The electronic health record (EHR) contains a vast quantity of data due to its observation nature, holding great promise as a valuable, efficient, and cost-effective tool. These data can inform quality improvement and research initiatives, especially those related to medical resources and patient outcomes.[1–3]. In its initial implementation, however, the EHR rarely captures outcomes of interest to key stakeholders reliably and accurately due to frequent limitations resulting from disorganized, incorrect, or missing variables that lack vigorous extraction methodologies. Together this limits the data’s validity and utility. In order to provide validated results for scientific interpretation, vigorous, reproducible, and validated techniques must be established for each EHR variable of interest.
Many institutions rely on a structured repository of data, drawn from the EHR, to facilitate ongoing access, a so-called data warehouse or data mart.[5, 6] This data repository is frequently created after the EHR has been created and, at many institutions, is created and maintained by data analysts working in isolation from front-line clinicians. The intensive care unit (ICU) is a particularly challenging area for creation of a data mart. Critically ill patients suffer life threatening organ pathology in at least one, if not many, organ systems. These patients are generally intensively monitored, with very high frequency of physiologic data capture. Laboratory data may be obtained multiple times per day. Multiple organ support modalities may be employed, with complex documentation and monitoring to quantify the degree of support. Once data are located though, they can support surveillance, decision support, and modeling of outcomes.
We sought to define a methodology for the creation of a structured, rigorously validated intensive care unit (ICU) data mart based on data automatically and routinely derived from the EHR. We identified data elements commonly used for quality improvement and research purposes, including high-quality outcomes data, to refine the methodologies presented here. Importantly, data analysts and clinicians worked side-by-side throughout the process.