Chart retrieval is one of the major pain points cited by risk-bearing organizations as an impediment to risk adjustment, HEDIS/quality, and other key initiatives. This time consuming and expensive process is typically done manually via fax, mail, or by sending chart abstractors in person to provider offices. Many organizations use chart retrieval vendors, but the costs can be prohibitive and continue to increase as volume goes up. In addition, vendors charge on a per chart basis so if a patient has visited multiple provider offices, organizations would have to pay for retrieval from each office. Acquiring patient charts is critical to an organization’s success in risk adjustment programs yet most organizations, unable to find a viable alternative, are left with these antiquated chart retrieval methods.
The Downside of Retrieval Workarounds
Due to the high costs of chart retrieval, organizations often “target” the charts that they want to retrieve in one of two ways:
- Claims-based analytics.
These analytics rely on algorithms that look at historical claims and attempt to predict how much outstanding HCC capture exists. However, chart retrieval targeting effectiveness is limited because claims are often inaccurate or incomplete. Because targeting relies heavily on reconfirmation of chronic conditions, it can further break down in cases where members have new diagnoses being captured for the first time or an acute condition. Our analysis indicates that there is significant revenue opportunity remaining when organizations limit the scope of their chart retrieval. An organization that targets members via claims analytics typically captures about 70% of the total risk-adjusted revenue. That still leaves 30% of risk-adjustable revenue unaddressed. For a health plan with around 55,000 members, that’s roughly $18M of outstanding revenue.
- PCP Charts.
Organizations may target just primary care physician records for their chart retrieval efforts, believing that the majority of risk gaps can be captured. However, our data indicates that approximately 50% of HCC coefficients come from specialists’ charts. This was a higher number than what we expected to see, further providing evidence that targeting PCPs may not be the best strategy if the goal is to capture all outstanding risk conditions.
An Alternative Solution to Chart Retrieval
n alternative to manual chart retrieval, Health Fidelity’s Automated Chart Extraction (ACE) data acquisition technology enables medical records to be extracted from a provider’s EMR in an automated, scalable, and secure manner. Organizations that are set up with ACE are able to download all of the medical records of members from a provider for an annual flat-fee. This typically decreases chart retrieval costs by over 80%. Furthermore, ACE enables retrieval of data without any abrasion to the providers or administrative burden to IT resources. Once the technology is set up, ACE allows you to obtain high quality, complete, and timely data without incremental resources.
By using technology to streamline chart retrieval, ACE allows risk-bearing organizations to acquire a more complete data set for their risk adjustment needs. More than just a tool to reduce costs, ACE increases risk capture by enabling access to data that organizations would normally not have obtained. Having a robust data acquisition solution will allow organizations to acquire high-quality data continuously and automatically to fuel complete and accurate risk capture.
Leveraging NLP to Optimize ROI
Whether you obtain medical records manually or via an automated technology like ACE, funneling your medical records through data acquisition technology such as NLP helps targeting and risk adjustment activities alike. Organizations that can acquire all their available charts for analysis with NLP will be armed with more accurate and complete data to understand the revenue opportunity of their membership and optimize the ROI on their risk adjustment activities.
Find out how Health Fidelity can help your organization with our data acquisition technology.
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