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

doi:10.1371/journal.pdig.0000347

. 2023 Oct 11;2(10):e0000347.

doi: 10.1371/journal.pdig.0000347. eCollection 2023 Oct.

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

Jana Sedlakova^{1

2

3}, Paola Daniore^{1

2}, Andrea Horn Wintsch^{1

4

5}, Markus Wolf^{1

6}, Mina Stanikic^{1

2

7}, Christina Haag^{1

2

7}, Chloé Sieber^{1

2

7}, Gerold Schneider^{1

8}, Kaspar Staub^{1

9}, Dominik Alois Ettlin^{1

10}, Oliver Grübner^{1

11}, Fabio Rinaldi^{1

12

13

14

15}, Viktor von Wyl^{1

2

7}; University of Zurich Digital Society Initiative (UZH-DSI) Health Community

Affiliations

¹ Digital Society Initiative, University of Zurich, Zurich, Switzerland.
² Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.
³ Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland.
⁴ Center for Gerontology, University of Zurich, Zurich, Switzerland.
⁵ CoupleSense: Health and Interpersonal Emotion Regulation Group, University Research Priority Program (URPP) Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland.
⁶ Department of Psychology, University of Zurich, Zurich, Switzerland.
⁷ Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
⁸ Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.
⁹ Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland.
¹⁰ Center of Dental Medicine, University of Zurich, Zurich, Switzerland.
¹¹ Department of Geography, University of Zurich, Zurich, Switzerland.
¹² Dalle Molle Institute for Artificial Intelligence (IDSIA), Switzerland.
¹³ Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
¹⁴ Fondazione Bruno Kessler, Trento, Italy.
¹⁵ Swiss Institute of Bioinformatics, Switzerland.

PMID: 37819910
PMCID: PMC10566734
DOI: 10.1371/journal.pdig.0000347

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

Jana Sedlakova et al. PLOS Digit Health. 2023.

. 2023 Oct 11;2(10):e0000347.

doi: 10.1371/journal.pdig.0000347. eCollection 2023 Oct.

Authors

Affiliations

¹ Digital Society Initiative, University of Zurich, Zurich, Switzerland.
² Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland.
³ Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland.
⁴ Center for Gerontology, University of Zurich, Zurich, Switzerland.
⁵ CoupleSense: Health and Interpersonal Emotion Regulation Group, University Research Priority Program (URPP) Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland.
⁶ Department of Psychology, University of Zurich, Zurich, Switzerland.
⁷ Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
⁸ Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.
⁹ Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland.
¹⁰ Center of Dental Medicine, University of Zurich, Zurich, Switzerland.
¹¹ Department of Geography, University of Zurich, Zurich, Switzerland.
¹² Dalle Molle Institute for Artificial Intelligence (IDSIA), Switzerland.
¹³ Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
¹⁴ Fondazione Bruno Kessler, Trento, Italy.
¹⁵ Swiss Institute of Bioinformatics, Switzerland.

PMID: 37819910
PMCID: PMC10566734
DOI: 10.1371/journal.pdig.0000347

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.

Copyright: © 2023 Sedlakova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Conflict of interest statement

The authors have declared that no competing interests exist.

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References

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1. Unstructured Data—an overview | ScienceDirect Topics. [cited 21 Aug 2023]. https://www.sciencedirect.com/topics/computer-science/unstructured-data
1. Badawy R, Hameed F, Bataille L, Little MA, Claes K, Saria S, et al.. Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research. Digital biomarkers. 2019;3: 116–132. doi: 10.1159/000502951 - DOI - PMC - PubMed
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Grants and funding

This work was supported by a Digital Society Initiative Health Community Grant to VVW (grant number not applicable). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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[1] Kong H-J. Managing Unstructured Big Data in Healthcare System. Healthcare informatics research. 2019;25: 1–2. doi: 10.4258/hir.2019.25.1.1 - DOI - PMC - PubMed

[2] Kong H-J. Managing Unstructured Big Data in Healthcare System. Healthcare informatics research. 2019;25: 1–2. doi: 10.4258/hir.2019.25.1.1 - DOI - PMC - PubMed

[3] Unstructured Data—an overview | ScienceDirect Topics. [cited 21 Aug 2023]. https://www.sciencedirect.com/topics/computer-science/unstructured-data

[4] Unstructured Data—an overview | ScienceDirect Topics. [cited 21 Aug 2023]. https://www.sciencedirect.com/topics/computer-science/unstructured-data

[5] Badawy R, Hameed F, Bataille L, Little MA, Claes K, Saria S, et al.. Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research. Digital biomarkers. 2019;3: 116–132. doi: 10.1159/000502951 - DOI - PMC - PubMed

[6] Badawy R, Hameed F, Bataille L, Little MA, Claes K, Saria S, et al.. Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research. Digital biomarkers. 2019;3: 116–132. doi: 10.1159/000502951 - DOI - PMC - PubMed

[7] Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, et al.. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. European Heart Journal. 2018;39: 1481–1495. doi: 10.1093/eurheartj/ehx487 - DOI - PMC - PubMed

[8] Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, et al.. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. European Heart Journal. 2018;39: 1481–1495. doi: 10.1093/eurheartj/ehx487 - DOI - PMC - PubMed

[9] Stephenson D, Alexander R, Aggarwal V, Badawy R, Bain L, Bhatnagar R, et al.. Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science. Digital biomarkers. 2020;4: 28–49. doi: 10.1159/000512500 - DOI - PMC - PubMed

[10] Stephenson D, Alexander R, Aggarwal V, Badawy R, Bain L, Bhatnagar R, et al.. Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science. Digital biomarkers. 2020;4: 28–49. doi: 10.1159/000512500 - DOI - PMC - PubMed

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

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

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Abstract

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