Design of an information volatility measure for health care decision making

  • Authors:

  • Monica Chiarini Tremblay;Alan R. Hevner;Donald J. Berndt

  • Affiliations:

  • Decision Sciences and Information Systems, College of Business Administration, Florida International University, 11200 SW 8th Street, RB 250, Miami, FL 33199, United States;Information Systems and Decision Sciences, College of Business, 4202 Fowler Ave., CIS1040, University of South Florida,, Tampa, FL 33620, United States;Information Systems and Decision Sciences, College of Business, 4202 Fowler Ave., CIS1040, University of South Florida,, Tampa, FL 33620, United States

  • Venue:
  • Decision Support Systems
  • Year:
  • 2012

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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.