The Case for hbRecon: Comparing Clinical Registry Data and Claims-Based Auditing Tools

Learn how integrating claims-based audits with clinical registry data-based audits like hbRecon provides more actionable insights to optimize financial and clinical performance.

Conducting coding audits is crucial for healthcare organizations in maintaining accurate billing, compliance with regulations, revenue integrity, and quality improvement. In turn, audits provide insight into optimizing coding practices, ensuring the delivery of the highest-quality care, and identifying missed opportunities for appropriate reimbursement. When performed more granularly, audits allow for optimizing the coding processes, accurately capturing all eligible services and procedures while minimizing financial and legal risks.

Historically, claims-based audits have been the standard practice; however, registry data-based audits provide an alternative, nuanced insight into each episode of care and associated event. While claims-based solutions utilize billing data to investigate the revenue cycle journey, hbRecon uses clinical registry data, which provides unique insights into uncovering coding errors that lead to missed revenue opportunities.

The Difference Between Clinical Registry Data and Claims Data

So, how do clinical registry data and claims data differ? Clinical registry data and claims data are valuable information sources in the healthcare industry, but they serve different purposes and have distinct characteristics.

Clinical registry data from sources such as the American College of Cardiology (ACC), the Society of Thoracic Surgeons (STS), Extracorporeal Life Support Organization (ELSO), the American Heart Association (AHA), and State Organizations (COAP & CCORP), includes discrete data elements that contain detailed clinical information about patients, diagnoses, procedures, and outcomes. It is particularly useful in auditing the clinical processes and outcomes of specific medical procedures or conditions.

On the other hand, claims data refers to information collected from insurance claims submitted by healthcare providers for reimbursement purposes. This coded data source includes details about the services provided, such as procedures performed, diagnoses, medications prescribed, and associated costs. However, information for each claim is limited, and it doesn’t capture all aspects of a person’s treatment or health – many things must be inferred.1

Expanding Your Toolkit for Effective Auditing

Both clinical registry data and claims data are instrumental in medical data auditing. Claims data is typically used for financial audits and to assess the appropriateness and accuracy of billing and reimbursement practices, aiming to ensure coding accuracy, compliance with payer requirements and regulatory standards, and integrity of the submitted claims data. On the other hand, clinical registry data provides deeper clinical insight to determine if the claims’ codes appropriately represent the services provided and diagnoses assigned, revealing any missed opportunities for revenue capture due to miscoding. This increased specificity allows auditors to assess adherence to clinical guidelines, identify variations in care, and evaluate patient outcomes.

When segregated, the audits’ data sets lose efficacy. For example, a claims-based audit may determine the claim’s coding to be accurate, but this audit alone cannot determine if additional, necessary coding was omitted due to insufficient provider documentation or missteps in translating clinical language into coding language, especially around MCC and CC conditions. However, clinical registry data-based audits, analogous to the hbRecon-based audit, can reveal potentially missed diagnoses and/or treatment codes, reducing the risk of undercoding or missing revenue opportunities.

The integration of both audit types provides a more comprehensive and accurate assessment of healthcare quality, outcomes, and financial performance. With this synergistic approach to healthcare delivery, it’s possible to ensure that both clinical and financial aspects are aligned and optimized. This is where hbRecon’s use of clinical registry data in conjunction with claims data in the audit process demonstrates its distinctive superiority when uncovering errors in revenue capture.

Harness the Power of Clinical Registry Data With hbRecon

When it comes to revenue capture, the following factors demonstrate why clinical registry data offers superior results:

  1. Clinical Specificity and Coding Accuracy. hbRecon analyzes clinical registry data with detailed discrete & structured clinical information about a patient encounter, including diagnoses, procedures, treatments, and outcomes. This level of specificity allows for an accurate and comprehensive comparison of the services provided and the services billed, leading to improved revenue capture.
  2. Comprehensive Data. Clinical registry data often includes information beyond what is captured in claims data. It may include additional clinical measures, patient-reported outcomes, and quality indicators that provide a more comprehensive view of the patient’s condition and treatment. One clinical registry module alone can warehouse over 2,000 data elements in a single visit. With the hbRecon toolkit, this additional information can support accurate documentation and coding, leading to improved revenue capture.
  3. Quality Improvement. Clinical registry data is often used for quality improvement initiatives, clinical research, and benchmarking. These activities focus on optimizing patient outcomes and care processes, which can indirectly impact revenue capture by enhancing the quality and efficiency of care delivery. hbRecon allows users to review all relevant clinical registry, financial, and coding data in one location.
  4. Patient Acuity. This is where hbRecon’s use of clinical registry data shines again, capturing a better picture of the patient’s acuity – because if the coding is wrong, patients may appear healthier or sicker – lending to a more accurate CMI. From there, hbRecon translates that into the associated diagnosis-related group (DRG) code, adding a CC or MCC when applicable. This benefits the cardiovascular population by better reflecting their actual patient acuity, CMI, and severity of illness, which makes way for appropriate quality improvement initiatives and medically indicated clinical research, ultimately enhancing patient outcomes and care processes.
    Beth Kennalley, a quality professional with one of hbRecon’s major hospital clients, shared what she found most valuable about implementing hbRecon in terms of efficient and accurate billing: “The ability to be able to compare two independent chart abstractions is invaluable but would take too much time to do manually to be useful. This tool allows us to do this, find multiple errors quickly, and promptly submit this information for correction. The tool has reassured us that our billing is accurate in these areas.”

Beth shared that hbRecon’s monthly review of abstraction and coding differences has taken less and less time, and her organization has improved performance in both areas. Efficiency is critical to her ability to quickly and accurately review both data sets to ensure they accurately reflect the case performed.

hbRecon: Translating Clinical Data for Coding Specificity

hbRecon’s phased approach for clinical registry data auditing is a valuable tool for increasing revenue capture with its unique clinical registry and coding data dataset, providing detailed and specific clinical information about patient encounters, allowing for accurate and complete coding based on the documentation of services provided.

Is your healthcare organization ready to discover alternate revenue streams through the hbRecon toolkit? Our platform can integrate clinical registry and coding data sources to algorithmically analyze and determine recommended billing codes and identify probable coding mismatches and rebilling opportunities.

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MIT OpenCourseWare (Dec 13, 2018) 4.3.3 Healthcare Costs – Video 2: Claims Data

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