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Optimizing Health Outcomes with Timely Information

The wealth of health-related information drawn from medical records, pharmacies, laboratories, remote patient monitoring, and emerging sources like consumer wearables, offers a range of challenges and prospects for the healthcare industry. The effective integration, management, and utilization of this data is crucial for optimizing long-term health outcomes.

However, the raw data collected from providers, electronic health records (EHRs), laboratories, and other sources cannot be immediately translated into actionable insights. The genuine value of the data emerges when it is transformed into practical knowledge, a process achieved by adapting it to fit seamlessly into existing workflows. The significance of data becomes apparent in its ability to cater to the unique needs of individuals – determining who requires which information and precisely when they require it. At Availity, we term this customized data as “fit-for-purpose data.”

Fit-for-purpose data refers to data that is relevant and actionable based on the intended “purpose,” or use. For example, in a risk adjustment workflow, coders need very specific data elements from clinical sources to capture condition suspects and retrospectively code for reimbursement; and they need this information at certain points in time. Fit-for-purpose data, in this example, is a subset of relevant clinical data, delivered in a format that coders can consume, and at the time that retrospective coding is performed.

Consider the multitude of diverse payer workflows, each with its unique intricacies. The data and its frequency that may suffice for one purpose, like quality measurement, but may not necessarily be suitable for another, such as care coordination. Consequently, fit-for-purpose data is intentionally tailored to cater to the distinct requirements of specific workflows, aiming to optimize the value and influence of the data.

Challenges in Deploying Fit-for-Purpose Data at Scale

Several factors, including inherent variation in source documentation, inconsistent terminologies, poor standardization, and other systemic data quality issues make deploying fit-for-purpose data challenging. These barriers often lead to poor data quality and the need to integrate data from multiple sources to complete each member’s health picture.

Practical Solutions to Leverage Fit-for-Purpose Data at Scale

Creating data that aligns with the specific requirements of each payer workflow may seem like a challenging undertaking. Moreover, the additional complexity of integrating this data into existing workflows leaves organizations grappling with where to begin.

Payers are seeking assistance from clinical data integration vendors who specialize in producing fit-for-purpose data at scale. These vendors help payers utilize clinical data effectively for risk adjustment, quality measurement, care management, value-based care, and enterprise analytics.

The market is flooded with various technology-based solutions, ranging from end-to-end platforms to integration engines and terminology servers. To navigate through this crowded landscape and make an informed choice, consider the following critical characteristics when selecting a clinical data integration partner:

  • Application Programming Interface (API) Driven Workflows: Flexible API-based technology to integrate and deploy data to the right place at the right time. API-driven workflows enable seamless data exchange and interoperability between systems, improving data accessibility and usability.
  • Standards-Based and Transparent Approach: Maintain and leverage industry-standard terminologies to promote semantic interoperability and adhere to government program specifications. Avoid “black box” approaches that rely on non-standard coding or proprietary processes that inhibit adoption and erodes trust in downstream data utilization.
  • Use Case-Oriented Feature Set: Utilize capabilities designed to meet clinical workflow needs and drive speed to value in the customer environment. A comprehensive feature set enables healthcare organizations to address specific requirements and optimize data usage.
  • Ease of Deployment: Offer fast and repeatable implementation processes and provide necessary resources to drive adoption, such as training, documentation, and best practices.
  • Scalability: A clinical data integration system should have the capacity and infrastructure to ingest and optimize large volumes of multi-format health data while delivering real-time results. This includes the ability to ingest and process millions of data records efficiently.
  • Expertise: In-house domain experts with applicable healthcare industry knowledge. Clinical data is a complex space, and it is critical for technology to be built and maintained by experts in data standards, (e.g., HL7), clinical terminologies, informatics, and care delivery.

Deploying fit-for-purpose data at scale is crucial for payers seeking to optimize the impact of data in downstream workflows. While a challenging and complex problem to solve, implementing practical clinical data integration solutions to prepare data for the use case will accelerate a payer’s ability to leverage fit-for-purpose data as a strategic asset for better decision making and better health outcomes.

Interested in learning more? Download our Clinical Data Integration Buyer’s Guide today!