To Manage Healthcare Hierarchies and Relationships You Need to Re-evaluate Data Models and Architectures
Enable Trust in Healthcare Data
Whether it be black swan events such as Covid-19, new regulatory standards such as HL7 FHIR, or decisions made at the executive level, it is vital to understand how a single action can have a ripple effect over several entities within an organization. To enable sound decision making, executives in leading healthcare systems have placed an emphasis on the accuracy and validity of the data backing their reports. IT orgs have been forced to re-evaluate their existing data models and system architectures to structure their data in a way that reflects their organizations hierarchies and relationships. Additionally, these hierarchies must be flexible and governed, allowing for ongoing changes to be made in a sustainable and standardized fashion.
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IT orgs have been forced to re-evaluate their existing data models and system architectures to structure their data in a way that reflects their organizations hierarchies and relationships.
Challenges of Defining and Maintaining Hierarchies & Relationships
Businesses often struggle managing data that is hierarchical in nature within their current legacy systems and data models.
- Creating trusted reports with clear data lineage
- Identifying coverage gaps
- Mitigating credentialing/enrollment inaccuracies causing write-offs
- Measuring Quality of Care
- Associating costs with departments, locations, and supervisors
- Visibility into social determinants of health
- Associating marketing/website information with operational data for patient scheduling
- Standardizing definitions and terminology across the organization
- Measuring Quality of Care
- Interoperability & adhering to HL7 FHIR standards
Infoverity’s streamlined approach to hierarchy and relationship management
Data Profiling
Understand where data is located and the quality in which it is stored across the business to enable decision making on standardized data procedures and policies.
Data Governance
Establish a group of stakeholders and key decision makers from different business units across the organization that operates in a standardized manner to make quick and efficient decisions regarding data definitions and future initiatives.
Relationship Management
Collaborate with architects on designing a data model that fits the specific needs of the organization while adhering to best practices to effectively manage relationships between Providers, Patients, Locations, and Payors.
Hierarchy Management
Organizing data in a structure that reflects the business model simplifies reporting, analytics, decision making, and increases the flexibility/longevity of data that is hierarchical in nature.
Consolidated Master Datasets
Establish a single source of truth for enterprise reporting and operational data feeds leveraging industry best practices for matching and merging large datasets.
Data Lineage
Traceability of data and the movement of data across the enterprise including system owners, integrations, KPI measurements, data quality monitoring, and access management.
Data Quality & Reference Data Management
Improve the quality and consistency of data by establishing master data dictionaries that standardize inbound data to an enterprise standard.
Interoperability Compliance
Educate staff on new policies and standards that are issued by governing authorities to ensure solutions are created in a sustainable fashion that mitigates risk of fines and additional costs.
Enable Truth in Healthcare Data
Learn how to re-evaluate your existing data models and systems architectures so your data reflects your organizations hierarchies and relationships.