We describe fieldwork in which we studied hospital ICU physicians and their strategies and documentation aids for composing patient progress notes. We then present a clinical documentation prototype, activeNotes, that supports the creation of these notes, using techniques designed based on our fieldwork. ActiveNotes integrates automated, context-sensitive patient data retrieval, and user control of automated data updates and alerts via tagging, into the documentation process. We performed a qualitative study of activeNotes with 15 physicians at the hospital to explore the utility of our information retrieval and tagging techniques. The physicians indicated their desire to use tags for a number of purposes, some of them extensions to what we intended, and others new to us and unexplored in other systems of which we are aware. We discuss the physicians' responses to our prototype and distill several of their proposed uses of tags: to assist in note content management, communication with other clinicians, and care delivery.

OBJECTIVE: The authors present a system that scans electronic records from cardiac surgery and uses inference rules to identify and classify abnormal events (e.g., hypertension) that may occur during critical surgical points (e.g., start of bypass). This vital information is used as the content of automatically generated briefings designed by MAGIC, a multimedia system that they are developing to brief intensive care unit clinicians on patient status after cardiac surgery. By recognizing patterns in the patient record, inferences concisely summarize detailed patient data. DESIGN: The authors present the development of inference rules that identify important information about patient status and describe their implementation and an experiment they carried out to validate their correctness. The data for a set of 24 patients were analyzed independently by the system and by 46 physicians. MEASUREMENTS: The authors measured accuracy, specificity, and sensitivity by comparing system inferences against physician judgments, in cases where all three physicians agreed and against the majority opinion in all cases. RESULTS: For laboratory inferences, evaluation shows that the system has an average accuracy of 98 percent (full agreement) and 96 percent (majority model). An analysis of interrater agreement, however, showed that physicians do not agree on abnormal hemodynamic events and could not serve as a gold standard for evaluating hemodynamic events. Analysis of discrepancies reveals possibilities for system improvement and causes of physician disagreement. CONCLUSIONS: This evaluation shows that the laboratory inferences of the system have high accuracy. The lack of agreement among physicians highlights the need for an objective quality-assurance tool for hemodynamic inferences. The system provides such a tool by implementing inferencing procedures established in the literature.