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Health Informatics and Evidence Based Practice > Big Data in Healthcare > Flashcards

Big Data in Healthcare Flashcards

1
Q

Definition of Big Data

A

Data whose scale, distribution, diversity and/or timeliness requires the use of new technical architecture and analysis to enable the insights that unlock new sources of business value

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2
Q

Definition of Real World Data

A

Data regarding effects of health interventions not collected during RCTs but during routine clinical practice
This includes clinical, economic outcomes, patient reported outcomes

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3
Q

Definition of Real World Evidence

A

Evidence derived from RWD

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4
Q

Definition of Real World Studies

A

Non traditional trials looking at effectiveness in routine clinical practice: pragmatic, adaptive trials, drug utilisation studies

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5
Q

Definition of response adaptive randomisation

A

Procedure that uses past treatment assignments and patient responses to select the probability of future treatment assignments

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6
Q

Definition of adaptive clinical trials

A

Evaluates an intervention by observing participant outcomes and modifying parameters of a trial according to observations

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7
Q

Definition of reimbursement bias

A

Data that does not need to be collected into EHRs is not recorded

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8
Q

Definition of software bias

A

EHR system doesnt allow for certain values to be inputted

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9
Q

What is Big Data

A

Data whose scale, distribution, diversity and/or timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value

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10
Q

What is Big Data characterised by

4Vs

A

Volume, zettabytes
Variety, generally not structured, in different forms
Velocity, streaming data that is constantly being generated
Veracity, uncertainty of the quality

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11
Q

How is Big Data used in medicine

A

Focus on value found in the data and apply it to clinical practice

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12
Q

What are the 2 traditional assumptions of Big Data that don’t apply in medicine

A

Correlation can replace causality given sufficient no

  • We must understand why a=> b before using this knowledge
  • If not, might lead to harm

Ignores structure of the population, assumes it is big enough to be valid
-Doesn’t control for bias

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13
Q

What is Real World Data

What are medical examples of RWD

A

Data regarding effects of health interventions not collected during an RCT

During routine clinical practice

  • Clinical, economic and patient reported outcomes
  • EHRs, observational studies, patient registries
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14
Q

What is Real World Evidence and Real World Trials

A

Can derive Real World Evidence from RWD

Real World Studies consist of non traditional trials which investigate effectiveness in routine clinical practice

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15
Q

What are the 4 applications of RWD

  • in trials
  • observational trials
  • demographic studies
  • epidemiology
A

When RCTs cant be used

Assess products and therapies already in use
-observing drug safety and comparing product effectiveness

Recording treatment patterns
-in diverse patient populations to target products and services

Characterise disease and patient populations
-understand epidemiology trends and disease management opportunities

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16
Q

How can pharmaceutical companies use RWD

A

Value based pricing methods
Gain a better understanding of disease and treatments
Informing product and trial design to address opportunities to gain and defend market access

17
Q

What are data registries

A

Collection of info about individuals, usually focused around a specific diagnosis/condition
They are on a voluntary basis

18
Q

What are claims databases and why are they important in health data collection

What are the 3 pros and 2 cons

A

Electronic record of claims submitted for the purpose of payment for services in countries without a health service

Pros

  • Provides a longitudinal view into a patient’ use of healthcare services
  • Economic theory provides assurance of services inclusion
  • Typically includes reimbursed patient paid amounts

Cons

  • Can lack clinical depth
  • Patients are not representative of the population, only those with health insurance included
19
Q

How would you use social networking data in the future in research

A

Survey patients on conduct of more patient centric clinical trials

Design of patient reported outcome measures that matter to the patients

Faster way for researchers to conduct observational studies

Mechanism for regulators to collect info on drug related AEs

20
Q

What are the current views of sharing social media data for care improvement
4 opinions

A

Generally are happy to share health data

  • help doctors improve care
  • help research and learn more about their diseases
  • help patients like themselves

Many are concerned about health data being used in a detrimental way

21
Q

Describe the current use of EHR system data in research
Why is primary care data so good
What are the current problems with current EHRs

A

Data needed extracted from routinely collected clinical data
Primary care data covers cradle => grave

Needs integration with other data sources for treatments outside primary care and context
Hospital EHR systems all vary from department to department

22
Q

How would you address the downfalls of RCTs with RWD

How would RWD benefit applications for Stage 1 drug trials

A

May address efficacy, effectiveness gap

By introducing RWD into preauthorisation drug development => improve info available at initial market authorisation

23
Q

What are the 4 main challenges in clinical trials

A

Too expensive and difficult to run

Findings apply to the average of a large population, not to targeted subgroups
Or findings are too narrow, population not represented

Morally problematic randomisation

24
Q

How would you use Big Data in clinical trials

What would the possible effects of this be

A

Adaptive designs incoorporate rules to adjust trial aspects during enrollment
Use data to pick suitable patients for trials

  • generate generalizable estimates of treatment
  • understand heterogeneity of treatment effect
25
Q

How would you use response adaptive randomization in trials

What does it aim to achieve

A

Randomization procedure that uses past treatment assignments and patient responses to select the probability of future treatment assignments

Aims to reduce the odds of patients being allocated to the poorly faring treatment group

26
Q

How would you use adaptive clinical trials

What does this aim to achieve

A

Evaluates an intervention by observing participant outcomes and modifying parameters of a trial according to the observations

This continues throughout the trial

Aims to quickly identify drugs with a therapeutic effect and find the patient population for whom the drug is most appropriate

27
Q

How would you overcome the slow translation of knowledge

A

Clinicians can receive the feedback of the data that they entered into the system (results of trials and experiments)

Computer interpretable guidelines that can semi automate the curation of evidence bases
This knowledge can be disseminated to the clinicians

28
Q

How can EHR data be used to help healthcare providers and researchers

A

Automatic eligibility checking of patients for trials

Find patients with the appropriate profile for a clinical trial

Automatic insertion of data into electronic forms to save the clinician time

29
Q

What are the 4 main obstacles to Big Data collection

A

Restrictive policies on data access
Lack of standard policy of patient data confidentiality
No international standardisation on data collection routes
Expensive and restrictive licenses to data access

30
Q

Describe the policies on data governance

What are the current problems with data governance

A

Each data set has a different associated set of info governance regulations

31
Q

Describe research data governance in the UK

  • What type of approval do you need to start a clinical trial
  • How can NHS data be used
A

Clinical trials need ethical approval from National Research Committee

NHS data collected for clinical/admin purposes can be used without consent but not always for research even though the usage of the data is the same in observational studies

32
Q

What are the 3 main problems with routine data quality

A

Data errors
-use of abbreviations and terminology not standardised

Reimbursement bias
-Data that doesnt need to be collected is not recorded

Software bias
-System doesn’t allow certain values to be inputted

Health Informatics and Evidence Based Practice (7 decks)

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