From the 2022 HVPA National Conference
Anirudh Saraswathula MD, MS (Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine), April Taylor MS, MHA, Matthew Severson MS, Jenna Swann MS, Patricia Dasch BSN, RN, Cory Williamson MHA, Laura Marco BSN, RN, Redonda Miller MD, MBA, Allen Kachalia MD, JD
Background
Quality of care is a key element of the healthcare value equation, and significant challenges exist in every aspect of its measurement, reporting, and interpretation. In contemporary practice, a considerable portion of quality measurement comes from coding and administrative claims data, information routinely used not only for financial purposes, but also to report to third party entities measuring patient safety and quality. Coding compliance, however, a key assurance program in healthcare finance, may not optimally reflect how the quality of care is measured, particularly in risk-adjustment models. For example, the specific order or number of secondary codes or the use of “present on admission” flags may not significantly impact compliance but may considerably affect the patient encounter’s risk adjustment. Our institution follows Maryland’s Total Cost of Care model, an additional challenge, as reimbursement and quality measures reviewed by Maryland’s Health Services Cost Review Commission (HSCRC) use the All Patients Refined Diagnosis Related Groups (APR-DRG) coding schema while CMS and most national measures use Medicare Severity DRG (MS-DRG). This brings broad implications for healthcare organizations, affecting institutional reputation as well as financial returns on pay-for-performance programs.
Objective
To ensure that quality of care and value are measured appropriately, we set out to evaluate how well our organization’s current coding processes enable us to accurately reflect the complexity and quality of delivered care in our mandated quality data reporting.
Methods
Leaders were engaged from key departments: Clinical Documentation Excellence (CDE), Health Information Management (HIM), and Quality and Clinical Analytics. After determining the enterprise-level workflow for quality data reporting from coding and billing data, multi-disciplinary initiatives are in motion to improve quality data reporting.
Results
In detailed qualitative interviews with leaders from across the organization, 15 key steps were identified for quality data reporting from coding and billing workflows. These came from 6 categories: provider documentation, CDE review, HIM coding, QI review, bill holding, and rebilling. The most critically modifiable steps were found to be: (1) the DRG system selection for coding, (2) ensuring capture of all complications and comorbidities (CC), major CCs (MCC), and Elixhauser codes, and (3) ensuring that data captured by the CDE teams concurrently is effectively leveraged by HIM and QI in determining the final codeset. Thus, three institution-wide initiatives were implemented in addition to ongoing documentation improvement efforts and a robust query process: (1) a study to determine whether MS- or APR-DRG-based coding most accurately reflected care quality, (2) coding workflow modifications to improve capture of all relevant comorbidities and other characteristics in patient accounts, and (3) a more robust and textured coding reconciliation process beyond the DRG with prebill review at the individual code level.
Conclusion
A critical part of the value equation in modern healthcare administration, claims-based quality measurement is a challenging process fraught with complexities in data generation and interpretation. Maryland’s unique regulatory environment poses additional challenges. We present here a single institution’s efforts to evaluate and improve key aspects of its coding and billing workflows that impact quality data reporting.
