria API changes quarterly. FlowMason's config-driven HTTP callout means updating the endpoint or payload is a JSON change, not a redeploy.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| Auth prep time | 60 min | 8 min |
| Denial rate | 20% | 9% |
| Staff capacity | Baseline | +3× requests per day |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Care Gap Outreach
The Problem
Care managers manually identify patients overdue for screenings, preventive care, or chronic condition follow-ups. Lists are stale by the time they're worked. Outreach is generic — the same message regardless of patient history, language preference, or access barrier profile.
Why not DIY?
Running a nightly batch against thousands of patient records, chunking to stay within governor limits, and personalizing each outreach message requires FMScheduler + PipelineRunnerBatch + ForEach — three separate orchestration layers. Each alone is weeks of Apex.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| Patients contacted per week | 200 | 2,000 |
| Outreach relevance | Generic template | Personalized per patient |
| Care gap closure rate | Baseline | +28% |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Patient Matching for Clinical Trials
The Problem
Trial coordinators manually read clinical notes to find eligible patients. Match rate is 2–5%. A coordinator can screen roughly 50 patients per week against 10–15 trials. Most eligible patients are never identified — trials miss enrollment targets and timelines slip.
Why not DIY?
The TryCatch pattern — gracefully handling incomplete clinical records instead of failing the entire pipeline — is critical here. Clinical data is messy. FlowMason makes graceful degradation a first-class JSON stage.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| Screening throughput | 50/week | 500+/week |
| Patient match rate | 2–5% | 8–15% |
| Time to first match | Days | Hours |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Chat with your Healthcare data.
Beyond the pipeline patterns above, FlowMason ships a chat surface admins drop on any Lightning page. Healthcare users type plain-English questions; FlowMason generates SOQL, validates through the 8-gate sanitiser, runs under FLS, returns rows. Read-only by default.
Try asking:
- › "show me patients overdue on medication review"
- › "count claims denied this week by reason"
- › "which trials match this patient's diagnosis?"
FLS-aware. Allowlist-governed. Permset-gated. No data leaves the org.
Ready to build Healthcare pipelines?
Tell us your use case. We'll show you exactly which pipeline pattern fits.
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