Root-cause analysis of process-data quality problems

Andrews, Robert; Emamjome, Fahame; Hofstede, Arthur H. M. ter; Reijers, Hajo A.

doi:10.1080/2573234x.2021.1947751

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…To extend this study, we advocate for empirical mixed methods case studies to validate the framework, including an examination of the interrelationships between DQ dimensions and DQ outcomes, based on real-life data and consultation with a variety of stakeholders. Existing approaches can be used to identify the presence of issues related to DQ dimensions within digital health system logs [ 38 , 183 ]. The DQ outcomes could be assessed by extracting prerecorded key performance indicators from case hospitals and be triangulated with interview data to capture patients’, clinicians’, and hospitals’ perspectives of the impacts of DQ [ 184 ].…”

Section: Discussionmentioning

confidence: 99%

“…Future research should examine the root causes of DQ challenges in health care data to prevent such challenges from occurring in the first place. A framework that may prove useful in illuminating the root causes of DQ issues is the Odigos framework, which indicates that DQ issues emanate from the social world (ie, macro and situational structures, roles, and norms), material world (eg, quality of the EHR system and technological infrastructure), and personal world (eg, characteristics and behaviors of health care professionals) [ 183 ]. These insights could then be incorporated into a data governance roadmap for digital hospitals.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Digital Health Data Quality Issues: Systematic Review

Syed¹,

Eden²,

Makasi³

et al. 2023

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…We employ the Odigos framework (Figure 1) to understand the root causes of digital health data quality (DQ) issues. The Odigos framework has been cumulatively developed to prognostically and diagnostically identify why DQ issues occur (Andrews et al, 2020(Andrews et al, , 2022Emamjome et al, 2020). The Odigos framework builds on Mingers and Willcocks (2017, pp.…”

Section: Odigos Frameworkmentioning

confidence: 99%

“…The Odigos framework is a notable exception, which instantiates semiotics theory in the context of process-oriented DQ. The Odigos framework posits that process-oriented DQ issues stem from the social (e.g., situational and macro level, structures and norms), material (e.g., technology and infrastructure), and personal worlds (e.g., system users, analysts) (Andrews et al, 2022). Although the Odigos framework was designed to provide insights into process-oriented DQ issues, we argue that it is commensurate with the broader context of DQ in digital health as process-oriented data is a subset of data extracted from electronic medical records, which records the patient's journey.…”

Section: Introductionmentioning

confidence: 99%

Revealing the Root Causes of Digital Health Data Quality Issues: A Qualitative Investigation of the Odigos Framework

Eden,

Syed,

Makasi

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Digital health data quality (DQ) is a critical concern in the healthcare industry, jeopardizing the secondary use of data for transforming population health, and hindering patient care and organizational outcomes. Limited evidence exists for explaining why these DQ issues emerge. The Odigos framework is a notable exception asserting that DQ issues emerge from three worlds: material (e.g., technology), personal (e.g., technology users), and social (e.g., organizations/institutions) but has yet to unpack the elements within these worlds. Through deductive and inductive analysis of interview data from a case study of the Emergency Department of Australia's first large digital hospital, we apply and extend the Odigos framework by identifying elements emanating from the three worlds and their interrelationships as root causes of DQ issues. These elements can then be used by hospitals to develop strategies to proactively improve their digital health DQ.

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“…While reviewing the existing literature, we have seen a focus on use-cases [14,23,25], on general approaches to (and techniques for) process analytics [5] and strategies and frameworks for creating event logs for process mining [4,17]. Recently, also data quality is receiving more attention [3]. However, we could not find previous work addressing all these elements and a hands-on data preprocessing example and corresponding best-practices.…”

Section: Introductionmentioning

confidence: 99%

A Practitioner’s View on Process Mining Adoption, Event Log Engineering and Data Challenges

Accorsi

Lebherz

2022

Lecture Notes in Business Information Processing

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Process mining is, today, an essential analytical instrument for data-driven process improvement and steering. While practical literature on how to derive value from process mining exists, less attention haas been paid to how it is being used in different industries, the effort involved in creating an event log and what are the best practices in doing so. Taking a practitioner’s view on process mining, we report on process mining adoption and illustrate the challenges of log contruction by means of the order to cash (i.e. sales) process in an SAP system. By doing so, we collect a set of best practices regarding the data selection, extraction, transformation and data model engineering, which proved themselves handy in large-scale process mining projects.

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Root-cause analysis of process-data quality problems

Cited by 10 publications

References 32 publications

Digital Health Data Quality Issues: Systematic Review

Digital Health Data Quality Issues: Systematic Review

Revealing the Root Causes of Digital Health Data Quality Issues: A Qualitative Investigation of the Odigos Framework

A Practitioner’s View on Process Mining Adoption, Event Log Engineering and Data Challenges

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