Improving Billing Accuracy Through Enterprise-Wide Standardized Structured Reporting With Cross-Divisional Shared Templates.

OBJECTIVE: We describe our experience in implementing enterprise-wide standardized structured reporting for chest radiographs (CXRs) via change management strategies and assess the economic impact of structured template adoption. METHODS: Enterprise-wide standardized structured CXR reporting was implemented in a large urban health care enterprise in two phases from September 2016 to March 2019: initial implementation of division-specific structured templates followed by introduction of auto launching cross-divisional consensus structured templates. Usage was tracked over time, and potential radiologist time savings were estimated. Correct-to-bill (CTB) rates were collected between January 2018 and May 2019 for radiography. RESULTS: CXR structured template adoption increased from 46% to 92% in phase 1 and to 96.2% in phase 2, resulting in an estimated 8.5 hours per month of radiologist time saved. CTB rates for both radiographs and all radiology reports showed a linearly increasing trend postintervention with radiography CTB rate showing greater absolute values with an average difference of 20% throughout the sampling period. The CTB rate for all modalities increased by 12%, and the rate for radiography increased by 8%. DISCUSSION: Change management strategies prompted adoption of division-specific structured templates, and exposure via auto launching enforced widespread adoption of consensus templates. Standardized structured reporting resulted in both economic gains and projected radiologist time saved.

Big Data and the Future of Radiology Informatics.

Rapid growth in the amount of data that is electronically recorded as part of routine clinical operations has generated great interest in the use of Big Data methodologies to address clinical and research questions. These methods can efficiently analyze and deliver insights from high-volume, high-variety, and high-growth rate datasets generated across the continuum of care, thereby forgoing the time, cost, and effort of more focused and controlled hypothesis-driven research. By virtue of an existing robust information technology infrastructure and years of archived digital data, radiology departments are particularly well positioned to take advantage of emerging Big Data techniques. In this review, we describe four areas in which Big Data is poised to have an immediate impact on radiology practice, research, and operations. In addition, we provide an overview of the Big Data adoption cycle and describe how academic radiology departments can promote Big Data development.