Informatics 2- Data Information and Knowledge

Question 1

data

Answer 1

• 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)

Question 2

information

Answer 2

meaningful data to draw conclusions

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

Question 3

knowledge

Answer 3

information justifiably believed to be true

-ex. smokers are more likely to develop lung cancer

Question 4

bit

Answer 4

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

Question 5

byte

Answer 5

a series of eight bits is a byte

-10011101

Question 6

integers

Answer 6

• numbers

- type of data

Question 7

floating point numbes

Answer 7

• type of data
• 3.5456
• decimal

Question 8

character

Answer 8

• 8 bytes is a character

- ex. “a” and “z”

Question 9

strings

Answer 9

• putting characters together makes strings

- “hello” or “ball”

Question 10

file formats

Answer 10

from least to most storage:

• image files
• text files
• sound files
• video files

Question 11

image files

Answer 11

• JPG
• GIF
• PNG
• more clarity -> more storage*

Question 12

text files

Answer 12

• txt

- doc

Question 13

sound files

Answer 13

• WAV

- MP3

Question 14

video files

Answer 14

MPG

Question 15

informatics vs. IT and computer scientists

Answer 15

• 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

Question 16

data to information

Answer 16

• 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

Question 17

information to knowledge

Answer 17

• information produces knowledge

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

Question 18

you cant convert data to knowledge

Answer 18

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

Question 19

clinical data warehouse (CDW)

Answer 19

• 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

Question 20

patients charts are made up of

Answer 20

-structured and unstructured data

Question 21

van der Lei

Answer 21

• 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

Question 22

query

Answer 22

• search
• looking for something
• query the x-ray

Question 23

CDW as a clinical resource

Answer 23

• 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

Question 24

use of aggregated clinical data

Answer 24

• 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

Question 25

CDW summary

Answer 25

- 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

Question 26

classification

Answer 26

- 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

Question 27

semantic gap

Answer 27

- 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

Question 28

tracking

Answer 28

-everything you do in health care is tracked

Question 29

concepts are poorly defined

Answer 29

- definition of sick - system in human body are connected - conceptual and computational models are rarely complete

Question 30

health information technology is really health data technology

Answer 30

- 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

Question 31

interfaces

Answer 31

- 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

Question 32

incomplete information

Answer 32

-information with missing data, but potentially obtainable

Question 33

uncertain information

Answer 33

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

Question 34

imprecise information

Answer 34

-information not as specific as it should be

Question 35

vague information

Answer 35

-unclear information

Question 36

inconsistent information

Answer 36

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

Question 37

computer algorithms (spell checks)

Answer 37

- 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

Question 38

health IT: easy to sell?

Answer 38

- 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

Question 39

future trends

Answer 39

- transition from "data processing" to "information processing" - introduce human cognition and abilities