Health Insurer Provider Network Analytics on D23.io
Master provider network analytics on D23.io's managed Superset. Real dashboards for AU/US health insurers tracking claims-cost-per-member, network adequacy, and provider performance.
Health Insurer Provider Network Analytics on D23.io
Table of Contents
- Why Provider Network Analytics Matter for Health Insurers
- Understanding D23.io’s Managed Superset Platform
- Core Metrics: Claims-Cost-Per-Member and Network Adequacy
- Provider Performance Dashboards: Design and Implementation
- Building Your Analytics Infrastructure
- Real-World Dashboard Examples for AU and US Insurers
- Data Integration and Governance
- Optimising Network Strategy with Analytics
- Compliance and Security Considerations
- Getting Started with D23.io
Why Provider Network Analytics Matter for Health Insurers
Health insurers operate in an increasingly competitive and regulated landscape where provider network strategy directly impacts member satisfaction, claims costs, and regulatory compliance. The ability to measure and optimise your network—understanding which providers drive costs, which deliver outcomes, and where gaps exist—is no longer a nice-to-have; it’s a core operational requirement.
Provider network analytics answer fundamental business questions: Are your members accessing in-network care? Which specialties have coverage gaps? What is the true cost of network breadth versus depth? How do your networks compare across geographies? For Australian health insurers, these questions carry additional weight given the complexity of private and public health interactions, whilst US insurers face similar pressures around network adequacy compliance and member retention.
When you lack visibility into provider network performance, you’re flying blind. Claims spike unexpectedly. Members switch plans because they can’t find in-network cardiologists in their area. Contracting teams negotiate blindly without cost or utilisation data. Compliance officers scramble to document network adequacy when regulators ask. Network analytics solve this by centralising provider, claims, and member data into actionable dashboards that drive evidence-based decisions.
D23.io’s managed Superset platform addresses this directly. Rather than building analytics from scratch or relying on vendor black boxes, you get a purpose-built, flexible analytics environment where you control the logic, own the insights, and can iterate quickly as business priorities shift.
Understanding D23.io’s Managed Superset Platform
D23.io is a managed analytics-as-a-service platform built on Apache Superset, designed specifically for health insurers and healthcare operators who need production-grade dashboards without the operational overhead of self-hosting. Superset is an open-source data visualisation and business intelligence tool that sits between your data warehouse and end users, allowing you to build interactive, SQL-driven dashboards in days rather than months.
What makes D23.io different is the managed layer. You don’t run Superset yourself—D23.io handles infrastructure, upgrades, security patches, and scaling. You focus on data modelling, dashboard design, and business logic. This model is particularly powerful for health insurers because it compresses time-to-insight while maintaining the flexibility you need to adapt to changing regulatory or business requirements.
Superset’s semantic layer—called the “database” layer in Superset terminology—lets you define metrics, dimensions, and calculated fields once, then reuse them across dozens of dashboards. This means your “claims-cost-per-member” metric is calculated consistently everywhere it appears. Your “network adequacy” definition is enforced at the semantic layer, not baked into individual dashboard SQL. This consistency is critical for compliance and stakeholder trust.
D23.io also integrates with enterprise single sign-on (SSO) systems, role-based access control (RBAC), and audit logging—table stakes for health insurers handling sensitive claims and member data. When you’re building dashboards that executives, network managers, actuaries, and compliance teams all use, you need fine-grained permission controls. Superset’s native RBAC, combined with D23.io’s managed security posture, lets you grant access at the dashboard, chart, or even row level.
The platform supports direct connections to your data warehouse (Snowflake, BigQuery, Redshift, Postgres) or data lake, meaning your analytics run on live or near-live data. No data exports, no manual refreshes, no stale insights. For a health insurer tracking claims in real-time or monitoring network changes, this freshness is essential.
Core Metrics: Claims-Cost-Per-Member and Network Adequacy
Two metrics sit at the centre of every health insurer’s provider network strategy: claims-cost-per-member (CCPM) and network adequacy.
Claims-Cost-Per-Member (CCPM)
CCPM is deceptively simple: total claims costs divided by covered members, typically calculated monthly or annually. But the power lies in slicing it by multiple dimensions. What is CCPM by provider network? By geography? By product line? By member age cohort? By diagnosis or procedure type?
When you break CCPM down by individual providers or provider groups, you identify outliers. A cardiology practice with CCPM 40% above the network average warrants investigation—are they treating sicker patients, ordering unnecessary tests, or driving genuine value through better outcomes? A primary care network with CCPM 15% below average might be understaffed or under-utilised, creating access issues.
CCPM also reveals network composition risk. If 60% of your claims costs flow through 15% of providers, you have concentration risk. If your network is broad but shallow—many providers with low utilisation—you’re paying for breadth you’re not using. Analytics let you see this trade-off clearly and make conscious choices about where to invest.
In the D23.io dashboard context, CCPM is typically calculated at the data warehouse layer (in Snowflake or BigQuery) and then exposed through Superset as a metric. You define it once—sum of allowed amounts, divided by distinct member count, filtered by date range—and then drill into it by provider, geography, product, or any other dimension your claims data supports.
Network Adequacy
Network adequacy is a regulatory concept: do your members have reasonable access to in-network providers across all required specialties and geographies? In Australia, private health insurers must demonstrate network adequacy to the Private Health Insurance Ombudsman. In the US, health plans face state and federal requirements around network adequacy, with specific distance and time-to-care thresholds.
Measuring adequacy requires linking claims data to provider location and specialty data. The question becomes: for each member, what is the closest in-network provider in each specialty? How many members in a given postcode lack an in-network provider within X km? What percentage of members have a PCP available within Y minutes’ drive time?
D23.io dashboards can visualise this through heat maps, gap analysis tables, and trend charts. You might see that your network has adequate cardiologists in Sydney’s inner west but gaps in the outer west. Or that rural members have longer travel times to specialists. These insights drive contracting priorities: you know exactly where to recruit new providers to close gaps.
Network adequacy analytics also feed compliance. When your regulator asks “prove your network is adequate,” you pull a dashboard showing member-to-provider ratios, geographic coverage, and specialty availability. This is far stronger than anecdotal claims.
Integrating CCPM and Adequacy
The real insight emerges when you combine CCPM and adequacy analytics. You might discover that your lowest-cost network has the worst adequacy metrics—members are using out-of-network providers because in-network options don’t exist. Or that your highest-adequacy network is driving CCPM up because you’ve recruited too many providers in low-utilisation areas.
D23.io’s Superset environment lets you build dashboards that show both metrics side-by-side, filtered by product, geography, or member cohort. This drives network strategy conversations from guesswork to data-driven prioritisation.
Provider Performance Dashboards: Design and Implementation
A well-designed provider performance dashboard answers three questions: (1) How is this provider performing relative to peers? (2) What is their cost and utilisation profile? (3) Are there quality, access, or compliance concerns?
Key Dashboard Components
Provider Summary Card: Name, specialty, location, network status, member count, claims volume YTD. This is your entry point—it tells you immediately if you’re looking at a high-volume specialist or a small practice.
Cost and Utilisation Trend: A line chart showing claims volume and CCPM over the past 12–24 months. This reveals whether a provider is stable, growing, or declining. A sudden cost spike might indicate a new high-cost procedure, a patient cohort change, or billing issues.
Comparative Benchmarking: How does this provider’s CCPM compare to specialty peers? To your network average? To national benchmarks? Superset can pull benchmark data from provider network analytics tools and best practices or from your own historical data. A visual comparison (box plot, percentile rank) makes outliers obvious.
Utilisation by Service Line: A breakdown of claims by procedure type, diagnosis, or service category. This reveals what this provider actually does. A “cardiologist” who bills 60% for primary care visits might be filling a gap, or might be misclassified.
Member Satisfaction and Outcomes (if available): Net Promoter Score, complaint rate, readmission rate, or other quality metrics. These humanise the cost data. A high-cost provider might deliver better outcomes; a low-cost provider might have poor member satisfaction.
Network Adequacy Contribution: For each provider, show how many members they serve as primary or specialist, and whether they fill a geographic or specialty gap. This contextualises their role in your network strategy.
Building the Dashboard in Superset
In D23.io’s managed Superset, you start by connecting to your claims warehouse. You’ll typically have three core tables: members (demographics, enrollment), providers (NPI, specialty, location, contract terms), and claims (dates, amounts, procedure codes, provider IDs).
You then define calculated fields (metrics) in Superset’s semantic layer:
- Total Claims: Sum of allowed amounts
- Claims Count: Count of claim records
- Unique Members: Count of distinct member IDs
- CCPM: Total Claims / Unique Members
- Claims per Member: Claims Count / Unique Members
- Average Claim Amount: Total Claims / Claims Count
Once these metrics are defined, building the dashboard is drag-and-drop. You create a chart (e.g., a table showing providers ranked by CCPM), set filters (e.g., specialty, date range, network status), and Superset generates the SQL and executes it against your warehouse.
The key to a performant dashboard is thoughtful data modelling upstream. If your warehouse is a raw claims table with 500M rows, Superset queries will be slow. If you’ve pre-aggregated claims by provider-month in a summary table, queries snap back in milliseconds. D23.io and PADISO can help design this architecture—balancing freshness, query performance, and maintainability.
Building Your Analytics Infrastructure
Successful provider network analytics require a solid foundation. This means data pipelines, data quality, and semantic consistency.
Data Sources and Integration
Your primary data sources are:
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Claims Data: From your claims processing system or TPA. This includes member ID, provider ID (typically NPI), date of service, procedure code, diagnosis code, allowed amount, paid amount, member copay/coinsurance.
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Provider Master Data: NPI, name, specialty (primary and secondary), address, phone, contract terms, network status (in-network, out-of-network, credentialed but not contracted). This often comes from your provider contracting or credentialing system.
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Member Data: Member ID, age, gender, postcode, enrollment status, product line, employer (if group), effective dates. From your member management system.
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Network Definition Data: Which providers are in which networks? Which members are eligible for which networks? This might be stored in your benefits administration system or policy database.
Moving this data into D23.io’s Superset requires a data warehouse (Snowflake, BigQuery, Redshift) or data lake (Databricks, Iceberg). You’ll set up ETL pipelines (using tools like dbt, Fivetran, or custom scripts) to extract data from source systems, transform it (clean, deduplicate, join), and load it into your warehouse daily or hourly.
This is where many health insurers struggle. Claims data is messy. Provider IDs might be missing or incorrect. Member IDs might change. Dates might be in different formats. A production-grade analytics platform requires clean, consistent data. This is why data standards like USHIK exist—they define how healthcare data should be structured.
Data Quality and Governance
Once data is in your warehouse, you need monitoring. Are claims arriving on time? Are there unexpected spikes in claim counts or amounts? Are provider records stale? D23.io’s Superset can include data quality dashboards that monitor pipeline health, alert when SLAs are breached, and help you catch data issues before they corrupt your insights.
Governance means defining who owns what data, who can change it, and how. For provider network analytics, the network manager typically owns provider master data, the actuary owns claims analysis definitions, and compliance owns network adequacy reporting. Role-based access in Superset ensures each team sees only what they need.
Documentation is critical. Every metric, every filter, every assumption should be documented. Why is CCPM calculated this way? Why are out-of-network claims excluded from adequacy analysis? Why is this provider classified as a cardiologist? When someone questions a dashboard number, you need to explain the logic clearly.
Semantic Layer and Metric Definitions
Superset’s semantic layer is where you codify business logic. Instead of every analyst writing their own SQL for CCPM, you define it once in the semantic layer. This ensures consistency, reduces errors, and makes dashboards more maintainable.
For health insurers, consider defining metrics for:
- Network Metrics: In-network percentage, out-of-network percentage, claims-cost-per-member, claims-per-member, average claim amount
- Provider Metrics: Provider utilisation (members served), provider concentration (% of claims from top N providers), provider churn (new/lost providers per month)
- Adequacy Metrics: Specialty coverage by geography, distance-to-care, member-to-provider ratios
- Quality Metrics (if available): Readmission rate, preventable ER visits, member satisfaction
Once these are defined in the semantic layer, building dashboards is fast. You’re not writing SQL; you’re configuring which metrics to show, how to slice them, and what filters to apply.
Real-World Dashboard Examples for AU and US Insurers
Australian Health Insurer Example
An Australian private health insurer with 500K members across three networks (Basic, Standard, Premium) needs to understand network performance and adequacy. Their D23.io dashboard includes:
Network Comparison Dashboard: Side-by-side metrics for each network—total claims, CCPM, member count, in-network percentage, adequacy score. This reveals that their Premium network has higher CCPM but better adequacy, whilst their Basic network has lower costs but gaps in specialist availability.
Geographic Coverage Heat Map: A map of Australia showing member concentration by postcode and in-network provider availability by specialty. This reveals that rural members have limited access to cardiologists and orthopedists, driving the need for targeted contracting in those areas.
Provider Ranking Table: Top 50 providers by claims volume, with columns for CCPM, member count, specialty, location, and trend (growing/stable/declining). The insurer uses this to identify high-value providers for relationship management and outliers for investigation.
Adequacy Compliance Dashboard: Shows the percentage of members with an in-network GP within 10km, an in-network cardiologist within 30km, etc. This is built specifically to match PHIO requirements and is updated monthly for compliance reporting.
This insurer also benefits from AI automation for insurance claims processing, which can feed anomalies back into their dashboards—flagging unusual provider billing patterns for network managers to investigate.
US Health Insurer Example
A US health plan with 2M members across 15 states faces different regulatory pressures. Their D23.io dashboard focuses on:
State-by-State Adequacy Dashboard: For each state, shows network adequacy metrics against state-specific requirements (e.g., primary care within 30 minutes, specialists within 45 minutes). This is tied to their compliance calendar—when the state regulator audits, they pull this dashboard as evidence.
Network Competitiveness Analysis: Compares their network breadth, depth, and cost to competitors in each state. This feeds strategic decisions about where to invest in network growth.
Claims Routing and Leakage: Shows what percentage of claims flow in-network versus out-of-network, by service line. High out-of-network percentages in a service line trigger investigations—are members not finding in-network providers? Are in-network providers not available at the right times?
Provider Consolidation Analysis: As part of M&A activity (common in US health insurance), they use dashboards to identify overlapping providers, redundant contracting, and consolidation opportunities. This supports the value-creation engineering that roll-up projects require.
Both examples show how D23.io’s Superset platform adapts to regional and regulatory contexts. The underlying architecture is the same, but the dashboards are tailored to what matters locally.
Data Integration and Governance
Moving from data silos to an integrated analytics platform is a significant undertaking. Here’s how to do it systematically.
Phase 1: Assessment and Planning
Start by inventorying your data sources. Where does claims data live? Provider data? Member data? How often is it updated? What quality issues exist? Create a data lineage diagram showing how data flows from source systems to your warehouse to dashboards.
Identify your business requirements. What questions do network managers ask repeatedly? What compliance reporting do you need? What decisions would better data enable? Prioritise ruthlessly—you can’t build everything at once.
Assess your technical readiness. Do you have a data warehouse? If not, cloud-native options like Snowflake or BigQuery are fast to deploy. Do you have data engineering capability? If not, managed ETL tools like Fivetran reduce the burden. Do you have BI tools? Superset via D23.io is purpose-built for health insurers and faster to deploy than enterprise BI platforms.
Phase 2: Data Warehouse Design
Your warehouse should follow a dimensional model: fact tables (claims, member enrollments) and dimension tables (providers, members, dates). This structure makes queries fast and dashboards intuitive.
For health insurers, a typical schema includes:
- fact_claims: One row per claim. Dimensions: member_id, provider_id, date, procedure_code, diagnosis_code, amount, member_copay, paid_amount. Slowly changing dimensions for provider and member attributes.
- fact_member_months: One row per member per month. Dimensions: member_id, month, network, product_line, age_band, postcode. Measures: premium, out-of-pocket, claims_count, claims_amount.
- dim_provider: One row per provider. Attributes: NPI, name, specialty, address, network_status, contract_terms. Slowly changing dimensions to track history (when did they join/leave the network?).
- dim_member: One row per member. Attributes: member_id, age, gender, postcode, employer, enrollment_date, termination_date.
This design supports fast queries and clear semantics. When you ask “what is CCPM by specialty?” Superset joins fact_claims to dim_provider, groups by specialty, and calculates the metric.
Phase 3: ETL and Data Quality
Set up automated pipelines to load data daily. Use tools like dbt to transform raw claims into the dimensional model. Add data quality checks: do claim counts match source systems? Are there orphaned provider IDs? Are dates in the right range?
Create a data quality dashboard (in Superset, naturally) that monitors pipeline health. Alert when claim volume drops unexpectedly, when provider records are stale, or when member counts don’t match enrollment systems.
Phase 4: Semantic Layer and Metrics
Once data is clean and integrated, define your metrics in Superset’s semantic layer. This is where business logic lives. Document every metric: definition, calculation, assumptions, update frequency.
For health insurers, consider metrics like:
- Network Metrics: CCPM, in-network percentage, out-of-pocket per member, claims-per-member
- Provider Metrics: Provider utilisation (members served), claims concentration, provider churn
- Member Metrics: Premium per member, claims per member, out-of-pocket per member, network switching rate
- Adequacy Metrics: Specialty coverage by geography, distance-to-care, member-to-provider ratio
Define these once, reuse them everywhere. This is the power of the semantic layer.
Phase 5: Dashboard Development and Rollout
Start with high-impact dashboards: network performance, provider ranking, adequacy compliance. These drive immediate business decisions and build momentum.
Involve stakeholders—network managers, actuaries, compliance, finance—in dashboard design. They know what questions matter. Iterate based on feedback.
Roll out access gradually. Start with a core team, gather feedback, refine, then expand to broader audiences. This reduces confusion and ensures dashboards meet real needs.
Optimising Network Strategy with Analytics
Analytics are only valuable if they drive decisions. Here’s how provider network analytics feed strategy.
Network Design and Contracting
Use analytics to inform contracting priorities. Where are adequacy gaps? Which specialties are underrepresented? Which geographies have high out-of-network usage? Target recruitment toward these gaps.
Use benchmarking to negotiate better terms. If your CCPM is 20% above peers, investigate why. Is it provider mix? Member mix? Service line mix? Once you understand the driver, you can negotiate smarter—asking for discounts from high-cost providers or recruiting lower-cost alternatives.
Use provider performance data to identify relationship opportunities. High-performing, low-cost providers are candidates for deeper partnerships (e.g., preferred provider arrangements, value-based contracting). Outliers warrant investigation or potential de-contracting.
Network Adequacy and Member Access
Use adequacy dashboards to prioritise network investments. If 20% of members lack an in-network cardiologist within 30 minutes, recruit cardiologists in those areas. If rural members have poor access, consider telehealth partnerships or travelling clinics.
Monitor adequacy trends. If in-network percentage is declining, investigate—are providers leaving the network? Are members switching to out-of-network? This early warning system lets you respond before adequacy becomes a compliance issue.
Cost Management and Value-Based Care
Use CCPM analytics to identify cost drivers. Is it high-volume, low-cost services? High-cost procedures? Expensive providers? Once you identify the driver, you can intervene—negotiating volume discounts, implementing care management programs, or shifting to value-based contracting.
For health insurers moving toward value-based care, analytics are essential. You need to track outcomes (readmissions, preventable ER visits, member satisfaction) alongside costs. A high-cost provider delivering better outcomes is a good investment; a low-cost provider with poor outcomes is a liability.
Competitive Positioning
Use comparative analytics to position your networks competitively. If your network is broader but more expensive than competitors, you might emphasize access and choice. If your network is narrower but lower-cost, emphasize affordability. Analytics let you quantify these trade-offs and make conscious positioning choices.
For brokers and employers evaluating your plans, network analytics provide credibility. Rather than claiming “we have a great network,” you show data: “our network includes 95% of specialists in your area, with average CCPM 12% below regional benchmarks.”
Compliance and Security Considerations
Health insurer data is sensitive. Provider network analytics must be built with compliance and security in mind.
Data Security
D23.io’s managed Superset platform includes encryption in transit and at rest, network isolation, and regular security updates. But you’re responsible for controlling access. Use role-based access control (RBAC) to ensure network managers see only their network data, actuaries see only what they need, and compliance sees only what regulators require.
Audit logging is critical. Who accessed which dashboards? When? What data did they export? For a health insurer, this audit trail is a compliance requirement and a security best practice.
Data Privacy
When building provider network analytics, be mindful of member privacy. Dashboards should never expose individual member names or identifiers. Aggregate at the provider or geographic level. If you’re showing member-level data (e.g., to identify high-cost members for care management), that’s a separate, access-controlled environment.
Similarly, provider data should be de-identified where possible. You might show “Provider A in Postcode 2000” without revealing the provider’s name, particularly if you’re publishing benchmarks externally.
Regulatory Compliance
For Australian health insurers, private health insurance regulations require demonstrating network adequacy. D23.io dashboards provide this evidence. Document your methodology: how do you define adequacy? How do you measure it? How often do you review it? When regulators ask, you have clear, auditable answers.
For US health insurers, state and federal regulations require network adequacy compliance and often mandate reporting. D23.io dashboards feed this reporting directly. Rather than assembling reports manually, you pull them from dashboards—faster, more accurate, more auditable.
When pursuing SOC 2 compliance or ISO 27001 certification, analytics platforms are in scope. You’ll need to document data flows, access controls, and audit logging. D23.io’s managed infrastructure and Superset’s native security features simplify this.
Getting Started with D23.io
If you’re a health insurer ready to build provider network analytics, here’s how to start.
Step 1: Assess Your Current State
Where is your data today? In legacy systems? In a data warehouse? Scattered across multiple platforms? What dashboards or reports exist? What’s missing? What decisions are you making blind?
Involve your stakeholders: network managers, actuaries, compliance, finance, IT. Understand their pain points and priorities.
Step 2: Define Your Target State
What dashboards do you need? What metrics matter most? What frequency (daily, weekly, monthly)? What access model (who sees what)? What compliance requirements must be met?
Prioritise ruthlessly. You can’t build everything at once. Start with high-impact dashboards that drive immediate decisions.
Step 3: Evaluate D23.io and Superset
D23.io offers managed Superset with healthcare-specific features. Consider a proof-of-concept: connect your claims warehouse, build one dashboard (e.g., provider ranking by CCPM), and see if it meets your needs. A typical PoC takes 2–4 weeks and costs a fraction of a full implementation.
Alternatively, PADISO offers D23.io consulting engagements that include architecture design, data modelling, semantic layer definition, dashboard development, and training. A $50K fixed-fee engagement typically delivers a production-ready analytics platform in 6 weeks.
Step 4: Build Your Data Foundation
If you don’t have a data warehouse, set one up. Snowflake and BigQuery are fast to deploy and cost-effective. If you have a warehouse but it’s not set up for analytics, work with a data engineer to design a dimensional model.
Set up ETL pipelines to load claims, provider, and member data daily. Add data quality monitoring. This foundation is critical—everything else depends on clean, integrated data.
Step 5: Develop and Deploy Dashboards
Start with your highest-priority dashboards. Involve stakeholders in design. Iterate based on feedback. Once dashboards are working, roll out access gradually.
As you gain confidence, add more dashboards. Build a library of reusable metrics in the semantic layer. Create self-service capabilities so business users can slice and dice data without writing SQL.
Step 6: Operationalise and Iterate
Once dashboards are live, monitor usage. Are people using them? Are they driving decisions? Gather feedback quarterly and iterate. Add new metrics as business priorities change. Remove dashboards that aren’t used.
Keep your data fresh. Monitor pipeline health. Update documentation. Train new users. Treat analytics as a living, evolving capability, not a one-time project.
Advanced Topics: Agentic AI and Network Analytics
As your analytics mature, consider layering in agentic AI. Rather than requiring users to navigate Superset’s interface, they could ask Claude or another AI agent: “What is our CCPM by specialty this month? Which specialties are outliers?” The agent queries your Superset dashboards, synthesises the results, and provides a natural-language summary.
PADISO’s work on agentic AI and Apache Superset integration shows how this works in practice. Non-technical users (network managers, executives) can get insights without learning Superset’s UI. This democratises analytics and accelerates decision-making.
For health insurers, this means compliance officers can ask “show me our network adequacy by state” and get an instant answer. Network managers can ask “which providers are driving CCPM up?” without waiting for analysts. Executives can ask “how do our networks compare on cost and adequacy?” and get a visual, narrative response.
This is where analytics become truly transformative—not just dashboards for specialists, but insights accessible to anyone who needs them.
Conclusion: From Data to Decisions
Provider network analytics on D23.io’s managed Superset transform how health insurers operate. Instead of relying on intuition, spreadsheets, or vendor black boxes, you get transparent, auditable, actionable insights into your network’s performance.
The journey starts with clean data and a clear business model (facts and dimensions). It continues with thoughtful metric definitions in the semantic layer. It accelerates with well-designed dashboards that answer real questions. And it matures when analytics drive decisions—network design, contracting, compliance, competitive positioning.
For Australian health insurers, D23.io offers a local, compliant analytics platform. For US health insurers, it provides the flexibility and auditability that regulators demand. For both, it compresses time-to-insight and reduces the operational burden of self-hosted BI tools.
If you’re ready to move beyond gut-feel network decisions, D23.io and PADISO can help. We’ve built AI automation capabilities for insurance that feed analytics, designed platform engineering architectures that scale, and delivered AI strategy and readiness assessments that position insurers for the future.
Your provider network data is an asset. It’s time to unlock it.