“Victor Babes”
UNIVERSITY OF MEDICINE
AND PHARMACY
TIMISOARA
DEPARTMENT OF
MEDICAL INFORMATICS AND BIOPHYSICS
Medical Informatics Division
www.medinfo.umft.ro/dim
2004 / 2005
MEDICAL DATABASES
COURSE 2
Operations with informations
-
Generation
Acquisition – dep. on information nature
Storage – data bases, knowledge bases
Processing – for interpretation
Commitment
Protection
Use
1. PACIENT RECORD
1.1. Terminology:
EHR - ELECTRONIC HEALTH RECORD
EPR - ELECTRONIC PATIENT RECORD
CPR – COMPUTERIZED PATIENT
RECORD
1.2. PACIENT RECORD
• a. ON PAPER
– AVANTAGES / DISADVANTAGES
•
•
•
•
EASY TO CARRY, EASY TO “BROWSE”
LOW COST, FREE FORMAT
FAST DATA ENTRY
ACCESS FROM ONE PLACE ONLY
• b. ELECTRONIC
– AVANTAGES / DISADVANTAGES
•
•
•
•
•
•
ACCESS FROM DIFFERENT PLACES, MORE PERSONS
EASY TO READ, EASY TO SEARCH INFORMATION
GOOD BASE FOR DATA ANALISYS, FOR TAKE DECISSION
NEED FOR TRAINED PERSONNEL
REQUIRE MORE TIME FOR DATA ENTRY
HIGHER COST
1.3. EHR STRUCTURE
• IDENTIFICATION DATA (apart file!!!)
• EVENTS: consultation, hospitalisation,
surgical intervention, X-ray, etc
– time scale
– ACTIONS
• OBSERVATIONS: case history, lab.results, investigations –
signals, images
• DECISIONS: diagnosis
• INTERVENTIONS, THERAPY : prescriptions
– RELATIONS
2. DATA FILES
2.1. DATA FILES
• DEFINITIONS:
– DATA = formalized representations of concepts
or facts, appropriate for processing (both
human or automatic processing)
– FILE = an organized set of data
2.2 TYPES OF DATA
•
•
•
•
•
•
QUALITATIVE – Case history (descriptive)
NUMERICAL – Laboratory Investigations
GRAPHICS – Biosignals (EKG, EEG…)
SOUNDS: Phonocardiogram
STATIC IMAGES : x-ray, NMR
DYNAMIC IMAGES – movies
(“MULTIMEDIA” FILES)
DATA FILE STRUCTURE - scheme
• 2.3. DATA FILE STRUCTURE
– a) RECORDS (+ Header + EOF)
– b) FIELDS
• NAME
• TYPE:
–
–
–
–
–
NUMERICAL
CHARACTER
LOGICAL ( Y / N )
DATE
COMMENT
• SIZE
2.4. PATIENT RECORD
3. DATA BASES
• 3.1. GENERAL NOTIONS
– DEFINITION: DATABASE = a structured
set of data - comprises both data and
relations between data
– STRUCTURE:
• FILES (with at least 1 common field - ID)
• RELATIONS between records and/or data
– PROPERTIES: independence on physical
support or language
3.2. Creating DataBases
• Data collecting
– Record Structure
– Coding
– Staff training for filling in
• Data validation
– Field type
– All possible relations
3.3. Coding and classification
• Thesaurus - terms list
• Nomenclature - associated code list
• Types of codes:
– numerical, mnemonical, hierarchical,
juxtapositional
• Taxonomy – classifications rules
– Taxonomic axes
• Nosology - classification in medicine
3.4. Classification Systems
• ICD - International Classification of Diseases (10)
• ICPC - International Classification for Primary
Care
• SNOMED – System of NOmenclature in
MEDicine - multiaxial
• Specialized: Mental, Oncology, Procedures
• MeSH / UMLS - Medical Subject Headings
Unified Medical Language System
• DRG - Diagnostic Related Groups – for finance
Case-Mix
3.5. DB CLASSIFICATION
• On data distribution:
– Local DB (all on 1 computer)
– Distributed DB (on several computers)
• On structure:
– RELATIONAL DB
– HIERARCHICAL DB
– NETWORK DB
a) RELATIONAL DB
• Logical structure (rows & columns)
• Several searching criteria
• Easy changes
b) HIERARCHICAL DB
• Tree structure: each element is subordinated to
only one element
• Fast search and processing
• No flexibility for procedure changes
4. DBMS
DataBase Management System
• a) DEFINITION:
• DBMS = a set of software tools for:
– building a DB
– control access to data
– assure data security and integrity
• Represented by:
– specialized languages
– dictionaries, nomenclature
• b) DBMS Functions
– DESCRIPTION:
• data structure
• relations
• access conditions
– DATA MANIPULATION:
• create, delete, update a record
• search, sort, edit virtual records
– USE FUNCTION:
• USER - DB dialogue
c) RELATIONAL MODEL FOR DATA
REPRESENTATION - DBMS Languages
• Languages based on relational algebra
• Languages using relational operators
– ( >, $, ", L etc)
• Transform oriented languages (SQL)
• Graphical relational languages (QBE,
Paradox)
• Examples: dBase, Foxpro, Access, Oracle
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