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Informatics > Informatics 2- Data Information and Knowledge > Flashcards

Informatics 2- Data Information and Knowledge Flashcards

1
Q

data

A
  • observations
  • may or may not be meaningful
  • computers do not understand
  • input, store, process
  • output zero (off) and one (on)
  • each zero or one is known as a bit
  • a series of eight bits is called a byte
  • ex. 112134493 (pt id number)
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2
Q

information

A

meaningful data to draw conclusions

  • take data and make conclusion
  • ex. patient ID, interpreting diagnoses codes
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3
Q

knowledge

A

information justifiably believed to be true

-ex. smokers are more likely to develop lung cancer

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4
Q

bit

A
  • only zero’s and ones’
  • only language a computer understands
  • computers do not understand you (they are dumb!)
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5
Q

byte

A

a series of eight bits is a byte

-10011101

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6
Q

integers

A
  • numbers

- type of data

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7
Q

floating point numbes

A
  • type of data
  • 3.5456
  • decimal
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8
Q

character

A
  • 8 bytes is a character

- ex. “a” and “z”

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9
Q

strings

A
  • putting characters together makes strings

- “hello” or “ball”

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10
Q

file formats

A

from least to most storage:

  • image files
  • text files
  • sound files
  • video files
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11
Q

image files

A
  • JPG
  • GIF
  • PNG
  • more clarity -> more storage*
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12
Q

text files

A
  • txt

- doc

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13
Q

sound files

A
  • WAV

- MP3

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14
Q

video files

A

MPG

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15
Q

informatics vs. IT and computer scientists

A
  • computer science- software
  • informatics- broad spectrum, hardware, software, combine them into a relative way
  • IT search or sort data more efficiently
  • takes data and makes it easier to use (sort, filter)
  • informatics manipulates information (tools vary, could be computers)
  • information retrieval:
  • relationship between aspirin and heart attack -> finding correlations
  • problem is identifying documents that contain certain meaning
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16
Q

data to information

A
  • vocabularies help convert data into information
  • ICD-10 162.9 is meaningless datum
  • interpreting ICD-10-CM as “Lung neoplasm, not otherwise specified” turns datum into a unit of information
  • human interpretation is necessary
  • interoperability- transmission of information
  • consistency of interpretation
17
Q

information to knowledge

A
  • information produces knowledge

- in clinical world, evidence exists that knowledge is true rather than proven fact

18
Q

you cant convert data to knowledge

A

-it needs to put into something meaningful first (information)

19
Q

clinical data warehouse (CDW)

A
  • clinical data are collected via electronic health records (EHRs)
  • clinical records composed of:
  • structured data- billing codes, lab results, ICD-9-CM 162.9 = Lung Neoplasm, medication lists etc… -> easier to manage and retrieve
  • unstructured or (free text)- clinical notes, natural language, but difficult to process -> natural language processing (NLP)
  • shared database that collects, integrates, and stores clinical data from a variety of sources including electronic health records, radiology and other information systems
  • staging: extract, transform and load
  • EHR designed for real time updating of individual data
  • CDW supports queries (a search) for groups
  • take all the information and make a diagnosis
  • cant be deleted after it enters the data warehouse
20
Q

patients charts are made up of

A

-structured and unstructured data

21
Q

van der Lei

A
  • data shall be used only for the purpose for which they were collected
  • this law has a collateral: if no purpose was defined prior to the collection of the data, then the data should not be used
  • if there is no purpose why collect it?
  • waste of time! we dont have time
22
Q

query

A
  • search
  • looking for something
  • query the x-ray
23
Q

CDW as a clinical resource

A
  • monitor quality to query for specific quality measures in specific pt populations
  • all the people in the background are communicating with pt, looking that everything is being handled and input correctly
  • clinical and translational researchers to identify trends and link research with clinical practice
  • hospital infection control specialists track pathogens
  • public health agencies conduct surveillance for natural or man-made illnesses
  • informatics for integrating biology and the bedside (i2b2) project by Harvard
24
Q

use of aggregated clinical data

A
  • recognize records for pts with specific conditions
  • could be use of billing codes (controlled vocab -> ICD-10-CM)
  • concept extraction- identifying concepts within unstructured data -> extracting information from free text clinical notes (discharge summaries or pathology reports)
  • need to map between terms or phases and controlled vocab with accuracy
  • good notes -> better care
25
Q

CDW summary

A
  • CDWs more than just archive data
  • must turn data into information and knowledge
  • make sense of clinical data
  • make clinical data meaningful ( data to information) and information to knowledge
26
Q

classification

A
  • problem of categorizing data into two or more categories
  • algorithm can be used to learn a representation of features that characterizes annotated positive (pts with breast cancer) and negative (pts without breast cancer) cases
  • new cases can then be categorized automatically
  • someone programs the computer -> how do you know they were correct
27
Q

semantic gap

A
  • difference between data and information = meaning (semantics)
  • right answer wrong data -> right data wrong answer
  • in banking the gap between data and information is narrow -> bc its numbers!
  • direct link between data (numbers) and information (account balances)
  • did collect data correctly and did you interpret it correctly
28
Q

tracking

A

-everything you do in health care is tracked

29
Q

concepts are poorly defined

A
  • definition of sick
  • system in human body are connected
  • conceptual and computational models are rarely complete
30
Q

health information technology is really health data technology

A
  • existing technology stores, manipulates and transmits data (symbols), not information (data + meaning)
  • in health care, data do not fully represent the meaning
  • you can collect data but it may not answer your question
  • all data needs a purpose
31
Q

interfaces

A
  • in health care systems we use HL7 (health language 7)
  • HL7 reference information model (RIM)
  • connects multiple systems together
  • HEALTH CARE ONLY
  • complex
  • does not necessarily match all health care environments
  • take the first group of numbers and matching it to a second group of numbers -> tells the system where to point something correctly (area)
  • matching fields
  • lock and key
  • interface people teach other people how to do this -> they dont get paid to do it for other people
32
Q

incomplete information

A

-information with missing data, but potentially obtainable

33
Q

uncertain information

A

-not possible to objectively determine whether it is true or false

34
Q

imprecise information

A

-information not as specific as it should be

35
Q

vague information

A

-unclear information

36
Q

inconsistent information

A

-information that contains 2 or more assertions that cannot simultaneously hold

37
Q

computer algorithms (spell checks)

A
  • computers and programming languages process discrete symbols according to precise formal rules
  • they do not make sense of a highly ambiguous information
  • person who wrote the algorithm is smart not the computer
38
Q

health IT: easy to sell?

A
  • no
  • improve health care quality, prevent medical errors, and increase efficiency
  • but no
  • increase mortality, increase error and decrease efficiency
  • not just HIT, but how it is implemented and in what context clinical results are determined
39
Q

future trends

A
  • transition from “data processing” to “information processing”
  • introduce human cognition and abilities

Informatics (15 decks)

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