Clinical documentation improvement as a quality metric in oncology.

Abstract

123 Background: With the rapid pace of healthcare reimbursement transformation from a fee-for-service to a fee-for-value system, clinical documentation is increasingly recognized as a critical area of focus for quality improvement. Engaging busy physicians to improve their clinical documentation is a challenging endeavor. Many hospitals utilize clinical documentation improvement specialists to query physicians for more specific and comprehensive clinical documentation. In this paper, we will report on a novel, complementary, and replicable approach we utilized to improve clinical documentation in the medical oncology clinical service line at the Mount Sinai Hospital in New York City. Methods: We organized a team of stakeholder physicians, clinical documentation specialists, clinical informaticists, and data analysts in July 2013 to address a perceived gap in publicly-reported severity-adjusted quality metrics in medical oncology. We used University Health System Consortium data to conduct a detailed comparative analysis of the rates of coding for common and important secondary ICD-9 diagnoses on inpatient claims by base Diagnosis Related Group (DRG). A list of 34 diagnoses was targeted for improvement in the accuracy and specificity of clinical documentation. We then developed an electronic medical record tool to assist clinicians in real-time clinical documentation. We rolled out a new quality metric with monthly reporting on CC/MCC (complication or comorbidity and major complication or comorbidity) capture rates to departmental leadership, provided educational outreach to front-line providers and developed additional non-electronic reminder tools for provider use. Results: We increased the overall CC/MCC capture rate from 88% to 94% and the case mix index (CMI) for the chemotherapy base MS-DRG from 1.11 to 1.28 from March 2013 to March 2014. The capture rate remained the same (6) or increased (18) in 82% of the targeted diagnoses. Conclusions: Adding clinical documentation metrics to the existing quality infrastructure, supported with comparative analytical data and EMR tools, can drive sustainable improvements in clinical documentation which supports publicly-reported severity-adjusted quality measures.