Breaking Down Healthcare’s Data Walls: The Power of Enterprise Data Management

The following is a guest article by Ashay Thakur, VP of Data Strategy at Cedar Gate Technologies

The traditional fee-for-service (FFS) payment model has dominated the healthcare industry’s payment systems for decades. Unfortunately, this focus on quantity over quality has plagued the healthcare industry, leading to high-volume care with little to no accountability for whether it provides value in terms of better patient outcomes. Thankfully, many payers, providers, and employers are moving away from FFS to value-based care (VBC), which prioritizes quality outcomes over volume. 

This transition also holds promise for more cost-effective care delivery. However, the path to successful VBC adoption is not easy, and participants must learn to navigate the nuances and complexities of pursuing a model where the incentives are completely counter to FFS. In VBC, providers are incentivized to keep patients healthy, limiting the need for acute intervention and more care or procedures. That requires extensive data-sharing capabilities among all healthcare stakeholders to personalize care based on someone’s health history, risks, and unique care needs.

Data interoperability is, and has been, a long-standing issue in healthcare. Many payer and provider networks are built on a patchwork of legacy systems that were not designed for value-based care. Much of this technology lacks the capabilities to predict risk, measure outcomes, meet quality goals, and bill for services in new payment models such as prospective bundles or capitation. The failure to achieve effective data interoperability disrupts provider operations and payer-provider relationships, leading to delayed claim processing, reduced revenue streams, and inefficiencies across the board.

Here’s how these challenges play out for many payers and providers: 

Struggles to Share Data that Enhances Payer-Provider Collaboration

Most payers struggle to implement essential VBC functions, primarily coordinating care with provider partners in their networks. Key obstacles include fragmented data, and limited visibility while working from different datasets – with providers operating out of an EHR while payers mainly use claims data. Poor communication and disparate systems only compound these challenges. Addressing these issues requires robust data sharing and interoperability to facilitate more coordinated care and address challenges like health disparities and access. But it must also include capabilities to protect proprietary information and data, so payers don’t worry about compromising their competitive advantage.

Grappling with Fragmented Care Management Technologies

The lack of data interoperability among care management systems poses a significant obstacle for payers and providers seeking to implement and collaborate on VBC models. When payers and providers are working from multiple, disconnected solutions, it’s more difficult to create effective care plans that address the needs of a member or patient population while also helping everyone meet value-based care goals. That includes providers trying to collaborate effectively across clinics and facilities to meet patients’ needs and coordinate care. In a recent JAMA survey, just 8% of providers said that they could easily use information from different EHRs.

Maintaining Security and Protecting Patient Data

Technological alignment is a frequent challenge for payers, driven by the complexity and volume of medical data needed for operations. Organizations must be able to efficiently manage a diverse tech stack to ensure interoperability. Misalignment can lead to increased risk of HIPAA violations or data breaches, operational inefficiencies, and care delays.

The Need for Technological Alignment

Technological alignment is essential to overcome these data interoperability challenges. Next-generation enterprise data management systems are designed to offer all healthcare stakeholders – including payers, providers, and patients – access to accurate, enriched data that supports the entire care journey. They go beyond simply storing data, enabling users to capture information from disparate sources, then cleansing, normalizing, and homogenizing the information in a centralized data lake. 

These systems leverage advanced technologies such as AI and machine learning to aggregate, enrich, and present data to support informed decision-making. These advanced enterprise data management systems can then feed clean data into analytics, care, and payment applications to help users predict and stratify risk in member populations, optimize employer benefits plans, build high-performing provider networks, and target populations for appropriate clinical interventions.

The benefits of enterprise data management extend beyond improved data interoperability. These systems also empower healthcare organizations to meet regulatory requirements, enhance reporting capabilities, and support performance in any VBC model. By simplifying tech stacks and removing barriers to collaboration, enterprise data management systems pave the way for more effective AI-driven decision support and predictive analytics.

A Path Forward for Data Management

We live and work in a time when data is the key to unlocking better care. By embracing the next generation of enterprise data management systems, healthcare organizations can overcome the persistent challenges of fragmented data and inefficient communication. They offer a sustainable solution that guarantees providers and payers can make informed decisions and collaborate effectively. This improves patient outcomes and operational efficiency and supports the successful adoption of VBC models.

About Ashay Thakur

Ashay Thakur is the VP of Data Strategy at Cedar Gate Technologies. He oversees strategic development and governance of the organization’s data foundation, driving innovation to enhance scalability, quality, and excellence across Cedar Gate’s end-to-end value-based care platform.

The following is a guest article by Ashay Thakur, VP of Data Strategy at Cedar Gate Technologies

The traditional fee-for-service (FFS) payment model has dominated the healthcare industry’s payment systems for decades. Unfortunately, this focus on quantity over quality has plagued the healthcare industry, leading to high-volume care with little to no accountability for whether it provides value in terms of better patient outcomes. Thankfully, many payers, providers, and employers are moving away from FFS to value-based care (VBC), which prioritizes quality outcomes over volume. 

This transition also holds promise for more cost-effective care delivery. However, the path to successful VBC adoption is not easy, and participants must learn to navigate the nuances and complexities of pursuing a model where the incentives are completely counter to FFS. In VBC, providers are incentivized to keep patients healthy, limiting the need for acute intervention and more care or procedures. That requires extensive data-sharing capabilities among all healthcare stakeholders to personalize care based on someone’s health history, risks, and unique care needs.

Data interoperability is, and has been, a long-standing issue in healthcare. Many payer and provider networks are built on a patchwork of legacy systems that were not designed for value-based care. Much of this technology lacks the capabilities to predict risk, measure outcomes, meet quality goals, and bill for services in new payment models such as prospective bundles or capitation. The failure to achieve effective data interoperability disrupts provider operations and payer-provider relationships, leading to delayed claim processing, reduced revenue streams, and inefficiencies across the board.

Here’s how these challenges play out for many payers and providers: 

Struggles to Share Data that Enhances Payer-Provider Collaboration

Most payers struggle to implement essential VBC functions, primarily coordinating care with provider partners in their networks. Key obstacles include fragmented data, and limited visibility while working from different datasets – with providers operating out of an EHR while payers mainly use claims data. Poor communication and disparate systems only compound these challenges. Addressing these issues requires robust data sharing and interoperability to facilitate more coordinated care and address challenges like health disparities and access. But it must also include capabilities to protect proprietary information and data, so payers don’t worry about compromising their competitive advantage.

Grappling with Fragmented Care Management Technologies

The lack of data interoperability among care management systems poses a significant obstacle for payers and providers seeking to implement and collaborate on VBC models. When payers and providers are working from multiple, disconnected solutions, it’s more difficult to create effective care plans that address the needs of a member or patient population while also helping everyone meet value-based care goals. That includes providers trying to collaborate effectively across clinics and facilities to meet patients’ needs and coordinate care. In a recent JAMA survey, just 8% of providers said that they could easily use information from different EHRs.

Maintaining Security and Protecting Patient Data

Technological alignment is a frequent challenge for payers, driven by the complexity and volume of medical data needed for operations. Organizations must be able to efficiently manage a diverse tech stack to ensure interoperability. Misalignment can lead to increased risk of HIPAA violations or data breaches, operational inefficiencies, and care delays.

The Need for Technological Alignment

Technological alignment is essential to overcome these data interoperability challenges. Next-generation enterprise data management systems are designed to offer all healthcare stakeholders – including payers, providers, and patients – access to accurate, enriched data that supports the entire care journey. They go beyond simply storing data, enabling users to capture information from disparate sources, then cleansing, normalizing, and homogenizing the information in a centralized data lake. 

These systems leverage advanced technologies such as AI and machine learning to aggregate, enrich, and present data to support informed decision-making. These advanced enterprise data management systems can then feed clean data into analytics, care, and payment applications to help users predict and stratify risk in member populations, optimize employer benefits plans, build high-performing provider networks, and target populations for appropriate clinical interventions.

The benefits of enterprise data management extend beyond improved data interoperability. These systems also empower healthcare organizations to meet regulatory requirements, enhance reporting capabilities, and support performance in any VBC model. By simplifying tech stacks and removing barriers to collaboration, enterprise data management systems pave the way for more effective AI-driven decision support and predictive analytics.

A Path Forward for Data Management

We live and work in a time when data is the key to unlocking better care. By embracing the next generation of enterprise data management systems, healthcare organizations can overcome the persistent challenges of fragmented data and inefficient communication. They offer a sustainable solution that guarantees providers and payers can make informed decisions and collaborate effectively. This improves patient outcomes and operational efficiency and supports the successful adoption of VBC models.

About Ashay Thakur

Ashay Thakur is the VP of Data Strategy at Cedar Gate Technologies. He oversees strategic development and governance of the organization’s data foundation, driving innovation to enhance scalability, quality, and excellence across Cedar Gate’s end-to-end value-based care platform.