Abstract
Clinical decision-making is crucial for quality health care, and mistakes in clinical decision-making can contribute to medical errors. Medical errors can then lead to adverse health and cost implications. Therefore, developing Clinical Decision Support Systems (CDSS) to aid clinical decision-making has been a well-established practice over time. Along this line, CDSS of various capacities, some even in the form of Smartphone Apps, have emerged lately. With the emergence of such diverse CDSS and also increasing demand for better quality health care, there tends to arise a need for the use of digital health capabilities to not only support making better clinical decisions but also to enable the capture of data to lay a foundation and support data-driven advancements in health care. In this light, it is important as a first step to have a structured approach to scope out different CDSS we encounter according to their limits and capabilities, as such a scoping can serve as a useful guide for designing, improving and also planning meaningful use of data that may be produced. Such a scoping strategy has not yet been well established in extant literature. As such, we contribute to that void by presenting in this chapter a suggested approach to scope (or classify) CDSS based on criteria we find useful. The criteria we have presented have been found through literature and also our experience gained along with an ongoing Australian study about a Smartphone App-based CDSS. After presenting the scoping strategy, we demonstrate how it can be put into practice through a case study involving an existing Smartphone App-based CDSS. With the aid of the scoping, we also map out the available data capture opportunities and also point out some noteworthy limitations.