ArticleApplied clinical informatics2022
Dashboard Design to Identify and Balance Competing Risk of Multiple Hospital-Acquired Conditions.
Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Centralized Multipatient Dashboards' Impact on Intensive Care Unit Clinician Performance and Satisfaction: A Systematic Review.Applied clinical informatics · 2024Pooled it
- How Can Clinicians Decide if AI-Enabled Risk Prediction Models Are Fit for Purpose for Real World Use?Journal of evaluation in clinical practice · 2026Article
- Opportunities and Challenges Associated with the Pilot Implementation of Clinical Decision Support Systems in a Rural Hospital: A Qualitative Study.Applied clinical informatics · 2025Article
- Creation of an Interactive Dashboard to Facilitate Early Detection of Cardiac Amyloidosis in African American Veterans.Applied clinical informatics · 2025Article
- From Data to Decision: Design and Evaluation of a Patient Safety Dashboard.Medical journal of the Islamic Republic of Iran · 2025Article
- Impact of a Disease-Focused Electronic Health Record Dashboard on Clinical Staff Efficiency in Previsit Patient Review in an Ambulatory Pulmonary Hypertension Care Clinic.Applied clinical informatics · 2024Article
- Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Developing the VA Geriatric Scholars Programs' Clinical Dashboards Using the PDSA Framework for Quality Improvement.Applied clinical informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundHospital-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.
objectivesThe 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.
methodsThis 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.
resultsTwenty-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.
conclusionParticipants 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.