How to Align Your Organization’s Data: Implementing a Healthcare Data Integration Strategy

April 6, 2023

Article Summary

The latest updates to Federal electronic reporting requirements for the healthcare industry suggest an emerging trend that will require healthcare organizations to future-proof their data management strategies. To stay current, while also planning for the future, organizations can benefit from implementing data integration models that optimize value-based care program reimbursement and align with the new reporting standards.

data integration strategy woman working on multiple desktops

In our previous article, “How to Align Your Organization’s Data with Evolving Reporting Standards: Unlocking High-Value Data,”we discussed the role high-value data plays in aligning a health system’s data to The Centers for Medicare & Medicaid Services (CMS) 2023 mandate for electronic data reporting submissions.

High-value data is created by standardizing data shapes and terminology on a health system-wide scale. This data has the power to transform an organization’s ability to access timely data assets and bring them together to support care.

High-quality data combined with thoughtful healthcare data integration provides clinicians and administrators with the trusted information necessary to benchmark health population metrics and can be combined and reused across other analytics platforms, yielding exponential value.

The Importance of Healthcare Data Integration in Achieving High-Value Data

Implementing a thoughtful healthcare data integration strategy is the next step in evolving a healthcare organization’s ability to optimize data management and generate high-quality data that can deliver real-time, actionable data to clinicians and administrators.

Data integration is a required next step in achieving high-value data because multiple data sources often exist across each health organization, and the data is typically siloed in different source systems that are unable to cross-reference one another to deliver timely insights. Combining high-quality data with advanced analytics empowers organizations to efficiently stay up-to-date with changes in reporting standards and aligned to care quality and financial goals.

How a Healthcare Data Integration Supports Value-Based Care Programs

The new CMS standards point toward a broader shift across the industry for healthcare organizations to update outdated data management protocols and develop more robust analytics for integrating previously siloed data sources. Value-based Care (VBC) programs reward healthcare organizations that provide cost-effective, high-quality care. New regulations have created even greater incentives for healthcare organizations to adopt more efficient strategies for data management.

To meet CMS standards, organizations should implement a deliberate healthcare data integration strategy as it customizes an organization’s data analytics infrastructure and combines clinical and financial metrics to deliver timely, high-value data that drive informed decisions.

Ultimately, integrated, high-value data transforms how clinicians engage with data by delivering real-time trusted data to support value-based care initiatives.

Core Principles of a Successful Healthcare Data Integration Strategy

In order to build a meaningful healthcare data integration strategy, an organization should apply the following four principles: emphasize critical use cases, employ an expansive data model, decentralize the data management function, and optimize data collection.

1: Emphasize Critical Use Cases

Focusing on critical use cases for a healthcare organization’s unique needs will ensure that the data management strategy utilizes high-quality data that can be monitored continuously and proactively.

2: Work with an Expansive Data Model

Combining data across an entire organization’s financial and clinical data systems supports the cultivation of high-quality data assets and promotes system-wide buy-in. This brings trusted data to clinicians, administrators, and leaders and empowers the individuals who are best equipped to drive the highest levels of improvement.

3: Decentralize the Data Management Function

A focus on decentralizing data management ensures timely, cross-domain decision-making that can generate the unique data assets necessary to enable cost, quality, and revenue assessments. Data integration management provides access to important analytics for analysts, clinicians, finance experts, and members of the leadership team in a single, visual-display platform. This frees up valuable time and resources that each team can use to promptly address care quality or financial goals.

4: Optimize Data Collection Strategies

Clinical and financial data collection abilities reduce the reliance on source data. To meet emerging federal standards and prepare for high-value performance, time-to-value data accessibility matters for faster results and increased productivity. Lower barriers to data access through data integration and normalization create high-value, reusable data assets. 

Case Study – MultiCare Health System Leverages Healthcare Data Integration to Meet Ever-Evolving Data Needs

MultiCare Health System, a not-for-profit health care organization based in Tacoma, Washington, put these four principles to work, when it adopted the Health Catalyst data and analytics platform and consolidated its reporting and business intelligence tools.

After MultiCare recognized its processes had not kept pace with the increased demand and changing environment, creating gaps in critical information that clinical and operational leaders needed for data-informed decision making, it leveraged a partnership with Health Catalyst to implement a successful healthcare data integration strategy to ensure it could flex to meet the organization’s ever-evolving data and analytics needs.

MultiCare implemented changes to reduce the number of analytics applications and reports, leveraged its high-quality data and analytics and reusable content, implemented new processes to standardize and triage the management of incoming tickets, and standardized internal monitoring and reporting to allow the analytics team to identify and address problem areas as they arise and facilitate continuous improvement.

Through MultiCare’s reporting and analytics improvement, the organization achieved:

  • 95 percent relative improvement in ticket request response time.
  • 88 percent relative reduction in ticket request backlog.
  • 67 percent relative reduction in non-strategic work.
  • 71 percent relative reduction in the number of analytics solutions.
  • 67 percent relative reduction in the number of reports requested per week.
  • 79 percent relative reduction in full-time employees over four years while maintaining expected service levels—avoiding more than $1 million in costs.

The Importance of Combining High-Value Data with Data Integration

The latest reporting requirements from CMS come when healthcare organizations are already dealing with the challenges posed by budget and resource constraints. Inefficient data management systems create unnecessary barriers for clinicians to access data in a timely manner, keeping quality of care programs, and revenue expectations from achieving optimal results.

Evaluating the strength of your healthcare organization’s ability to leverage high-value data with robust data integration can help establish scalable analytics that will keep pace with the demands of future data reporting standards.

Up Next: In our next article, we’ll explore how to equip your data strategy for future changes. The incentive to modernize data management technology continues to grow, and healthcare organizations that work to prioritize high-value data and data integration will be better prepared to meet value-based expectations in care delivery and reporting outcomes in the future.

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