Chronicling the patient journey

Black, Alofi Shane; Sahama, Tony

doi:10.1145/2843043.2843381

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…The analysis of the literature identified five types of digital health DQ outcomes: (1) clinical, (2) business process, (3) clinician, (4) research-related, and (5) organizational outcomes ( Multimedia Appendix 8 [ 15 , 16 , 20 , 31 , 33 , 39 , 40 , 42 , 51 , 52 , 55 , 57 , 58 , 61 , 63 , 64 , 84 , 90 , 105 , 113 , 166 , 175 - 178 ]). Using NVivo’s built-in cross-tab query coupled with subject matter expert analysis, it was identified that different DQ dimensions were related to DQ outcomes in different ways ( Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

Digital Health Data Quality Issues: Systematic Review

Syed¹,

Eden²,

Makasi³

et al. 2023

J Med Internet Res

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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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Section: Resultsmentioning

confidence: 99%

Digital Health Data Quality Issues: Systematic Review

Syed¹,

Eden²,

Makasi³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…34 Other groups emphasise the concept of individual-focused PHR to maintain long term error-free healthcare qualities. 42 As one of many practical examples Uchimura's team presented a mobile app self-administrative medical solution, to further avoid mistakes and increase correctness of the PHRs. 47…”

Section: Discussionmentioning

confidence: 99%

“…Black et al applies globally relevant ethnic and social perspectives, delving into the patient journey and the PHR adoption in detail. 42 As a continuation to these, the needs of the disabled persons from ethical, social, and judicial perspectives, have been elaborated. 43 It is further emphasised that a global PHR must be all-encompassing, and not exclude anyone.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review and Meta-Analysis for a Global Patient co-Owned Cloud (GPOC)

Davids

El-Sharkawy

Herlenius³

et al. 2023

Preprint

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Current geopolitical tensions together with the global pandemic have provided important lessons for the need to independently re-evaluate our healthcare needs, guide and promote patient self-awareness and patient-centred care and to consider how cross-border medical information needs have become connected. The pandemic and war have also led to various humanitarian and healthcare crises for which there’s a need to re-evaluate and develop technologies to better manage Personal Health Records (PHRs) for displaced refugees with chronic diseases crossing borders. The recent trend of mobile platform-based, and electronic health record for e-health technologies enabled cloud-based PHR management as a paradigm for patient centred care. However, these platforms are yet to gain use-ubiquity globally. Here we performed a Prospective Register of Systematic Reviews (PROSPERO) registered and Preferred Reporting Items Systematic and Meta-Analyses (PRISMA)-guided systematic review and meta-analysis of the Personal Health Record looking at outcomes such as data security, efficiency, privacy, cost-based measures to generate a benchmark for future studies in this area. A meta-analysis of twelve axes for a future Global Patient co-Owned Cloud (GPOC) highlight the potential in health economics, performance, cryptography and speed of the basic techniques that are currently available, that would facilitate the construction of a GPOC. Whilst the field is early in its development, we highlight barriers, limitations and solutions through a proposed global consensus to ensure appropriate value delivery, safety and ethical governance for global digital personal health record adoption that can fundamentally beneficially transform the future of healthcare.

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“…The analysis of the literature identified five types of digital health DQ outcomes: (1) clinical, (2) business process, (3) clinician, (4) research-related, and (5) organizational outcomes (Multimedia Appendix 8 [15,16,20,31,33,39,40,42,51,52,55,57,58,61,63,64,84,90,105,113,166,[175][176][177][178]). Using NVivo's built-in cross-tab query coupled with subject matter expert analysis, it was identified that different DQ dimensions were related to DQ outcomes in different ways (Table 2).…”

Section: Outcomes Of Digital Health Dqmentioning

confidence: 99%

Digital Health Data Quality Issues: Systematic Review (Preprint)

Syed¹,

Eden²,

Makasi³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Chronicling the patient journey

Cited by 7 publications

References 4 publications

Digital Health Data Quality Issues: Systematic Review

Digital Health Data Quality Issues: Systematic Review

Systematic Review and Meta-Analysis for a Global Patient co-Owned Cloud (GPOC)

Digital Health Data Quality Issues: Systematic Review (Preprint)

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