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

Eden, Rebekah; Syed, Rehan; Makasi, Tendai; Andrews, Robert; Ter Hofstede, Arthur; Wynn, Moe; Donovan, Raelene; Eley, Robert; Staib, Andrew

doi:10.24251/hicss.2023.372

Cited by 2 publications

References 27 publications

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Data Quality–Driven Improvement in Health Care: Systematic Literature Review

Lighterness,

Adcock,

Scanlon

et al. 2024

J Med Internet Res

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Background:The promise of real-world evidence and the learning healthcare system primarily depends on access to highquality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), current best practices for its assessment and improvement are unknown.Objective: We sought to investigate how studies define, assess, and improve the quality of structured real-world healthcare data.Methods: A systematic literature search of studies in the English language was implemented in EMBASE and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardised DQ concepts according to the DAMA (Data Management Association) DQ framework to enable comparison between studies. After screening and filtering by two independent authors, we identified 39 relevant articles reporting DQ improvement initiatives.Results: Studies were characterised by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains, and most targeted data generated by multiple institutions. DQ assessment methods were largely manual and targeted only completeness and/or one other DQ dimension. Use of DQ frameworks (n = 6) and quality improvement methodologies (n = 5) were sorely lacking. Most studies reported improvements in DQ through the implementation of a combination of interventions, which included either DQ reporting and personalised feedback, implementation of new digital systems for electronic data capture, training on DQ or data standards, or improvements in clinical workflows. Reporting of changes in DQ varied significantly, making it difficult to conduct objective meta-analysis for determination of treatment effect. Conclusions:There is an urgent need for standardised guidelines in the context of DQ improvement research to enable comparison and effective synthesis of lessons learnt. Frameworks such as Plan-Do-Study-Act (PDSA) learning cycles and the DAMA DQ framework can facilitate this unmet need. However, DQ improvement studies also need to pay closer attention to root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long term sustainable improvement.

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Data Quality–Driven Improvement in Health Care: Systematic Literature Review

Lighterness,

Adcock,

Scanlon

et al. 2024

J Med Internet Res

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Data Quality–Driven Improvement in Health Care: Systematic Literature Review (Preprint)

Lighterness,

Adcock,

Scanlon

et al. 2024

Preprint

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BACKGROUND The promise of real-world evidence and the learning healthcare system primarily depends on access to high-quality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), current best practices for its assessment and improvement are unknown. OBJECTIVE We sought to investigate how studies define, assess, and improve the quality of structured real-world healthcare data. METHODS A systematic literature search of studies in the English language was implemented in EMBASE and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardised DQ concepts according to the DAMA (Data Management Association) DQ framework to enable comparison between studies. After screening and filtering by two independent authors, we identified 39 relevant articles reporting DQ improvement initiatives. RESULTS Studies were characterised by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains, and most targeted data generated by multiple institutions. DQ assessment methods were largely manual and targeted only completeness and/or one other DQ dimension. Use of DQ frameworks (n = 6) and quality improvement methodologies (n = 5) were sorely lacking. Most studies reported improvements in DQ through the implementation of a combination of interventions, which included either DQ reporting and personalised feedback, implementation of new digital systems for electronic data capture, training on DQ or data standards, or improvements in clinical workflows. Reporting of changes in DQ varied significantly, making it difficult to conduct objective meta-analysis for determination of treatment effect. CONCLUSIONS There is an urgent need for standardised guidelines in the context of DQ improvement research to enable comparison and effective synthesis of lessons learnt. Frameworks such as Plan-Do-Study-Act (PDSA) learning cycles and the DAMA DQ framework can facilitate this unmet need. However, DQ improvement studies also need to pay closer attention to root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long term sustainable improvement.

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

Cited by 2 publications

References 27 publications

Data Quality–Driven Improvement in Health Care: Systematic Literature Review

Data Quality–Driven Improvement in Health Care: Systematic Literature Review

Data Quality–Driven Improvement in Health Care: Systematic Literature Review (Preprint)

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