Frontline providers yearn for seamless data flow at the point of care, and are often frustrated by the lack of access to electronic data that exists within and beyond their health networks. Achieving efficient, affordable and cohesive data sharing is the holy grail of value-based care initiatives, but the sheer volume of data poses particular challenges.
Best-of-breed and single-vendor legacy, mainframe or SaaS applications—when connected—could collectively represent much more than 1 billion lines of code and hundreds to thousands of standards.
In addition, data is formatted and archived in a variety of incompatible file types and numerous data sets that even the most diligent cybercriminal would find challenging to hack. While maturing standardization tools are gaining laudable traction, alignment disparities persist.
Structured data from acute and ambulatory electronic health records (EHRs) and transactional clinical and financial systems—pre-adjudicated and adjudicated hospital and pharmacy claims, hospital admission discharges, lab tests and much more—require high-level interoperability. “Crosswalking,” a computer science task, can enable seamless data connection between systems.
Crosswalking breaks down equivalent or near-equivalent code sets and other data elements such as demographics of providers, payers and patients to enable mapping of healthcare data standards: HL7, LOINC, SNOMED, CMS regulations, HIPAA, NLM, Medicare, Joint Commission, CPT, HCPCS, NQF, NDC and ICD-10. A core component of effective interoperability, crosswalking uses a software logic that generates more valuable data while qualifying the identification and accuracy of every data source.
Crosswalking’s automated mapping of equivalent, identical or similar information—overlaid with standardized definitions—can easily pair data with codes, which is the heart of health IT data conversion projects. Crosswalking also contributes to recalibrating “wacky data files” and normalizing the data to reduce redundancies and improve integrity.
After the large-scale data is crosswalked to standards, providers can use the data to populate benchmark reports, data repositories and other analytical and business intelligence activities that support, for example, population health management strategies and care management initiatives. Business rules can be used to measure the effectiveness of interventions or absence of care, or to create financial incentives. In short, the crosswalked, single data set is simplified and scalable to perform many more meaningful analytic activities.
Crosswalking large-scale data to achieve comprehensive integration makes good clinical and business sense in the universal effort to transform care delivery through innovative approaches. For example, against the backdrop of the Meaningful Use Stage 2 mandate requiring HL7 Consolidated Clinical Document Architecture (CCDA), many hospitals and health systems using enterprise EHRs only recently started to generate good data sets, though variability in implementation (omissions, unstructured content, etc.) remain a challenge. Crosswalking that data with a plethora of other data sources can help organizations advance to a higher level of compliance and accountability throughout the care coordination process.
Likewise, a single-source view into financial and risk management data is also in demand. The rising sophistication of ACO operations, coupled with new value-based performance contracting between groups of ambulatory practices and payers, creates another business case for crosswalking. The unified data can be used to develop alternate payment structures to share risk, incentivize and promote care quality, and reduce costs.
In using a crosswalk strategy, organizations can master the management of clinical and financial data across several large domains, making the information holistic, scalable and useful. With good data, there is no limit to what can be accomplished to promote positive health outcomes.
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