Article

Building a data foundation for coordinated care

A strategic framework for healthcare organizations

Shitij Ambre
Tech Specialist,
CitiusTech

Jan - 10

In today’s experiential economy, patient-centric and value-based healthcare is paradoxical without end-to-end care coordination. It is not only the key to enabling integrated, seamless, and tailored experiences but also instrumental in controlling healthcare costs (zero redundant tests and unnecessary treatments) and medical errors (minimal miscommunication that causes 80% of the errors).^[1][2]

While data modelers, developers, and business analysts seek insight into Dimensional Data Model to alleviate data silos and healthcare fragmentation, there’s a persistent need for a standardized framework that can ensure its seamless integration across the healthcare continuum. Successful care coordination requires a carefully structured data framework that supports complex healthcare relationships while maintaining data integrity and accessibility. To overcome hurdles like complex financial mechanisms, stringent regulations, poor interoperability, medical provider stigma, and lack of staff training on coordinated care, there is a dire need for a comprehensive framework of data modeling that ensures successful care coordination.

Privacy and data security: Safeguarding the vaults of trust

The first step in ensuring care continuity is to take into account the entities that are:

multi-valued (like multiple languages for a patient), and/or
slowly change their value (like the permanent address of a patient)

To address this, a strategic design approach leverages a Bridge Table to link the information in the DimPatient table with the DimLanguage table. The bridge table ‘DimPatientLanguages’, would have foreign keys referencing both the patient ID and the language ID, allowing for a many-to-many relationship between patients and languages.

To recognize patterns and make informed decisions, tracking the Slowly Changing Dimensions (SCDs) in dimensional design is crucial. Our framework enables this by segregating the SCDs into two sets:

Type 1, where a record will be overwritten with a new value when properties of that dimension changes.
Type 2, where a new record will be created in a dimension table when properties of that dimension changes.

For example, if the following Facility is moved to a new address, it will have a new record in it with a new address value. New and prior records exist in the dimension table belonging to the same natural key, along with the Effective From Date and Effective To Date fields to track the changes. The Record Indicator Flag will be used to check the status of the record, viz: valid or expired.

Dimension Table

Evidence-based care coordination interventions

By capturing all foundational entities, the framework enables a flexible care model that is the least resource-intensive. The foundation rests on a multi-data model capturing nuanced relationships between patients, providers, facilities, and services. Core dimensions that form the backbone of coordinated care delivery include:

Patient dimensions that encompass complete demographic information, multiple language capabilities, historical address tracking, and appointment histories
Provider dimensions that track detailed profiles, specializations, facility associations, and coverage areas
Facility dimensions that maintain location details, service capabilities, and resource availability

These dimensions interact through carefully defined relationships maintaining data integrity while enabling flexible care delivery models.

This framework establishes clear integration points for user dimensions managing access and responsibilities, care coordinator dimensions tracking provider relationships, patient dimensions maintaining comprehensive profiles, and facility dimensions documenting care delivery locations.

Tailored guidelines for data loading

The platform seamlessly integrates multiple data dimensions while maintaining strict compliance standards. Solutions must address every aspect of modern healthcare data needs from patient data management to facility coordination. Although the strategy to load data will depend upon the actual physical data model and other factors (such as data volume), which emerge after transforming or customizing this logical data model, the framework follows certain guidelines for data loading and historical data maintenance:

Data Load Order

Reference code tables
Dimension tables
Array tables
Bridge tables
Fact tables

Historical Data Maintenance
The model supports historical data tracking in two perspectives:

Business perspective - Historical data maintenance ensures that past patient demographic information is retained.
Technical perspective - The previous record is updated with an effective end date to indicate when it became invalid. This allows the organization to maintain a historical trail of changes to patient data, facilitating analysis, auditing, and reporting.

A methodical approach to implementation ensures successful deployment while minimizing operational disruption. The focus remains on delivering measurable improvements in care delivery through optimized data coordination.

The framework’s architecture supports emerging technologies while maintaining operational stability. This forward-looking approach ensures organizations can adapt to changing healthcare needs while maintaining coordination effectiveness.

The path forward: Crafting a holistic wellness journey

Healthcare organizations face a critical juncture where effective data management directly impacts care quality and operational success. They must prepare for continued evolution in healthcare delivery through scalable dimension management, efficient historical data handling, and performance optimization strategies while maintaining operational excellence.

Data governance frameworks can establish clear ownership structures, quality management protocols, access control frameworks, and compliance monitoring systems. This will be scaled up in the future with security measures like role-based access control, audit trail maintenance, encryption protocols, and privacy protection measures. Healthcare organizations seeking excellence in care coordination must prioritize:

Continuous data model optimization through regular assessment of dimensional relationships
Technology infrastructure advancement supporting emerging care models
Process improvement enhancing operational efficiency
Robust security and compliance frameworks protecting patient data

Understanding complex healthcare data relationships enables organizations to build sustainable, scalable data foundations. Explore healthcare technology solutions that can transform care coordination capabilities and drive meaningful improvements in patient care delivery.