Appl Clin Inform 2022; 13(04): 794-802
DOI: 10.1055/s-0042-1756368
Research Article

Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience

Dana M. Womack
1   Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
Edward J. Miech
2   Regenstrief Institute, Indianapolis, Indiana, United States
Nicholas J. Fox
1   Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
Linus C. Silvey
1   Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
Anna M. Somerville
1   Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
Deborah H. Eldredge
1   Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
Linsey M. Steege
3   School of Nursing, University of Wisconsin–Madison, Madison, Wisconsin, United States
› Author Affiliations
Funding This study was supported by the U.S. Agency for Healthcare Research and Quality (K12HS026370). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. The sponsors had no role in the study design, data collection, analysis, writing of the report, or decision to submit the article for publication.


Objectives The purpose of this study is to identify combinations of workplace conditions that uniquely differentiate high, medium, and low registered nurse (RN) ratings of appropriateness of patient assignment during daytime intensive care unit (ICU) work shifts.

Methods A collective case study design and coincidence analysis were employed to identify combinations of workplace conditions that link directly to high, medium, and low RN perception of appropriateness of patient assignment at a mid-shift time point. RN members of the study team hypothesized a set of 55 workplace conditions as potential difference makers through the application of theoretical and empirical knowledge. Conditions were derived from data exported from electronic systems commonly used in nursing care.

Results Analysis of 64 cases (25 high, 24 medium, and 15 low) produced three models, one for each level of the outcome. Each model contained multiple pathways to the same outcome. The model for “high” appropriateness was the simplest model with two paths to the outcome and a shared condition across pathways. The first path comprised of the absence of overtime and a before-noon patient discharge or transfer, and the second path comprised of the absence of overtime and RN assignment to a single ICU patient.

Conclusion Specific combinations of workplace conditions uniquely distinguish RN perception of appropriateness of patient assignment at a mid-shift time point, and these difference-making conditions provide a foundation for enhanced observability of nurses' work experience during hospital work shifts. This study illuminates the complexity of assessing nursing work system status by revealing that multiple paths, comprised of multiple conditions, can lead to the same outcome. Operational decision support tools may best reflect the complex adaptive nature of the work systems they intend to support by utilizing methods that accommodate both causal complexity and equifinality.

Protection of Human and Animal Subjects

This study was approved by the Oregon Health and Science University Institutional Review Board.

Publication History

Received: 05 March 2022

Accepted: 13 July 2022

Article published online:
31 August 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

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