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Syed R, Eden R, Makasi T, Chukwudi I, Mamudu A, Kamalpour M, Kapugama Geeganage D, Sadeghianasl S, Leemans SJJ, Goel K, Andrews R, Wynn MT, Ter Hofstede A, Myers T. Digital Health Data Quality Issues: Systematic Review. J Med Internet Res 2023; 25:e42615. [PMID: 37000497 PMCID: PMC10131725 DOI: 10.2196/42615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/07/2022] [Accepted: 12/31/2022] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. OBJECTIVE The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? METHODS Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. RESULTS The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. CONCLUSIONS The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.
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Affiliation(s)
- Rehan Syed
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Rebekah Eden
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Tendai Makasi
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Ignatius Chukwudi
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Azumah Mamudu
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Mostafa Kamalpour
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Dakshi Kapugama Geeganage
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Sareh Sadeghianasl
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Sander J J Leemans
- Rheinisch-Westfälische Technische Hochschule, Aachen University, Aachen, Germany
| | - Kanika Goel
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Robert Andrews
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Moe Thandar Wynn
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Arthur Ter Hofstede
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
| | - Trina Myers
- School of Information Systems, Faculty of Science, Queensland University of Technology, Brisbane, Australia
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Chen X, Faviez C, Vincent M, Briseño-Roa L, Faour H, Annereau JP, Lyonnet S, Zaidan M, Saunier S, Garcelon N, Burgun A. Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis. Front Pharmacol 2022; 13:786710. [PMID: 35401179 PMCID: PMC8993144 DOI: 10.3389/fphar.2022.786710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
A timely diagnosis is a key challenge for many rare diseases. As an expanding group of rare and severe monogenic disorders with a broad spectrum of clinical manifestations, ciliopathies, notably renal ciliopathies, suffer from important underdiagnosis issues. Our objective is to develop an approach for screening large-scale clinical data warehouses and detecting patients with similar clinical manifestations to those from diagnosed ciliopathy patients. We expect that the top-ranked similar patients will benefit from genetic testing for an early diagnosis. The dependence and relatedness between phenotypes were taken into account in our similarity model through medical concept embedding. The relevance of each phenotype to each patient was also considered by adjusted aggregation of phenotype similarity into patient similarity. A ranking model based on the best-subtype-average similarity was proposed to address the phenotypic overlapping and heterogeneity of ciliopathies. Our results showed that using less than one-tenth of learning sources, our language and center specific embedding provided comparable or better performances than other existing medical concept embeddings. Combined with the best-subtype-average ranking model, our patient-patient similarity-based screening approach was demonstrated effective in two large scale unbalanced datasets containing approximately 10,000 and 60,000 controls with kidney manifestations in the clinical data warehouse (about 2 and 0.4% of prevalence, respectively). Our approach will offer the opportunity to identify candidate patients who could go through genetic testing for ciliopathy. Earlier diagnosis, before irreversible end-stage kidney disease, will enable these patients to benefit from appropriate follow-up and novel treatments that could alleviate kidney dysfunction.
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Affiliation(s)
- Xiaoyi Chen
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.,HeKA, Inria, Paris, France.,Data Science Platform, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | - Carole Faviez
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.,HeKA, Inria, Paris, France
| | - Marc Vincent
- Data Science Platform, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | | | - Hassan Faour
- Data Science Platform, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | | | | | - Mohamad Zaidan
- Service de Néphrologie, Hôpital Universitaire Bicêtre, Kremlin Bicêtre, France
| | - Sophie Saunier
- Laboratory of Renal Hereditary Diseases, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.,HeKA, Inria, Paris, France.,Data Science Platform, Imagine Institute, Université de Paris, INSERM UMR 1163, Paris, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France.,HeKA, Inria, Paris, France.,Department of Medical Informatics, Hôpital Necker-Enfant Malades, AP-HP, Paris, France
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