Big Data in Informatics

Questions and Answers

What percentage of oncology studies highlighted apparent advances from 2001-2011 could be robustly replicated?

53%

18%

11% (correct)

29%

How many out of 18 microarray papers were found to have results that could not be reproduced?

10 (correct)

What is one potential threat to reproducible science mentioned in the document?

Inadequate sample sizes

Excessive data analysis

Lack of peer review

Overly honest methodologies (correct)

According to the document, which temperature range was used during the heat shock of E. coli?

42-43 degrees C Signup and view all the answers

Which study year was cited in relation to the repeatability of published microarray gene expression analyses?

2009 Signup and view all the answers

What key aspect of scientific reporting is suggested to potentially mislead researchers?

Editing by supervisors Signup and view all the answers

What issue is indicated regarding retractions in scientific publishing?

They can arise from reproducibility issues. Signup and view all the answers

What is the total number of oncology studies evaluated for reproducibility from 2001 to 2011?

53 Signup and view all the answers

Which standard specifically applies to the reporting of observational studies?

STROBE Signup and view all the answers

What is a major persistent issue in clinical research mentioned in the content?

Difficulty identifying subjects Signup and view all the answers

Which of the following is NOT associated with reporting standards in clinical research?

ISO 9001 Signup and view all the answers

What does the Learning Health System aim to align for continuous improvement?

Science, informatics, and care culture Signup and view all the answers

What is a common secondary problem resulting from issues in clinical research?

Diagnostic error Signup and view all the answers

Which reporting standard has evolved from STROBE for observational data?

RECORD Signup and view all the answers

What is highlighted as a critical factor that cannot be overlooked in data utilization?

Wasting data Signup and view all the answers

What characterizes funding for clinical research as mentioned in the content?

Not cost-effective Signup and view all the answers

What is the main challenge associated with data collected for multiple purposes in health informatics?

Patient information may lack completeness, accuracy, or currency. Signup and view all the answers

What bias is illustrated by the example of not recording a BMI for a thin person?

Reimbursement bias. Signup and view all the answers

Which term best describes a systematic representation of terms and relationships in informatics?

Classification. Signup and view all the answers

What type of code is represented by 'ICD-10 - E11'?

Alphanumeric abbreviation. Signup and view all the answers

What is a significant potential consequence of software bias in electronic health records (EHRs)?

Allowing negative values in patient data. Signup and view all the answers

What is emphasized regarding data recorded in EHR systems?

The importance of understanding the recording context. Signup and view all the answers

What percentage of data resurrection rate was observed in one UK longitudinal study?

1% Signup and view all the answers

What is the role of tracking data provenance in clinical research?

To clarify the origin and history of data. Signup and view all the answers

What is a fundamental function of a Learning Health System (LHS)?

Aggregating data from disparate sources. Signup and view all the answers

Which of the following statements best describes the continuous improvement aspect of an LHS?

Improvement occurs through ongoing study and data analysis. Signup and view all the answers

In the context of Learning Health Systems, what role does 'dissemination' play?

It conveys actionable knowledge to relevant stakeholders. Signup and view all the answers

Which characteristic of an LHS ensures that patient experiences contribute to healthcare learning?

The availability of every consenting patient’s characteristics. Signup and view all the answers

What does the term 'virtous cycles' refer to when discussing how learning happens in an LHS?

The continuous cycle of data analysis and action based on results. Signup and view all the answers

Which of the following best explains the role of governance in the ultra-large scale system described?

To facilitate collaboration among diverse stakeholders. Signup and view all the answers

What aspect of an LHS is highlighted by the need for tailored messages to decision-makers?

The necessity of understanding and responding to specific contexts. Signup and view all the answers

What is a key element in establishing a culture within a Learning Health System?

Facilitating ongoing training and education for improvement. Signup and view all the answers

What is a feature of the checklist view of an LHS regarding best practices?

Best practice knowledge is immediately available to support decisions. Signup and view all the answers

In precision medicine, what is the significance of interpreting results?

It questions the credibility of the results obtained. Signup and view all the answers

What is a key benefit of utilizing Big Data in health?

It allows for personalized medicine by analyzing vast amounts of patient data. Signup and view all the answers

What is emphasized as particularly critical in health research using Big Data?

Reproducibility of results across studies. Signup and view all the answers

Which of the following is NOT a characteristic of Big Data?

Limited variety of data types. Signup and view all the answers

In the context of Big Data, what do Learning Health Systems aim to achieve?

Continuously improve health outcomes through real-time data utilization. Signup and view all the answers

What should researchers be particularly aware of when working with Big Health Data?

The potential biases that may influence health data. Signup and view all the answers

Which aspect of Big Data is highlighted as an area of concern in health research?

Biases that can alter the validity of health data. Signup and view all the answers

Which of these statements correctly describes how Big Data research differs from classical research approaches?

Big Data focuses on finding generalizable trends rather than individual cases. Signup and view all the answers

What type of patient data is typically included in the assembly of data for health predictions?

Genotypes and clinical history. Signup and view all the answers

Study Notes

Classification and Terminology

Classification: Systematic arrangement of terms, concepts, and their interrelationships. Example: An apple is the fruit of the apple tree, which belongs to the rose family.
Nomenclature: An agreed system for naming within specific fields, such as medical terms.
Type 2 Diabetes: A chronic disease characterized by high blood sugar levels; results from the body's inadequate response to insulin; most prevalent diabetes form.
Terminology: Defined set of words and expressions used in particular domains.

Health Data Codes

ICD-10: E11, used for classifying diseases.
Read v3: CT10F, a system for recording clinical findings.
UMLS: C0375115, a unified medical language system reference.
ICPC: T31, used in primary care settings.
SNOMED CT: 16403005, comprehensive clinical terminology.

Challenges in Real World Data (RWD)

RWD can be incomplete, inaccurate, or outdated; clinicians and insurers must maintain vigilance regarding data integrity.
Contextual accuracy in Electronic Health Records (EHR) is crucial for reliable data interpretation.
Reimbursement Bias: Issues such as unnecessary BMI records in lean patients.
Software Bias: EHR limitations, such as the inability to input negative values.
Data inaccuracies exemplified by a 1% resurrection rate in a UK longitudinal study, and definitions diverging from textual descriptions.
Replication troubles in oncology studies, with only 11% replicability in major findings.

Reproducibility Gap

Significant challenges in replicating research findings, as demonstrated by microarray studies; 10 out of 18 papers failed reproducibility tests.

Reporting Standards

Emphasis on traceability and accountability in clinical research.
Key reporting standards include:
- GxP: Good Practices in clinical data management and clinical practice.
- CONSORT: Standards for trial reporting.
- CDISC ADAM: Details derived variables.
- STROBE: Guidelines for observational studies.
- RECORD: Evolution focused on routinely collected observational data.

Learning Health System (LHS)

LHS defined as a system where science, informatics, and culture converge to generate knowledge seamlessly during care processes, driving improvements in health.
Strives to make the most of collected data, mitigating persistent clinical research issues, such as participant identification hurdles and the high cost of case report forms (CRFs).
Key characteristics of an LHS include:
- Regular aggregation of data from diverse sources.
- Transforming data into actionable knowledge.
- Continuous improvement through ongoing assessments.

Macro View and Engagement in LHS

Stakeholders include patients, insurers, pharmaceutical companies, technology industries, governments, and academic institutions.
Interaction across stakeholders ensures ongoing recruitment, governance, data analysis, and dissemination of findings.

Formation of Learning Communities

Essential processes involve interpreting results, analyzing data, tailoring messages for decision-makers, and fostering actionable outcomes that lead to community learning.

Precision Medicine

Integrates patient-specific factors, such as genotypes and clinical history, to develop and recommend individualized treatment strategies.

Summary

Big Data plays a vital role in enhancing healthcare treatment and outcomes, necessitating an awareness of underlying data biases.
Emphasis on responsible scientific practice and reproducibility is paramount in health research.
The vision of creating Learning Health Systems represents a transformative approach to advancing healthcare practices and research methodology.

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Description

This quiz explores critical concepts in Big Data as presented by Professor Vasa Ćurčin. It covers classification systems, nomenclature, and the relationship between terms within the context of informatics. Engage with the material and assess your understanding of these essential topics.