Why Health Plans Need Automation to Get ROI from Clinical Data

Clinical data sourced from electronic health records (EHRs), labs, and health information exchanges (HIEs) contains unique and timely details on members such as vital signs, test results, diagnoses, immunizations, and more. Excited about the potential of clinical data to improve health outcomes and member satisfaction, optimize revenue, strengthen provider networks, and comply with pressing government mandates, health plans are increasing their investment in clinical data acquisition.

But once connections are made, the “pipes” are laid, and clinical data is rolling in, the journey to operationalize and capitalize on this investment has just begun. Across the board, when we assess the quality of the data our clients are acquiring, upwards of 50% of it cannot be used in its raw, native form. Once acquired, it must be integrated into data management systems and normalized, enriched and deduplicated for successful deployment across downstream applications.

Current approaches to data quality improvement involve skilled data scientists and analysts wielding multiple tools such as terminology services and data profiling to make clinical data structurally and semantically normalized and interoperable. These traditional approaches are poorly suited to transforming the sheer volume of clinical data into fit-for-purpose assets, much less scaling to handle the continual influx of data and changes to terminologies, standards, and regulations. As such, these approaches are costly, inefficient, and limiting.

The clinical informatics team at Availity is equipped to anticipate evolving national standards and terminology and aims to ensure that mappings are clinically correct. We incorporate deep clinical know-how into Availity Fusion™, which automates the process of Upcycled Data™, transforming raw, multi-source and multi-format clinical data into a robust, longitudinal data asset.

Availity Fusion’s data refinement capabilities helps ensure that clinical information is compliant with national terminology standards, consistent with its original intent, and further enriched for broadscale application and actionable intelligence. This process eliminates many of the challenges that limit data usability and exchange, such as non-conformant codes, details buried in text, and information delivered in multiple formats or from multiple sources.

The ROI of Upcycled Data

To quantify the return on investment of using an automated approach to data integration and normalization, my colleague Chun Li, Vice President of Informatics at Availity, partnered with a large national health plan to develop an Operational Cost Avoidance Model.

The health plan invested millions of dollars in clinical data acquisition but recognized they wouldn’t be able to cost-effectively achieve the promise of this investment because of systemic clinical data variability and volume. The Availity Clinical Solutions informatics team provided data on the resource and time requirements for Upcycling Data™. This data was based on their nearly decade-long experience in this area, as well as the number of terms per clinical domain found in the provided dataset. In addition, the health plan contributed information on document count, member count, and existing resources involved in data quality activities, which was based on their extensive experience.

The Operational Cost Avoidance Model can be used to calculate the cost that can be saved by deploying Availity Fusion in place of manual methods. Using the model, our partner health plan calculated that for just a subset of its membership, to process 1.96 million continuity of care documents (CCDs), at an average of 2.62 documents per member, would save up to $370 million annually in investment. Using the model, we estimate it could cost a mid-sized health plan up to $75 million for manual resources to perform data improvement functions.

With Availity Fusion’s sub-second processing per document, versus the hours required for an analyst, organizations can achieve a much faster time to value. The model doesn’t consider the ancillary value of redeployment of skilled resources, consistent, repeatable processes, increased trust in data, and scalability for future growth.

To understand how health plans are speeding time to value and avoiding significant costs to make data usable, download our Operational ROI whitepaper.