Related Experiment Video
Updated: Nov 17, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Measuring the Masses: Mass-Gathering Medical Case Reporting, Conceptual Modeling - The DREAM Model (Paper 5)
Adam Lund1,2,3, Sheila Turris1,2, Haddon Rabb2
1Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Introduction:
Without a robust evidence base to support recommendations for first aid, health, and medical services at mass gatherings (MGs), levels of care will continue to vary. Streamlining and standardizing post-event reporting for MG medical services could improve inter-event comparability, and prospectively influence event safety and planning through the application of a research template, thereby supporting and promoting growth of the evidence base and the operational safety of this discipline. Understanding the relationships between categories of variables is key. The present paper is focused on theory building, providing an evolving conceptual model, laying the groundwork for exploring the relationships between categories of variables pertaining the health outcomes of MGs.
Methods:
A content analysis of 54 published post-event medical case reports, including a comparison of the features of published data models for MG health outcomes.
Findings:
A layered model of essential conceptual components for post-event medical reporting is presented as the Data Reporting, Evaluation, & Analysis for Mass-Gathering Medicine (DREAM) model. This model is relational and embeds data domains, organized operationally, into "inputs," "modifiers," "actuals," and "outputs" and organized temporally into pre-, during, post-event, and reporting phases.
Discussion:
Situating the DREAM model in relation to existing models for data collection vis a vis health outcomes, the authors provide a detailed discussion on similarities and points of difference.
Conclusion:
Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.
More Related Videos
09:20Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
Data Collection III
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
Mass and Weight
Methods of Documentation VI: Case Management Model
For example, a patient with a chronic...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Mass Analyzers: Overview
Mass Analyzers: Common Types