Journal ArticleDOI
Design of an information volatility measure for health care decision making
Monica Chiarini Tremblay,Alan R. Hevner,Donald J. Berndt +2 more
- Vol. 52, Iss: 2, pp 331-341
Reads0
Chats0
TLDR
An Information Volatility Measure (IVM) is proposed to complement business intelligence (BI) tools when considering aggregated data (intra-cell) or when observing trends in data (inter-cell), drawn from volatility measures found in the field of finance.Abstract:
Health care decision makers and researchers often use reporting tools (e.g. Online Analytical Processing (OLAP)) that present data aggregated from multiple medical registries and electronic medical records to gain insights into health care practices and to understand and improve patient outcomes and quality of care. An important limitation is that the data are usually displayed as point estimates without full description of the instability of the underlying data, thus decision makers are often unaware of the presence of outliers or data errors. To manage this problem, we propose an Information Volatility Measure (IVM) to complement business intelligence (BI) tools when considering aggregated data (intra-cell) or when observing trends in data (inter-cell). The IVM definitions and calculations are drawn from volatility measures found in the field of finance, since the underlying data in both arenas display similar behaviors. The presentation of the IVM is supplemented with three types of benchmarking to support improved user understanding of the measure: numerical benchmarking, graphical benchmarking, and categorical benchmarking. The IVM is designed and evaluated using exploratory and confirmatory focus groups.read more
Citations
More filters
Journal ArticleDOI
Implementing routine outcome monitoring in clinical practice: Benefits, challenges, and solutions
TL;DR: The benefits, obstacles, and challenges that can hinder (and have hindered) implementation of routine outcome monitoring in clinical practice are reviewed.
Journal ArticleDOI
Towards an implementation framework for business intelligence in healthcare
Neil Foshay,Craig E. Kuziemsky +1 more
TL;DR: A framework for defining and prioritizing decision-support information needs in the context of healthcare-specific processes is created, finding strong management support, the right skill sets and an information-oriented culture to be key implementation considerations.
Journal Article
Editor's comments: riding the wave: past trends and future directions for health IT research
Journal ArticleDOI
Healthcare support for underserved communities using a mobile social media platform
TL;DR: This paper designs and evaluates an innovative mobile decision support system (MDSS) solution that connects underserved rural patients in Bangladesh to general practitioners (GPs) to evaluate patient conditions virtually and provide answers for further diagnosis or treatment.
References
More filters
Book
Judgment Under Uncertainty: Heuristics and Biases
Amos Tversky,Daniel Kahneman +1 more
TL;DR: The authors described three heuristics that are employed in making judgements under uncertainty: representativeness, availability of instances or scenarios, and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.
Book
The Sciences of the Artificial
TL;DR: A new edition of Simon's classic work on artificial intelligence as mentioned in this paper adds a chapter that sorts out the current themes and tools for analyzing complexity and complex systems, taking into account important advances in cognitive psychology and the science of design while confirming and extending Simon's basic thesis that a physical symbol system has the necessary and sufficient means for intelligent action.
Journal ArticleDOI
Design science in information systems research
TL;DR: The objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research.
Journal ArticleDOI
Judgment Under Uncertainty: Heuristics and Biases.
TL;DR: Three heuristics that are employed in making judgements under uncertainty are described: representativeness, availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development.