Evidence map›Paper›PMID 35082976›Full record

ArticleOnline journal of public health informatics2021

Leveraging Data and Digital Health Technologies to Assess and Impact Social Determinants of Health (SDoH): a State-of-the-Art Literature Review.

Kelly J Thomas Craig, Nicole Fusco, Thrudur Gunnarsdottir, Luc Chamberland, Jane L Snowdon, William J Kassler

Abstract read
In one paragraph

Article in Online journal of public health informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  14. Digital health equity frameworks and key concepts: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2025
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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.

Kelly J Thomas CraigIBM® Watson Health®, Center for AI, Research, and Evaluation, 75 Binney Street, Cambridge, MA, 02142 USA.
Nicole FuscoIBM ® Watson Health®, 75 Binney Street, Cambridge, MA, 02142 USA.
Thrudur GunnarsdottirIBM ® Watson Health®, 75 Binney Street, Cambridge, MA, 02142 USA.
Luc ChamberlandIBM ® Watson Health®, 75 Binney Street, Cambridge, MA, 02142 USA.
Jane L SnowdonIBM® Watson Health®, Center for AI, Research, and Evaluation, 75 Binney Street, Cambridge, MA, 02142 USA.
William J KasslerIBM ® Watson Health®, 75 Binney Street, Cambridge, MA, 02142 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIdentify how novel datasets and digital health technology, including both analytics-based and artificial intelligence (AI)-based tools, can be used to assess non-clinical, social determinants of health (SDoH) for population health improvement.

methodsA state-of-the-art literature review with systematic methods was performed on MEDLINE, Embase, and the Cochrane Library databases and the grey literature to identify recently published articles (2013-2018) for evidence-based qualitative synthesis. Following single review of titles and abstracts, two independent reviewers assessed eligibility of full-texts using predefined criteria and extracted data into predefined templates.

resultsThe search yielded 2,714 unique database records of which 65 met inclusion criteria. Most studies were conducted retrospectively in a United States community setting. Identity, behavioral, and economic factors were frequently identified social determinants, due to reliance on administrative data. Three main themes were identified: 1) improve access to data and technology with policy - advance the standardization and interoperability of data, and expand consumer access to digital health technologies; 2) leverage data aggregation - enrich SDoH insights using multiple data sources, and use analytics-based and AI-based methods to aggregate data; and 3) use analytics-based and AI-based methods to assess and address SDoH - retrieve SDoH in unstructured and structured data, and provide contextual care management sights and community-level interventions.

conclusionsIf multiple datasets and advanced analytical technologies can be effectively integrated, and consumers have access to and literacy of technology, more SDoH insights can be identified and targeted to improve public health. This study identified examples of AI-based use cases in public health informatics, and this literature is very limited.

Indexed as

artificial intelligencedata analyticsdigital healthhealth policysocial determinants of health

Identifiers

PMID35082976
PMCPMC8765800

What OpenQuestion holds

Textmetadata
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.