Evidence map›Paper›PMID 37819910›Full record

ArticlePLOS digital health2023

Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.

Jana Sedlakova, Paola Daniore, Andrea Horn Wintsch, Markus Wolf, Mina Stanikic, Christina Haag, Chloé Sieber, Gerold Schneider, Kaspar Staub, Dominik Alois Ettlin and 4 more

Abstract read
In one paragraph

Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 1 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

58 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  13. Regulatory framework for health information systems in Ethiopia: A qualitative document analysis.Health information management : journal of the Health Information Management Association of Australia · 2026
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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

14 authors.

Jana SedlakovaDigital Society Initiative, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-6887-5941
Paola DanioreDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Andrea Horn WintschDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Markus WolfDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Mina StanikicDigital Society Initiative, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-6477-7164
Christina HaagDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Chloé SieberDigital Society Initiative, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-6642-5082
Gerold SchneiderDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Kaspar StaubDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Dominik Alois EttlinDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Oliver GrübnerDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Fabio RinaldiDigital Society Initiative, University of Zurich, Zurich, Switzerland.
Viktor von WylDigital Society Initiative, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-8754-9797
University of Zurich Digital Society Initiative (UZH-DSI) Health Community

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.

Identifiers

PMID37819910
PMCPMC10566734

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Registered trials

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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.