Questions and Answers
What is one major issue affecting the publication of datasets by researchers?
What update to governance models is suggested for dataset usage?
What technological solution is proposed to improve the capture of data provenance?
What aspect of research reproducibility is highlighted as a significant issue?
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What percentage of academic studies was reported as replicable by the Bayer Healthcare team?
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Which organization is taking over the responsibilities from the National Information Governance Board regarding dataset usage?
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What is one proposed solution to enhance awareness of available data among researchers?
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Which strategy is suggested to address poor data management in health research groups?
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What is the main purpose of classification in informatics?
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Which statement best describes the challenges related to bias in real-world data (RWD)?
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What is one example of reimbursement bias in healthcare data recording?
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Which coding system uses the alphanumeric code 'CT10F'?
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What is a significant risk associated with longitudinal studies in the context of data errors?
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Which situation exemplifies software bias in electronic health records?
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What aspect of data is crucial when addressing information gaps in randomized control trials?
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Which of the following accurately reflects the role of terminology in informatics?
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What is the primary goal of a Learning Health System?
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Which standard is specifically designed for reporting observational studies?
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What is a secondary problem identified within persistent issues in clinical research?
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Which reporting standard was developed as an evolution of the STROBE guidelines?
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What does the acronym GxP encompass in the context of clinical research?
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Which of the following best describes the implication of not integrating clinical trials and observational studies?
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What is one consequence of complex, costly Case Report Forms (CRFs) in clinical research?
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What is a critical reason for emphasizing traceability and accountability in research data?
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Which of the following is a potential cause of bias in the health care system?
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What is NOT a component of practice workload that may introduce bias?
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Which aspect of electronic health record (EHR) systems can contribute to data bias?
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Which anonymisation technique is aimed primarily at qualitative data?
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What limits the effectiveness of crude removal of identifiers in qualitative data?
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Which factor is essential for achieving a reasonable level of anonymisation without sacrificing data content?
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Which of the following is a key consideration when preparing research datasets?
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Which technique is employed to maintain the integrity of relational data while anonymising it?
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What is a crucial characteristic of Big Data in healthcare?
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Which of the following best explains the differences between Big Data research and classical research approaches?
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What type of bias is particularly concerning in Big Health Data research?
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Why is reproducibility crucial in health research involving Big Data?
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What is a characteristic of a Learning Health System (LHS)?
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How can biases in Big Data be mitigated according to best practices?
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Which of the following best exemplifies a scenario that could be transformed into a Learning Health System?
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In administering tailored messages for better health interventions, it is important to consider which factor?
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Study Notes
Informatics
- Classification: Represents terms, concepts, and their relationships systematically.
- Nomenclature: An agreed system of names assigned within specific fields.
- Terminology: Defined words or expressions relevant to a particular discipline.
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Coding systems: Numeric or alphanumeric codes for medical diagnoses, e.g.:
- ICD-10: E11
- SNOMED CT: 16403005
Challenge of Bias in Real-World Data (RWD)
- Data collected is often used for various purposes; may lack completeness and accuracy.
- Clinicians and insurers need to be aware of potential biases in Electronic Health Records (EHR).
- Information gaps in Randomised Control Trials should be addressed, focusing on data provenance.
- Software bias may arise, such as limitations in EHR systems preventing accurate data entry.
- Data errors observed include peculiar statistics like a 1% "resurrection" rate in UK studies.
Possible Sources of Bias
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Healthcare System Bias:
- Impact of reimbursement systems on data recording practices.
- Influence of clinician roles and adherence to professional guidelines.
- Challenges in patient access to records and data-sharing among providers.
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Technical Factors:
- Variability in EHR functionalities and coding systems.
- Ineffective data processing and extraction methodologies.
- Workload Factors: Increased workload may impact data accuracy and consistency.
Anonymisation Techniques
- Quantitative Methods: Involve removing or aggregating variables to protect identities.
- Qualitative Approaches: Use pseudonyms or less specific terms instead of crude data removal.
- Objective is to achieve reasonable anonymisation without sacrificing data utility.
Challenges of Research Data Management
- Publishing Datasets: Lack of incentive for researchers to make datasets accessible; citation indices needed for evaluation.
- Governance Models: Outdated frameworks restrict dataset use without proper oversight; calls for more devolved approval processes.
- Awareness: Need for metadata registries to inform researchers about available datasets and their provenance.
- Capturing Provenance: Importance of documenting data provenance during analysis to enhance research transparency.
- Data Management: Necessity for coherent analytical strategies and training in health informatics.
Reproducibility Challenge
- Issues with ensuring transparency and correctness in published research findings.
- Research indicating only 25% of reviewed studies could be replicated, highlighting a critical challenge.
Reporting Standards
- Traceability and accountability in clinical research are vital.
- Established standards include:
- GxP (Good Practices in clinical data management)
- CONSORT for trial reporting
- STROBE for observational studies reporting
Learning Health System (LHS)
- Defined as systems that integrate progress in science, informatics, and culture to continuously improve healthcare quality.
- Emphasizes the need to leverage data effectively to avoid waste.
Context for Learning Health System
- Persistent issues include challenges in identifying subjects and complex data entry.
- A need for synergy between clinical trials and observational studies to address inefficiencies.
- Diagnostic errors, particularly in primary care, highlight areas requiring improvement.
- Proposed approach involves analyzing patient data and tailoring interventions to optimize health outcomes.
Summary Points
- Big Data plays a crucial role in enhancing healthcare systems and treatments.
- Understanding biases in data is essential for responsible research.
- The goal is to establish a Learning Health System that utilizes data for continuous improvement.
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Description
This quiz explores the systematic representation of terms and concepts in Informatics, focusing on Big Data and its classification. It delves into the connections between various terms and their relevance in modern data systems. Test your understanding of classifications and nomenclature in this exciting field.