Digital Health Data Quality Issues: Systematic Review

Syed, Rehan; Eden, Rebekah; Makasi, Tendai; Chukwudi, Ignatius; Mamudu, Azumah; Kamalpour, Mostafa; Geeganage, Dakshi Kapugama; Sadeghianasl, Sareh; Leemans, Sander J. J.; Goel, Kanika; Andrews, Robert; Wynn, Moe Thandar; Hofstede, Arthur H. M. ter; Myers, Trina

doi:10.2196/42615

Cited by 27 publications

(28 citation statements)

References 148 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data inconsistency significantly impaired data quality 50 and hampered digital healthcare development, as reiterated by stakeholders and experts in this study. The variability of data standardization and interoperability across medical institutions complicated the integration, utilization, and sharing of data on patients with NCDs.…”

Section: Discussionmentioning

confidence: 72%

Digital health integration for noncommunicable diseases: Comprehensive process mapping for full‐life‐cycle management

He,

Cui,

Feng

et al. 2024

J Evidence Based Medicine

View full text Add to dashboard Cite

AimTo create a systematic digital health process mapping framework for full‐life‐cycle noncommunicable disease management grounded in key stakeholder engagement.MethodsA triphasic, qualitative methodology was employed to construct a process mapping framework for digital noncommunicable disease management in Shanghai, China. The first phase involved desk research to examine current guidance and practices. In the second phase, pivotal stakeholders participated in focus group discussions to identify prevalent digital touchpoints across lifetime noncommunicable disease management. In the final phase, the Delphi technique was used to refine the framework based on expert insights and obtain consensus.ResultsWe identified 60 digital touchpoints across five essential stages of full‐life‐cycle noncommunicable disease management. Most experts acknowledged the rationality and feasibility of these touchpoints.ConclusionsThis study led to the creation of a comprehensive digital health process mapping framework that encompasses the entire life cycle of noncommunicable disease management. The insights gained emphasize the importance of a systemic strategic, person‐centered approach over a fragmented, purely technocentric approach. We recommend that healthcare professionals use this framework as a linchpin for efficient disease management and seamless technology incorporation in clinical practice.

show abstract

Section: Discussionmentioning

confidence: 72%

Digital health integration for noncommunicable diseases: Comprehensive process mapping for full‐life‐cycle management

He,

Cui,

Feng

et al. 2024

J Evidence Based Medicine

View full text Add to dashboard Cite

show abstract

“…A total of 22 articles were included in this review. The 22 reviews included systematic reviews (4/22, 18%) [ 23 - 26 ], scoping reviews (2/22, 9%) [ 27 , 28 ], and narrative reviews (16/22, 73%) [ 4 , 29 - 43 ]. All the reviews were published between 1995 and 2023.…”

Section: Resultsmentioning

confidence: 99%

“…Of the 22 reviews, 4 (18%) discussed the quality of public health informatics systems [ 37 , 38 ], real-world data repositories [ 31 ], and clinical research informatics tools [ 30 ]. Of the 22 reviews, 4 (18%) did not specify their data source [ 23 , 28 , 32 , 39 ].…”

Section: Resultsmentioning

confidence: 99%

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews

Declerck,

Kalra,

Vander Stichele

et al. 2024

JMIR Med Inform

View full text Add to dashboard Cite

Background Health care has not reached the full potential of the secondary use of health data because of—among other issues—concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data. Objective This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework. Methods A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English. Results A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions. Conclusions The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

show abstract

“…This data is essential for understanding a patient’s unique characteristics and genetic predisposition to disease. This database may be then subjected to advanced analysis using techniques such as ML and AI to identify patterns, trends, and associations in large volumes of patient data, which can be used to personalize treatments [ 236 , 237 ]. A notable example of this process is genomic medicine.…”

Section: Digital Health and Wearable Technologiesmentioning

confidence: 99%

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

Marques,

Costa,

Pereira

et al. 2024

Pharmaceutics

View full text Add to dashboard Cite

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient’s uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the “five rights”: the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug–drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

show abstract

Digital Health Data Quality Issues: Systematic Review

Cited by 27 publications

References 148 publications

Digital health integration for noncommunicable diseases: Comprehensive process mapping for full‐life‐cycle management

Digital health integration for noncommunicable diseases: Comprehensive process mapping for full‐life‐cycle management

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

Contact Info

Product

Resources

About