The Data Problem: Why 50% of Healthcare Outreach Fails Before It Starts
Executive Summary
Healthcare organizations spend billions on patient outreach, yet success rates hover around 5%. The root cause isn't the outreach strategy—it's the data underneath it.
Key findings:
- 50% of outreach targets contain data quality issues that prevent successful contact
- Healthcare organizations maintain an average of 14 separate data systems that rarely sync
- An intelligence layer that reconciles data before outreach can improve success rates by 8-12x
The Hidden Cost of Bad Data
When a health plan sends a medication adherence file to a provider network, something predictable happens: half the phone numbers are wrong, a third of the patients have already filled their prescriptions, and the eligibility data is weeks out of date.
This isn't a failure of any single system. It's the inevitable result of healthcare's fragmented architecture.
Quantifying the Problem
Our analysis of over 2 million patient records across healthcare organizations revealed:
| Data Quality Issue | Frequency |
|---|---|
| Outdated phone numbers | 34% |
| Incorrect patient attribution | 23% |
| Already-resolved care gaps | 31% |
| Deceased or disenrolled patients | 8% |
| Missing contact preferences | 67% |
Common data quality issues in healthcare outreach files
When more than half of your outreach list contains fundamental data problems, no amount of messaging optimization or calling persistence will fix your success rates.
The Financial Impact
Organizations waste significant resources on flawed outreach:
Staff Time
Care coordinators spending 80% of time on phone tag instead of patient care
Opportunity Cost
Missing patients who actually need intervention while chasing bad data
Quality Penalties
Poor performance on measures due to unreachable populations
Trust Erosion
Repeated calls about resolved issues damage patient relationships
Conservative estimates suggest healthcare organizations waste $4.2 billion annually on outreach to unreachable or ineligible patients.
The Root Cause: Fragmentation by Design
Healthcare data fragmentation isn't a bug—it's a feature of how the industry evolved. Each system was designed to solve a specific problem:
Claims Systems
Built for billing accuracy, claims systems optimize for payer adjudication. Patient identity is tied to subscriber relationships. A patient with multiple coverage sources appears as multiple people.
Electronic Health Records
EHRs optimize for clinical documentation and legal defensibility. Patient identity is tied to encounters at specific facilities. The same patient at two health systems appears as two people.
Eligibility Systems
Coverage verification systems optimize for real-time yes/no decisions. They don't maintain historical context or connect to clinical outcomes.
Pharmacy Systems
Dispensing systems optimize for medication fulfillment. They see fills, not prescriptions. They see transactions, not therapeutic relationships.
After decades of integration attempts, healthcare data remains siloed. The traditional answer—build connections between systems—consistently fails because systems use different definitions for the same concepts.
The Intelligence Layer Solution
Rather than trying to integrate systems (which has failed repeatedly), the solution is to add an intelligence layer that reconciles data at the point of use.
How It Works
- 1
Ingest
Data Acceptance
Accept data from any source in any format—Excel files, CSV exports, API feeds, database extracts - 2
Reconcile
Patient Matching
Match patients across sources using multiple identifiers, fuzzy matching, and pattern recognition - 3
Validate
Quality Checks
Check phone numbers, addresses, eligibility status against real-time databases - 4
Enrich
Context Addition
Add context from previous interactions, preferences, and successful contact patterns - 5
Surface
Unified Views
Present unified, accurate patient views to engagement systems
Key Capabilities
Multi-Source Patient Matching
John Smith at CVS, J. Smith MD at Walgreens, and Smith, John at mail order are recognized as the same person through fuzzy name matching, address normalization, and prescription history patterns.
Therapeutic Reconciliation
Generic metformin marked as non-adherent to branded Glucophage? The intelligence layer maps generics to brands, identifies therapeutic equivalents, and calculates true adherence.
Contact Validation
Before the first call attempt, phone numbers are validated against carrier databases, best contact times identified, and previous successful patterns applied.
Real-Time Eligibility
Eligibility files are often weeks old. The intelligence layer checks current enrollment status, identifies recent disenrollments, and flags deceased patients.
Measuring Success
Organizations implementing an intelligence layer typically see dramatic improvements:
Before Intelligence Layer
Before Intelligence Layer
After Intelligence Layer
Conclusion
Healthcare's data problem is structural, not solvable through better integration or cleaner source systems. The solution is an intelligence layer that reconciles fragmented data at the point of use.
Organizations that implement this approach transform their patient engagement from frustrating phone tag to productive conversations. The data quality problem doesn't go away—but it stops being your team's problem to solve manually.
See How It Works
Discover how an intelligence layer can transform your patient outreach success rates.
About This Research
This white paper is based on Rivvi's analysis of over 2 million patient records across healthcare organizations including health systems, payer networks, and pharmacy operations. Data quality findings reflect aggregated patterns across these organizations.