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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Telling Stories with Data.

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This study introduces a three-component system to analyze clinical ethics consult data, creating meta-narratives for performance assessment and demonstrating value. This approach enhances the evidence-based legitimacy of ethics consultations.

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Area of Science:

  • Clinical Ethics
  • Health Informatics
  • Data Science

Background:

  • Ethics consults provide valuable narratives but lack standardized data for performance assessment.
  • There's a perceived conflict between rich narrative fidelity and data-driven value demonstration.
  • Existing methods struggle to aggregate and analyze case data while preserving nuance.

Purpose of the Study:

  • To present a novel three-component system for analyzing clinical ethics consult data.
  • To enable data-driven assessment of ethics consult performance and value.
  • To bridge the gap between narrative richness and analytical rigor in clinical ethics.

Main Methods:

  • Development of the Armstrong Clinical Ethics Coding System (ACECS) with six codes for simplified case narratives.
  • Utilization of a data model to analyze aggregated case data, identify patterns, and uncover insights.
  • Application of data storytelling techniques for visual representation of findings.

Main Results:

  • The ACECS generates meta-narratives that can be aggregated and analyzed.
  • The data model reveals relationships and patterns not apparent in narratives alone.
  • Visualizations effectively demonstrate the value of consult services to stakeholders.

Conclusions:

  • The system allows for detailed, comparable descriptions of consult service work while maintaining case-level nuance.
  • Broad adoption can support the legitimacy of clinical ethics practice through evidence-based methods.
  • This approach aligns clinical ethics with other data-driven healthcare domains.