E-Health Decision Support Technologies in the Prevention and Management of Pressure Ulcers: A Systematic Review
Abstract
Pressure ulcers are problematic across clinical settings, negatively impacting patient morbidity and mortality while resulting in substantial costs to the healthcare system. E-health clinical decision support technologies can play a key role in improving pressure ulcer-related outcomes. This systematic review aimed to assess the impact of electronic health decision support interventions on pressure ulcer management and prevention. A systematic search was conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, and Cochrane. Nineteen articles, published from 2010 to 2020, were included for review. The findings of this review showed promising results regarding the usability and accuracy of electronic health decision support tools to aid in pressure ulcer prevention and management. Evidence indicated improved clinician adherence to pressure ulcer prevention practices and decreased healthcare costs postimplementation of an electronic health decision support intervention. However, the studies included in this review did not consistently show reductions in pressure ulcer prevalence, incidence, or risk. Most of the articles included in the review were limited by small sample sizes drawn from single hospitals or long-term care homes. More high-quality studies are needed to determine the types of electronic health decision support tools that can drive sustainable improvements to patient outcomes.
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ML identification of patients with a high risk of pressure ulcers or falls can trigger CDS for nursing interventions.
“For example, ML identification of patients with a high risk of pressure ulcers51,52 or falls53 can trigger CDS for nursing interventions.”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [51]
Cites
An overview of clinical decision support systems: Benefits, risks, and strategies for success
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0221-y · Cited by 3,019 · see citing works
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Potential uses of AI for perioperative nursing handoffs: a qualitative study
King, C. R., Shambe, A., & Abraham, J. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad015 · Cited by 27 · see citing works
“42 50 For example, ML identification of patients with a high risk of pressure ulcers 51,52 or falls 53 can trigger CDS for nursing interventions.”
Background
- Authors
- Ting, J. J., & Garnett, A.
- Affiliations
- Western University (CA)
- Venue
- CIN: Computers, Informatics, Nursing
- Published
- 2021-06-16
- Volume
- 39
- Issue
- 12
- Pages
- 955-973
- ISSN
- 1538-2931 (ISSN-L)
- 1538-9774
- OpenAlex
- W3167390333
- Cited by
- 11
- References
- 1
- Citations by year
- 2025: 5 · 2024: 4 · 2023: 1 · 2022: 1
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- CIN Computers Informatics Nursing (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Occupational Therapy › Pressure Ulcer Prevention and Management
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- pressure ulcers, clinical decision support systems, pressure ulcer prevention, pressure ulcer treatment, clinician adherence, health care costs, usability, long-term care facilities
- MeSH terms
- Pressure Ulcer, Humans, Long-Term Care, Telemedicine, Decision Support Systems, Clinical
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