Schema and structural drift
Fields that change type, disappear or start receiving unexpected values.
obs.value_unit · null rate 2.1% since 06:00
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Catch Data Problems
Catch Data Problems
Before your Models Do
Pulse continuously monitors the data your healthcare products and AI workflows depend on, surfacing quality gaps, broken feeds, clinical inconsistencies, privacy risks and freshness failures before they reach your users or models.
Data healthprod
MonitoringUpdated 2 min ago
Overall health
84/ 100
↓ 4.2 pts this week
Sources monitored6
Sources degrading2
Critical issues1
Seven pillarsScore · 7-day trend
Structural integrity94↑
Privacy and governance100·
Consistency72↓
Clinical validity89·
Freshness76↓
Identity resolution78↓
AI and ML readiness87↓
Active issues3 open
Critical
Lab Partner A · no new lab results since 09:14
Freshness · 4 datasets affectedNeeds attention
Duplicate patient identifiers up 18% this week
Identity resolution · Claims vs EHRNeeds attention
LDL-C disagrees between Lab A and EHR
Consistency · 1,906 patients
What Pulse watches
The signals that tell you data is becoming unreliable.
Healthcare data rarely fails in one obvious way. Pulse watches every source, feed and dataset for the signals that come first.
Freshness failures
Feeds that arrive late, stop arriving or fall outside their delivery window.
lab_a.oru · last 09:14 · expected every 30 min
Clinical inconsistencies
Unmapped codes, unit mismatches, wrong reference ranges and implausible results.
HbA1c 54 % · outside plausible range
Cross-source conflicts
Systems that disagree about the same patient fact, or a history that contradicts itself over time.
LDL-C · Lab 131 mg/dL vs EHR 3.62 mmol/L
Identity anomalies
Duplicate, ambiguous or conflicting patient identity signals.
3 MRNs → 1 person · 2 DOB conflicts
Pipeline reliability
Ingestion failures, delivery cadence and operational errors across pipelines.
wearables_api · 3 failed syncs / 24 h
Privacy and sensitive-data exposure
Health identifiers where they should not be, including free text, and gaps in governance.
notes_export.csv · SSN pattern in free text
AI readiness degradation
Data behind an AI or ML workflow that no longer meets its required conditions.
risk_model.v3 · ferritin coverage 61% (needs 80%)
Seven pillars
Seven pillars of data health, scored continuously.
Pulse continuously scores your healthcare data across seven pillars, from freshness to clinical validity. So your team can see where data is healthy, degrading or at risk, and open any pillar to see the findings behind its score.
Pillar health · All sourcesSelect a pillar to see its findings
Pillar 03Needs attention
Consistency
Do your sources agree with each other, and over time?
72↓ 9 pts / 7 days
Checks against
14Active findings
3High severity
2Source conflicts
8,214Records affected
Top findings
Needs attentionLDL-C disagrees between Lab A and EHR for 1,906 patientsLab A vs EHR
Needs attentionVITD25 in nmol/L from Lab B, ng/mL from Lab ALab A vs Lab B
WatchActive medications differ between EHR and claimsEHR vs Claims
When something breaks
When your data changes, Pulse tells you.
Every issue arrives with what changed, when it started, how severe it is and what it affects, so your team starts from the answer, not a search.
DegradingIdentity resolution
Detected 2h agoPatient identity consistency is degrading
78↓ 11 pts vs last week
12,481Records affected
3Downstream datasets
2AI workflows
Root signals
↑Duplicate patient identifiers increased 18%
↑Cross-source identity mismatch increased, Claims vs EHR
Blast radius
Claims→Patient 360→Risk model→AI copilot
Opened automatically by Pulse
Source failureFreshness
Detected 7h 12m agoLab Partner A · no new lab results since 09:14
7h 42mwithout new results
Deliveries · last 12 hours
05:00Last results 09:14Now 16:56
30 minExpected cadence
15Missed deliveries
2,840Members affected
Affected
4 datasetsHealth score pipelinerisk_model.v3
Opened automatically by Pulse
Alerts reach your team in the observability tools you already run.
Health over time
Your data has a pulse. Watch it change.
Every score is tracked over time, with the events behind each change. You see regressions as they start, and whether a fix actually held.
Overall health · Aug to OctAll sources · daily
84Today
89Last month
↓ 5.6% month over month
Events
1Schema change · claims_v4Aug 21
2Lab feed delayed · Lab BSep 12
3Issue resolved · feed restoredSep 16
4Identity conflicts increasedSep 30
Architecture
Your data stays where it is.
Pulse runs entirely inside your environment. It reads your data where it already lives, and its scores, findings and alerts stay with you. Nothing is sent to Seiba.
Your environmentYour cloud · your keys
FHIRR4 resources
HL7 v2ADT, ORU
LabsPartner feeds
EHRClinical data
ClaimsCSV, 837
NotesPDF, free text
Seiba Pulse agentProfilesValidatesScoresDetects
Stays in your environment
Raw PHI and identifiers
Health scores and findings
Alerts to your own tools
Nothing crosses this boundary · no data is sent to Seiba
Pulse and Diagnosis
Pulse watches continuously. Diagnosis goes deep.
Pulse is the product that keeps running. Seiba Diagnosis is a focused engagement with our team when you need to understand why, and plan the fix.
Seiba Pulse
Know what is happening now.
Continuous health monitoring
Health scores per pillar
Active issue detection
Freshness monitoring
Source and pipeline monitoring
Historical trends
Impact and blast radius
Regression detection
Alerts
Seiba Diagnosis
Understand why it is happening and what to fix.
Deep data assessment
Architecture review
Root-cause analysis
AI use-case readiness
Prioritized findings
Remediation roadmap
Implementation plan
Who it’s for
Built for teams shipping healthcare products with real data.
See where data risk is accumulating before it becomes an engineering or product problem.
Monitor the health of critical sources, pipelines and datasets without checking everything by hand.
Know whether the data behind your models and AI features is ready, and staying ready.
Surface privacy and governance issues before they become incidents.
Get started
Find out what your data is hiding before your models do.
Pulse shows you where your healthcare data is healthy, where it is degrading, and what needs attention.
01A live health score across all seven pillars
02Active issues ranked by severity and impact
03Continuous monitoring that keeps watching after the fix