Abstract
Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the ‘Historical Decision’ bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges.
Highlights
Proper use of clinical information is especially important in an effort to make sound clinical decisions and provide quality health services [1]
It is critical that Clinical Decision Support Systems (CDSS) simulate the cognition of clinical decision makers; health data become useful when combined with human knowledge and experience
CDSS designers can annotate such groups of variables to co-exist as predictors
Summary
Proper use of clinical information is especially important in an effort to make sound clinical decisions and provide quality health services [1]. A variety of information is combined by healthcare professionals who arrive at clinical conclusions [2] These decisions rely on information which once acquired, is further processed by healthcare professionals’ cognitive skills, such as in the differential diagnosis [3]. One highly emerging focus area of medical informatics is to improve care delivery of in-hospital patients with the development of data-driven, patient-centered decision support systems. Development of such systems is a highly demanding and multidisciplinary task that requires the integration of knowledge from the clinical domain, and decision science to adapt the CDSS to the hospital practice and clinical work flows [4].
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