Integrative review of clinical decision support for registered nurses in acute care settings
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Objective To report on the state of the science of clinical decision support (CDS) for hospital bedside nurses. Materials and methods We performed an integrative review of qualitative and quantitative peer-reviewed original research studies using a structured search of PubMed, Embase, Cumulative Index to Nursing and Applied Health Literature (CINAHL), Scopus, Web of Science, and IEEE Xplore (Institute of Electrical and Electronics Engineers Xplore Digital Library). We included articles that reported on CDS targeting bedside nurses and excluded in stages based on rules for titles, abstracts, and full articles. We extracted research design and methods, CDS purpose, electronic health record integration, usability, and process and patient outcomes. Results Our search yielded 3157 articles. After removing duplicates and applying exclusion rules, 28 articles met the inclusion criteria. The majority of studies were single-site, descriptive or qualitative (43%) or quasi-experimental (36%). There was only 1 randomized controlled trial. The purpose of most CDS was to support diagnostic decision-making (36%), guideline adherence (32%), medication management (29%), and situational awareness (25%). All the studies that included process outcomes (7) and usability outcomes (4) and also had analytic procedures to detect changes in outcomes demonstrated statistically significant improvements. Three of 4 studies that included patient outcomes and also had analytic procedures to detect change s…
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Our findings can also be related to work with dashboards intended to detect change in patient status which lack explicit AI predictions.
“Our findings can also be related to work with dashboards intended to detect change in patient status which lack explicit AI predictions.54”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [54]
Our work contrasts with much of the development of EHR AI support for nurses, which largely focuses on medication documentation, medication administration, and very simple rule-based systems to identify specific nursing needs.
“Our work contrasts with much of the development of EHR AI support for nurses,54 which largely focuses on medication documentation, medication administration, and very simple rule-based systems to identify specific nursing needs.”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [54]
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A Web-Based Decision Support System for Predicting Readmission of Pneumonia Patients after Discharge
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Nurse and Physician Perceptions and Decision Making During Interdisciplinary Communication
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HOSPITAIS PRIVADOS NO BRASIL E CANADÁ
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Deep Learning-Based Decision Support System for Nurse Staff in Hospitals
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- Authors
- Dunn Lopez, K., Gephart, S. M., Raszewski, R., Sousa, V., Shehorn, L. E., & Abraham, J.
- Affiliations
- Department of Health Systems Science, College of Nursing, University of Illinois at Chicago
- University of Arizona (US)
- University of Illinois Chicago (US)
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-06-21
- Volume
- 24
- Issue
- 2
- Pages
- 441-450
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7651925
- OpenAlex
- W2469520700
- Cited by
- 83
- References
- 69
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 11 · 2023: 12 · 2022: 11 · 2021: 8 · 2020: 7 · 2019: 6 · 2018: 12 · 2017: 6
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agency for Healthcare Research and Quality (1K08HS022908-01A1)
- Robert Wood Johnson Foundation Nurse Faculty Scholars Program
- Brazilian National Council for Scientific and Technological Development
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- clinical decision support, bedside nurses, registered nurses, acute care, electronic health records, usability, guideline adherence, medication management, diagnostic decision-making, situational awareness, patient outcomes, process outcomes, quality of care
- MeSH terms
- Diagnosis, Computer-Assisted, Hospitals, Humans, Nurses, Information Storage and Retrieval, Point-of-Care Systems, Decision Support Systems, Clinical, Nursing Informatics
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