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Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS DIGITAL HEALTH 2023; 2:e0000347. [PMID: 37819910 PMCID: PMC10566734 DOI: 10.1371/journal.pdig.0000347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/14/2023] [Indexed: 10/13/2023]
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.
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Affiliation(s)
- Jana Sedlakova
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
| | - Andrea Horn Wintsch
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center for Gerontology, University of Zurich, Zurich, Switzerland
- CoupleSense: Health and Interpersonal Emotion Regulation Group, University Research Priority Program (URPP) Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Markus Wolf
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Mina Stanikic
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christina Haag
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Chloé Sieber
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Gerold Schneider
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Kaspar Staub
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
| | - Dominik Alois Ettlin
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Oliver Grübner
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Geography, University of Zurich, Zurich, Switzerland
| | - Fabio Rinaldi
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Dalle Molle Institute for Artificial Intelligence (IDSIA), Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Fondazione Bruno Kessler, Trento, Italy
- Swiss Institute of Bioinformatics, Switzerland
| | - Viktor von Wyl
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Societal Technological Megatrends: A Bibliometric Analysis from 1982 to 2021. SUSTAINABILITY 2022. [DOI: 10.3390/su14031543] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article presents a bibliometric analysis of scientific publications investigating technological megatrends at the societal level, through the parallel analysis of 549 documents from Scopus and 291 documents in Web of Science (WoS) using the VOSviewer software and the Excel component of the MS Office 365 package. The main purpose of this study was to obtain an overview of the evolution of the research on the subject of technological megatrends from the perspective of interest, domains, geographical areas, sources, authors and cocitation networks, research clusters of countries, and cluster-related concepts. The results showed that publications on technological megatrends started in 1982, but from a scientific point of view they started in 1983 (Scopus) and 1984 (WoS), and that they display an increasing trend after 2010. Technological Forecasting and Social Change, Nature, SAE Technical Papers, VDI Berichte, Harvard Business Review, Advances in Intelligent Systems and Computing, and Sustainability represent the most important sources, and Gibbs, Kraemer, Dedrick, Kim, Chmiela, Sauceda, Müller, Tkatchenko, Pratt, Sarmiento, Montes, Ogilvie, Marcus, Perez, Brownson, D. Mourtzis, M. Doukas, and Bernidaki are the most notorious researchers in the field. At the societal level, technological megatrends are closely related to foresight, globalization, industry 4.0, the internet of things, digitalization, technology, artificial intelligence, innovation, the future, and sustainability. This study is original and useful for researchers in the context of the lack of similar studies on this subject.
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