Clinical practice

Graphical summary of patient status

A well-designed visualization can be rapidly interpreted and understood. The advantages of visual representations of quantitative data and meaningful integration of related patient information comes at the cost of much effort in identifying the right information to group together, how to present it, to whom, and in what context. Even small gains from these efforts become great when multiplied by many users making critical health care decisions. In this chapter, we draw on theoretical models of human cognition and visual perception, understanding of workflow and information needs in the context of clinical applications, and user-centered design methods to promote the design of effective information integration and visualization. We provide a grounding in how humans use vision to think which has led to the development of principles to guide the design of information integration and visualization. We present design methods, including contextual inquiry and participatory design activities, relevant to understanding clinical information integration and visualization needs. Finally, in a world of increasing information overload, we review examples of research and design efforts to identify and artfully present the most relevant patient information to support a variety of clinical decisions such as prioritizing resources to patients with the most urgent needs, detection of missed actions, and making the right treatment choices.

Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not natively support patient overviews from customizable CDS routines, but local physicians indicated a demand for multi-patient tabular overviews of important clinical parameters such as key laboratory measurements. As our PDMS installation provides Arden Syntax support, we set out to explore the capability of Arden Syntax for multi-patient CDS by implementing a prototypical dashboard for visualizing laboratory findings from patient sets.

Our implementation leveraged the object data type, supported by later versions of Arden, which turned out to be serviceable for representing complex input data from several patients. For our prototype, we designed a modularized architecture that separates the definition of technical operations, in particular the control of the patient context, from the actual clinical knowledge. Individual Medical Logic Modules (MLMs) for processing single patient attributes could then be developed according to well-tried Arden Syntax conventions.

We successfully implemented a working dashboard prototype entirely in Arden Syntax. The architecture consists of a controller MLM to handle the patient context, a presenter MLM to generate a dashboard view, and a set of traditional MLMs containing the clinical decision logic. Our prototype could be integrated into the graphical user interface of the local PDMS. We observed that with realistic input data the average execution time of about 200 ms for generating dashboard views attained applicable performance.

Our study demonstrated the general feasibility of creating multi-patient CDS routines in Arden Syntax. We believe that our prototypical dashboard also suggests that such implementations can be relatively easy, and may simultaneously hold promise for sharing dashboards between institutions and reusing elementary components for additional dashboards.