Appl Clin Inform 2022; 13(03): 621-631
DOI: 10.1055/s-0042-1749598
Research Article

Dashboard Design to Identify and Balance Competing Risk of Multiple Hospital-Acquired Conditions

Mary Beth Flynn Makic
1   University of Colorado, College of Nursing, Aurora, Colorado, United States
Kathleen R. Stevens
2   School of Nursing, University of Texas Health Science Center San Antonio, San Antonio, Texas, United States
R. Mark Gritz
3   Division of Health Care Policy and Research, School of Medicine, University of Colorado Denver, Aurora, Colorado, United States
Heidi Wald
4   SCL Health, Denver, Colorado, United States
Judith Ouellet
3   Division of Health Care Policy and Research, School of Medicine, University of Colorado Denver, Aurora, Colorado, United States
Cynthia Drake Morrow
5   Health Systems, Management and Policy, Colorado School of Public Health, Aurora, Colorado, United States
David Rodrick
6   Center for Quality Improvement and Patient Safety, Agency for Healthcare Research and Quality, Rockville, Maryland, United States
Blaine Reeder
7   University of Missouri Health, Sinclair School of Nursing and MU Institute for Data Science and Informatics, School of Nursing, Columbia, Missouri, United States
› Institutsangaben
Funding This study was supported under a contract with the Agency for Healthcare Research and Quality (AHRQ) Contract HHSP2332015000251 and HHSP23337003T.


Background Hospital-acquired conditions (HACs) are common, costly, and national patient safety priority. Catheter-associated urinary tract infections (CAUTIs), hospital-acquired pressure injury (HAPI), and falls are common HACs. Clinicians assess each HAC risk independent of other conditions. Prevention strategies often focus on the reduction of a single HAC rather than considering how actions to prevent one condition could have unintended consequences for another HAC.

Objectives The objective of this study is to design an empirical framework to identify, assess, and quantify the risks of multiple HACs (MHACs) related to competing single-HAC interventions.

Methods This study was an Institutional Review Board approved, and the proof of concept study evaluated MHAC Competing Risk Dashboard to enhance clinicians' management combining the risks of CAUTI, HAPI, and falls. The empirical model informing this study focused on the removal of an indwelling urinary catheter to reduce CAUTI, which may impact HAPI and falls. A multisite database was developed to understand and quantify competing risks of HACs; a predictive model dashboard was designed and clinical utility of a high-fidelity dashboard was qualitatively tested. Five hospital systems provided data for the predictive model prototype; three served as sites for testing and feedback on the dashboard design and usefulness. The participatory study design involved think-aloud methods as the clinician explored the dashboard. Individual interviews provided an understanding of clinician's perspective regarding ease of use and utility.

Results Twenty-five clinicians were interviewed. Clinicians favored a dashboard gauge design composed of green, yellow, and red segments to depict MHAC risk associated with the removal of an indwelling urinary catheter to reduce CAUTI and possible adverse effects on HAPI and falls.

Conclusion Participants endorsed the utility of a visual dashboard guiding clinical decisions for MHAC risks preferring common stoplight color understanding. Clinicians did not want mandatory alerts for tool integration into the electronic health record. More research is needed to understand MHAC and tools to guide clinician decisions.


The findings and conclusions in this document are those of the author(s) who are responsible for the content and do not necessarily represent the views of AHRQ. No statement in this document should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services. Identifiable information on which this publication is based is protected by federal law, Section 934(c) of the Public Health Service Act, 42 USC 299c-3(c). No identifiable information about any individuals or entities supplying the information or described in it may be knowingly used except in accordance with their prior consent. Any confidential identifiable information in this publication that is knowingly disclosed is disclosed solely for the purpose for which it was provided. C.D.M. was a PhD student at the University of Colorado School of Public Health throughout the study period. She is now employed by IBM Watson Health, Ann Arbor, MI.

Protection of Human and Animal Subjects

All study procedures were approved by an academic institutional review board, the Colorado Multiple Institutional Review Board, (#16-2020) Aurora, CO. Study participants provided verbal informed consent and received a $50 gift card incentive for participation.


Eingereicht: 05. Dezember 2021

Angenommen: 27. April 2022

Artikel online veröffentlicht:
08. Juni 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany