CKD as a Model for Improving Chronic Disease Care through Electronic Health Records.

Electronic health records have the potential to improve the care of patients with chronic medical conditions. CKD provides a unique opportunity to show this potential: the disease is common in the United States, there is significant room to improve CKD detection and management, CKD and its related conditions are defined primarily by objective laboratory data, CKD care requires collaboration by a diverse team of health care professionals, and improved access to CKD-related data would enable identification of a group of patients at high risk for multiple adverse outcomes. However, to realize the potential for improvement in CKD-related care, electronic health records will need to provide optimal functionality for providers and patients and interoperability across multiple health care settings. The goal of the National Kidney Disease Education Program Health Information Technology Working Group is to enable and support the widespread interoperability of data related to kidney health among health care software applications to optimize CKD detection and management. Over the course of the last 2 years, group members met to identify general strategies for using electronic health records to improve care for patients with CKD. This paper discusses these strategies and provides general goals for appropriate incorporation of CKD-related data into electronic health records and corresponding design features that may facilitate (1) optimal care of individual patients with CKD through improved access to clinical information and decision support, (2) clinical quality improvement through enhanced population management capabilities, (3) CKD surveillance to improve public health through wider availability of population-level CKD data, and (4) research to improve CKD management practices through efficiencies in study recruitment and data collection. Although these strategies may be most effectively applied in the setting of CKD, because it is primarily defined by laboratory abnormalities and therefore, an ideal computable electronic health record phenotype, they may also apply to other chronic diseases.