ArticlePerspectives in health information managemen2017
An Electronic Health Record Data-driven Model for Identifying Older Adults at Risk of Unintentional Falls.
Article in Perspectives in health information managemen, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- The use of predictive fall models for older adults receiving aged care, using routinely collected electronic health record data: a systematic review.BMC geriatrics · 2022Pooled it
- Determining the Importance of Clinical Modalities for NeuroDegenerative Disorders and Risk of Patient Injury Using Machine Learning and Survival Analysis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Using Health Information Technology to Create Pathways for Hepatitis C Treatment and Cure in West Virginia.Perspectives in health information managemen · 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
9 authors at 1 institution in 1 country.
Funding
Abstract
Screening for risk of unintentional falls remains low in the primary care setting because of the time constraints of brief office visits. National studies suggest that physicians caring for older adults provide recommended fall risk screening only 30 to 37 percent of the time. Given prior success in developing methods for repurposing electronic health record data for the identification of fall risk, this study involves building a model in which electronic health record data could be applied for use in clinical decision support to bolster screening by proactively identifying patients for whom screening would be beneficial and targeting efforts specifically to those patients. The final model, consisting of priority and extended measures, demonstrates moderate discriminatory power, indicating that it could prove useful in a clinical setting for identifying patients at risk of falls. Focus group discussions reveal important contextual issues involving the use of fall-related data and provide direction for the development of health systems-level innovations for the use of electronic health record data for fall risk identification.
Indexed as
Identifiers
29118679PMC5653950W2985704422What 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.