Evidence map›Paper›PMID 36111269›Full record

ArticleSSM - population health2022

Identifying individual social risk factors using unstructured data in electronic health records and their relationship with adverse clinical outcomes.

S Michaela Rikard, Bommae Kim, Jonathan D Michel, Shayn M Peirce, Laura E Barnes, Michael D Williams

Open access · goldAbstract read
In one paragraph

Article in SSM - population health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 20% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

S Michaela RikardDepartment of Biomedical Engineering, University of Virginia, USA.
Bommae KimDepartment of Quality and Performance Improvement, University of Virginia Health System, USA.
Jonathan D MichelDepartment of Quality and Performance Improvement, University of Virginia Health System, USA.
Shayn M PeirceDepartment of Biomedical Engineering, University of Virginia, USA.
Laura E BarnesDepartment of Systems and Information Engineering, University of Virginia, USA.
Michael D WilliamsSchool of Medicine, University of Virginia, USA.
University of Virginia · USUniversity of Virginia Health System · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To determine the prevalence of individual-level social risk factors documented in unstructured data from electronic health records (EHRs) and the relationship between social risk factors and adverse clinical outcomes. Study setting: Inpatient encounters for adults (≥18 years) at the University of Virginia Medical Center during a 12-month study period between July 2018 and June 2019. Inpatient encounters for labor and delivery patients were excluded, as well as encounters where the patient was discharged to hospice, left against medical advice, or expired in the hospital. The study population included 21,402 inpatient admissions, representing 15,116 unique patients who had at least one inpatient admission during the study period. Study design: We identified measures related to individual social risk factors in EHRs through existing workflows, flowsheets, and clinical notes. Multivariate binomial logistic regression was performed to determine the association of individual social risk factors with unplanned inpatient readmissions, post-discharge emergency department (ED) visits, and extended length of stay (LOS). Other predictors included were age, sex, severity of illness, location of residence, and discharge destination. Results: Predictors of 30-day unplanned readmissions included severity of illness (OR = 3.96), location of residence (OR = 1.31), social and community context (OR = 1.26), and economic stability (OR = 1.37). For 30-day post-discharge ED visits, significant predictors included location of residence (OR = 2.56), age (OR = 0.60), economic stability (OR = 1.39), education (OR = 1.38), social and community context (OR = 1.39), and neighborhood and built environment (OR = 1.61). For extended LOS, significant predictors were age (OR = 0.51), sex (OR = 1.18), severity of illness (OR = 2.14), discharge destination (OR = 2.42), location of residence (OR = 0.82), economic stability (OR = 1.14), neighborhood and built environment (OR = 1.31), and education (OR = 0.79). Conclusions: Individual-level social risk factors are associated with increased risk for unplanned hospital readmissions, post-discharge ED visits, and extended LOS. While individual-level social risk factors are currently documented on an ad-hoc basis in EHRs, standardized SDoH screening tools using validated metrics could help eliminate bias in the collection of SDoH data and facilitate social risk screening.

Indexed as

EHR, electronic health recordsElectronic health recordsLOS, length of staySDoH, social determinants of healthSocial determinants of healthSocial risk factors

Identifiers

PMID36111269
PMCPMC9467895
OpenAlexW4293724692

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.