Effective Clinical Data Normalization Multiply Use Cases for Clinical Data
Jonathan M. Niloff, MD, MBA
Chief Medical Officer
The most robust source of clinical data for interoperability is the clinical document (CCD, C-CDA) produced by electronic health records (EHRs). Every Meaningful Use certified EHR is required to produce a clinical document to support interoperability. However, as we have discussed, consuming these documents for all use cases is challenging due to the heterogeneity of the documents produced by different EHRs and the variation in how providers document clinical care. A sophisticated tool to normalize, organize and deduplicate the data in clinical documents is required. And this is not a new problem. As Robert Burns, a professor of healthcare management at the University of Pennsylvania’s Wharton School, said “The complexity of integrating mismatched data sets has vexed hospitals and other healthcare entities for decades.” ( STAT News, June 11, 2018).
There is technology available to address this vexation. Such a capability enables the discussion to progress beyond the normalization challenge to “How can we use this clinical data to improve healthcare?” In this post, I’ll discuss several of the most promising use cases for payers and providers.
For payers, normalized clinical data enables more accurate coding for risk adjustment, including RAF scores for Medicare Advantage. This avoids time consuming surveys and chart reviews. In addition to classic coding optimization, clinical data can also be used to support new Medicare Advantage provisions in the 2019 Final Rule allowing targeted cost sharing and supplemental benefits for specific enrollee populations based on health status or disease (“With Concerns Over Readiness, MAOs Chose Stability Over Flexibility”, From RADAR on Medicare Advantage, reported in AIS Health Daily, June 14, 2018).
Another use case enabled by regulatory change is that normalized clinical data from clinical documents can now be used as standard supplemental data and combined with other clinical data for HEDIS reporting by health plans. New rules from the National Committee for Quality Assurance (NCQA) designate data from clinical documents as standard supplemental data if it is managed by a Qualified Clinical Data Repository and processed through an NCQA certified quality engine. Consequently, this data has a lower audit burden. The standard method to obtain clinical data for HEDIS has traditionally been chart chases, a resource intensive and costly process. Automating this process provides operational efficiency gains and cost savings.
Providers also have significant use cases for normalized clinical data from clinical documents. These include ensuring effective exchange of data among providers to enable coordination of care and creating a deduplicated multi-sourced longitudinal patient record. Providers can also use the data to optimize their coding for risk scores, which can be a key success factor in their risk-based contracts. They can use the data to support quality improvement and their population health programs.
This is not meant to be an exhaustive description of the growing list of use cases for normalized clinical data from clinical documents but does illustrate the diverse benefits that can be derived from such data by health plans, providers, and the analytic vendors that support them. Most important, the robust exchange of this data improves clinical care, patient safety and care coordination. The improvement of clinical care while making our health system more efficient is a goal that we all share.