Making the most of structured data - MedCity News

Manual processes bog down payers in risk adjustment for years – if not decades. Now, an innovative approach to leveraging data enables a deeper understanding of member needs, increased revenue and enterprise-wide benefits stemming from automation.

Most payers report that they correctly capture 95% to 97% of their Hierarchical Condition Category (HCC) codes, a decent accuracy rate given the complexity of the task. Second- and third-level HCC code reviews by staff can be tedious, consuming a significant number of man-hours as coders toil over PDF files stitched together from unrelated systems. While 3% to 5% of incorrectly captured codes may not seem like much, this additional revenue can translate into millions of dollars per year for a health plan.

Given the current healthcare workforce shortage and the complexity of member bases, the ability to identify and streamline lost revenue through automation is a reasonable and worthwhile pursuit. Health plans can approach compliance more effectively by implementing a strategy that uses structured data about patient conditions. Additionally, providing specific, actionable patient data allows payers to better understand the needs of their member base and provide the appropriate level of support. As the industry strives for data usage that is standardized, consistent, and fluid, payers that automate the flow of data will, in the long run, be able to recognize each member’s risk and prioritize their needs to drive member services for outcomes-based care delivery.

Acknowledging the PDF problem

When collecting patient information for risk adjustment, the portable document format (PDF), while imperfect, reigns supreme. PDF saves the exact elements of a printed document to be viewed and shared consistently, regardless of system integration. These documents or electronic images are created to be read by humans, which they can do very easily. However, vital patient information stored in PDF files is unstructured, making it difficult for computers to interpret. Patient medical records are often long and complex, which means that when health plans want to process the data digitally, they must convert the PDF file into structured data. This reverse engineering approach based on optical character recognition and natural language processing can lead to data inaccuracies and is generally ineffective.

Payers must embrace the value of disaggregated structured data, leveraging the value of harmonizing it to create a clearer, longitudinal record of each member. While PDFs provide valuable information for human risk adjusters, they do little to maintain the fluidity of data, analytics, or member understanding that can be used across the enterprise. To effectively manage members’ medical complexities, health plans require accurate and comprehensive data on demographics, test results and diagnoses in forms that computers can accept and process. If they’re not there yet, payers will be on their way soon. PDFs won’t go away, but they will be used alongside structured data, bringing people and computers together to drive the future of risk adjustment.

Growing trust in data

The value of data in risk adjustment for supporting members is evolving into new applications. As noted, the use of structured data in level two and three audits of HCC coding is an effective means of combining human and computer strengths. Once reviewed by coding experts, computers can cost-effectively identify codes to add or delete during code review to help close gaps in justified revenue. Today, humans check the small fraction of differences identified by the computer to ensure compliance. Ultimately, this automated identification process allows payers to receive enough money from CMS to support all members, resulting in a significant return on investment.

Structured data also helps risk adjustment professionals optimize the sequence of charts that need to be coded. Due to the time constraints of human PDF review, they don’t have the resources to code every chart that sits on their desks. Instead, they need to make smart decisions about which members to code right away and which members to code when time permits. If they had structured data at their disposal and algorithms built on that data, the decision would be supported by historical information on the presence of chronic conditions to facilitate member prioritization.

Third, the data can help stratify the risk of members, allowing health plans to have a deeper understanding of clinical risks and offers to support patients in need. Beyond clinical risks, demographic data or information related to social determinants can help payers stay ahead of the curve and remove barriers that prevent successful outcomes.

Finally, the availability of structured data improves the exchange of clinical records with providers, allowing for a streamlined approach to coding exactly what is represented in the member’s record.

Assessing the future of automation

Given the value of structured data within the risk adjustment environment, it will soon be seen as a necessity in delivering modern, member-centric health plans. Transforming data does take time, and despite the availability of structured data, risk adjustment teams are typically used to workflows built on unstructured data in the form of PDF files. There is also anxiety around managing change: “upsetting the apple cart,” failing an audit, or eliminating employee positions in favor of machines. Alas, humans are imperfect and so are computers, which perform independent code reviews with varying degrees of accuracy. This is why human quality assurance is built into the automated code review process, and why computers also perform related tasks such as prioritizing coding. Health plans still rely heavily on employees, using automation to help process large volumes of member data.

Algorithms will become stronger, instilling confidence in automated code reviews. Further in the future, both structured and unstructured data will be extracted and harmonized across different systems to establish a more complete picture of a patient’s health. Today, payers can derive value from structured data, breaking down data silos to understand member complexity and empowering physicians to provide optimal care for the system’s most vulnerable patients.

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