Data quality: “Garbage in – garbage out”

Kilkenny, Monique F; Robinson, Kerin

doi:10.1177/1833358318774357

Cited by 99 publications

(63 citation statements)

References 12 publications

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“…Clinical data are generated by caregivers in the EHRs but are not primarily intended for further use in either research or feedback. CDSS quality depends on the ‘raw’ data and so ‘garbage in—garbage out’ illustrates the problem well [11].…”

Section: Methodsmentioning

confidence: 99%

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A computerised decision support system for cardiovascular risk management ‘live’ in the electronic health record environment: development, validation and implementation—the Utrecht Cardiovascular Cohort Initiative

et al. 2019

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Purpose We set out to develop a real-time computerised decision support system (CDSS) embedded in the electronic health record (EHR) with information on risk factors, estimated risk, and guideline-based advice on treatment strategy in order to improve adherence to cardiovascular risk management (CVRM) guidelines with the ultimate aim of improving patient healthcare. Methods We defined a project plan including the scope and requirements, infrastructure and interface, data quality and study population, validation and evaluation of the CDSS. Results In collaboration with clinicians, data scientists, epidemiologists, ICT architects, and user experience and interface designers we developed a CDSS that provides ‘live’ information on CVRM within the environment of the EHR. The CDSS provides information on cardiovascular risk factors (age, sex, medical and family history, smoking, blood pressure, lipids, kidney function, and glucose intolerance measurements), estimated 10-year cardiovascular risk, guideline-compliant suggestions for both pharmacological and non-pharmacological treatment to optimise risk factors, and an estimate on the change in 10-year risk of cardiovascular disease if treatment goals are adhered to. Our pilot study identified a number of issues that needed to be addressed, such as missing data, rules and regulations, privacy, and patient participation. Conclusion Development of a CDSS is complex and requires a multidisciplinary approach. We identified opportunities and challenges in our project developing a CDSS aimed at improving adherence to CVRM guidelines. The regulatory environment, including guidance on scientific evaluation, legislation, and privacy issues needs to evolve within this emerging field of eHealth.

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Section: Methodsmentioning

confidence: 99%

“…defined as the proportion of information provided versus information needed) and data accuracy (i.e. defined as agreement between the value presented in the dashboard and the ‘raw’ value in the EHR) [11, 13]. These parameters were assessed for risk factor levels, risk estimates, and medication prescription.…”

Section: Methodsmentioning

confidence: 99%

A computerised decision support system for cardiovascular risk management ‘live’ in the electronic health record environment: development, validation and implementation—the Utrecht Cardiovascular Cohort Initiative

et al. 2019

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“…Since even complex and sophisticated algorithms will not produce good results if the quality of the input data is poor, refinement of input data to improve their quality will provide better results even if the algorithm is less than optimal [37,57]. As has often been noted: “garbage in, garbage out” [58].…”

Section: Salient Points To Consider When Running Machine Learningmentioning

confidence: 99%

Application of machine learning in rheumatic disease research

Kim

Tagkopoulos

2019

Korean J Intern Med

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Over the past decade, there has been a paradigm shift in how clinical data are collected, processed and utilized. Machine learning and artificial intelligence, fueled by breakthroughs in high-performance computing, data availability and algorithmic innovations, are paving the way to effective analyses of large, multi-dimensional collections of patient histories, laboratory results, treatments, and outcomes. In the new era of machine learning and predictive analytics, the impact on clinical decision-making in all clinical areas, including rheumatology, will be unprecedented. Here we provide a critical review of the machine-learning methods currently used in the analysis of clinical data, the advantages and limitations of these methods, and how they can be leveraged within the field of rheumatology.

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“…Even when the data are extracted from an Electronic Health Record (EHR) and entered into the EDC system automatically, this needs to be checked by someone. The term "Garbage in, garbage out" (GIGO) comes from computer science, but applies to medical data as well [26]. The solution here is to reserve money to hire people who can do this job for a certain amount of hours per week.…”

Section: Commandment 2: Reserve Time and Money For Data Entrymentioning

confidence: 99%

The ten commandments of translational research informatics

Hulsen

2019

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Translational research applies findings from basic science to enhance human health and well-being. In translational research projects, academia and industry work together to improve healthcare, often through public-private partnerships. This "translation" is often not easy, because it means that the so-called "valley of death" will need to be crossed: many interesting findings from fundamental research do not result in new treatments, diagnostics and prevention. To cross the valley of death, fundamental researchers need to collaborate with clinical researchers and with industry so that promising results can be implemented in a product. The success of translational research projects often does not depend only on the fundamental science and the applied science, but also on the informatics needed to connect everything: the translational research informatics. This informatics, which includes data management, data stewardship and data governance, enables researchers to store and analyze their 'big data' in a meaningful way, and enable application in the clinic. The author has worked on the information technology infrastructure for several translational research projects in oncology for the past nine years, and presents his lessons learned in this paper in the form of ten commandments. These commandments are not only useful for the data managers, but for all involved in a translational research project. Some of the commandments deal with topics that are currently in the spotlight, such as machine readability, the FAIR Guiding Principles and the GDPR regulations. Others are mentioned less in the literature, but are just as crucial for the success of a translational research project.

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Data quality: “Garbage in – garbage out”

Cited by 99 publications

References 12 publications

A computerised decision support system for cardiovascular risk management ‘live’ in the electronic health record environment: development, validation and implementation—the Utrecht Cardiovascular Cohort Initiative

A computerised decision support system for cardiovascular risk management ‘live’ in the electronic health record environment: development, validation and implementation—the Utrecht Cardiovascular Cohort Initiative

Application of machine learning in rheumatic disease research

The ten commandments of translational research informatics

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