Lecture 3 - Healthcare data, Information, and knowledge Flashcards

1
Q

Data

A

Symbols or observation or observation ps reflecting differences in the world

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2
Q

Information

A

Data with meaning

ICD- 9 code means type 2 diabetes

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3
Q

Knowledge

A

Info justifiably true

Obese more likely to develop type 2 diabetes

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4
Q

Wisdom

A

Why, alternative reasoning

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5
Q

International classification of diseases ICD

A

International standard diagnostic tool for epidemiology, using standardised codes. Therefore, the code of the medical case in Kuwait will be the same as the US of the world

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6
Q

Data

A
Computers do not understand
Only input store process and output
Zero is off One is on these are binary units
0/1 a bit
8 bits are a byte
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7
Q

Data type

A

Integers
Floating (decimal)
Characters y/n
Character strings

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8
Q

File formats

A

Data can be aggregates to formats
Common standard is of important need to charge data and info
Standard format is important for communication between computer systems
Computer programmes do not understand data on the DISC or any storage device,
Important it accepts file format

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9
Q

Types of files

A

Image jpg gif png
Text txt doc
Sound wav mps
Video mpg

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10
Q

IT vs health informatics vs computer

A

It: more concerned about data, no matter what they mean, algorithm and number

Health informatics: concerned with the meaning of data, information, knowledge, and tools that retrieve and manipulate info such as in PAVS.
Manipulate info
Information retrieval: find relationship between aspirin and heart attacks
Challenge is identifying documents that contain certain meaning

Comp sci: database searching and data info retrieval
Informatics: vocabulary ontologies, information, info retrieval

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11
Q

Data to information

A

No computer, only human interpretation is necessary
Vocabularies help convert data to info

Make data meaningful

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12
Q

Interoperability

A

Transmition of information that require consistence of interpretation for the purpose that all information systems are using the same standard and can accept and show the same interpretation

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13
Q

Information to knowledge

A

Information makes knowledge
Experience is knowledge
Carrying out research to investigate correlation
Knowledge can only be formed thru information, not data directly

Make sense of clinical data

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14
Q

Clinical data are collected via EHR

These records are composed of

A

Structured data:
exact numbers, data e.g aspirin 400 mg
Easy to manage and retrieve

Unstructured data:
Free text 
Clinical notes
Natural language- difficult to process 
Natural language processing
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15
Q

Van der lei 20 years ago

A

Data shall only be used for the purpose collected
If no purpose was defined prior to the collection of data, data won’t be used

You have to know what data you want to collect, why and how to collect them

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16
Q

Healthcare DIK: object oriented models

Object

A

Unique real world phenomena we want to model

17
Q

Healthcare DIK: object oriented models

Field/ State/ Attribute

A

A piece of data that describes the object

18
Q

Healthcare DIK: object oriented models

Method/ behaviour

A

What the object does or what we do to it to change states

19
Q

Class

A

A common form/ template to describe unique objects

20
Q

Inheritance

A

Baby of classes
Parent may be age
Child may be paedriatrics/geriatric

21
Q

Clinical data warehouse

A
Shared database that 
Collects
Integrates 
Stores 
Clinical data from a variety of sources including EHR radiology 

Main source of all EHR
MASTER DATABASE

Supports queries for groups

22
Q

Staging

A

Extract, transform, and load into a common database

23
Q

EHR

A

Designed for real-time updating and retrieval of individual data

24
Q

Facts

A

Pieces of information queried by users
Diagnosis
Demographic
Lab tests

25
Q

Dimension

A

Describe facts

Us tiki see using summary statistics
Count, mean, median

26
Q

CDW as a clinical resource

A
Monitor
Identify 
Help 
Conduct
Build

Can recognise records for patients with illness
Info to knowledge
Tools eg simple descriptive analysis

27
Q

Monitor

A

Quality of a query measuring specific population

28
Q

Identify

A

Trends of clinical and transitional research to link r3search with clinical practice

29
Q

Help

A

Specialist to track pathogens

Faster reporting in electronic CDW

30
Q

Conduct

A

Surveillance for natural or man made illness to public health agencies

31
Q

Build

A

Platform of informatics
One made by Harvard medical school
Warehouse of clinical data from multiple resources, so queries can be made from one platform to retrieve data from multiple institutions eg integrating biology and the bedside i2b2

32
Q

Why is informatics difficult?

A

Banking is easy as gap in data and information is narrow
Healthcare concepts are poorly defined as systems are connected
Data is poorly managed

33
Q

Semantic gap

A

The difference between data and information as Med is only using data

34
Q

Obstacles of informatics in healthcare

A

Incomplete info- missing data but may be obtainable
Uncertain info- not sure true or false
Imprecise info- info not as specific
Vague info- unclear info
Inconsistent info- can’t hold both beliefs at the same time

We need to design in an accurate and purposeful way

35
Q

Future trends

A

Data processing to info processing
Introduce cognition and abilities (mental processing)
Creating systems that store specific information specific eg only cancer patients from population
Expert system