“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA DEPARTMENT OF MEDICAL INFORMATICS AND BIOPHYSICS Medical Informatics Division www.medinfo.umft.ro/dim.

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“Victor Babes” UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA DEPARTMENT OF MEDICAL INFORMATICS AND BIOPHYSICS Medical Informatics Division / 2005

MEDICAL DATABASES COURSE 2

Operations with informations 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 PAPERa. ON PAPER –AVANTAGES / DISADVANTAGES EASY TO CARRY, EASY TO “BROWSE”EASY TO CARRY, EASY TO “BROWSE” LOW COST, FREE FORMATLOW COST, FREE FORMAT FAST DATA ENTRYFAST DATA ENTRY ACCESS FROM ONE PLACE ONLYACCESS FROM ONE PLACE ONLY b. ELECTRONICb. ELECTRONIC –AVANTAGES / DISADVANTAGES ACCESS FROM DIFFERENT PLACES, MORE PERSONSACCESS FROM DIFFERENT PLACES, MORE PERSONS EASY TO READ, EASY TO SEARCH INFORMATIONEASY TO READ, EASY TO SEARCH INFORMATION GOOD BASE FOR DATA ANALISYS, FOR TAKE DECISSIONGOOD BASE FOR DATA ANALISYS, FOR TAKE DECISSION NEED FOR TRAINED PERSONNELNEED FOR TRAINED PERSONNEL REQUIRE MORE TIME FOR DATA ENTRYREQUIRE MORE TIME FOR DATA ENTRY HIGHER COSTHIGHER COST

1.3. EHR STRUCTURE 1.3. EHR STRUCTURE IDENTIFICATION DATA (apart file!!!)IDENTIFICATION DATA (apart file!!!) EVENTS: consultation, hospitalisation, surgical intervention, X-ray, etcEVENTS: consultation, hospitalisation, surgical intervention, X-ray, etc –time scale –ACTIONS OBSERVATIONS: case history, lab.results, investigations – signals, imagesOBSERVATIONS: case history, lab.results, investigations – signals, images DECISIONS: diagnosisDECISIONS: diagnosis INTERVENTIONS, THERAPY : prescriptionsINTERVENTIONS, THERAPY : prescriptions –RELATIONS

2. DATA FILES

2.1. DATA FILES DEFINITIONS: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)QUALITATIVE – Case history (descriptive) NUMERICAL – Laboratory InvestigationsNUMERICAL – Laboratory Investigations GRAPHICS – Biosignals (EKG, EEG…)GRAPHICS – Biosignals (EKG, EEG…) SOUNDS: PhonocardiogramSOUNDS: Phonocardiogram STATIC IMAGES : x-ray, NMRSTATIC IMAGES : x-ray, NMR DYNAMIC IMAGES – moviesDYNAMIC IMAGES – movies (“MULTIMEDIA” FILES)

DATA FILE STRUCTURE - scheme

2.3. DATA FILE STRUCTURE2.3. DATA FILE STRUCTURE –a) RECORDS (+ Header + EOF) –b) FIELDS NAMENAME TYPE:TYPE: –NUMERICAL –CHARACTER –LOGICAL ( Y / N ) –DATE –COMMENT SIZESIZE

2.4. PATIENT RECORD

3. DATA BASES 3.1. GENERAL NOTIONS3.1. 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)FILES (with at least 1 common field - ID) RELATIONS between records and/or dataRELATIONS between records and/or data –PROPERTIES: independence on physical support or language

3.2. Creating DataBases Data collectingData collecting –Record Structure –Coding –Staff training for filling in Data validationData validation –Field type –All possible relations

3.3. Coding and classification Thesaurus - terms listThesaurus - terms list Nomenclature - associatedcode listNomenclature - associated code list Types of codes:Types of codes: –numerical, mnemonical, hierarchical, juxtapositional Taxonomy – classifications rulesTaxonomy – classifications rules –Taxonomic axes Nosology - classification in medicineNosology - classification in medicine

3.4. Classification Systems ICD - International Classification of Diseases (10)ICD - International Classification of Diseases (10) ICPC - International Classification for Primary CareICPC - International Classification for Primary Care SNOMED – System of NOmenclature in MEDicine - multiaxialSNOMED – System of NOmenclature in MEDicine - multiaxial Specialized: Mental, Oncology, ProceduresSpecialized: Mental, Oncology, Procedures MeSH / UMLS - Medical Subject HeadingsMeSH / UMLS - Medical Subject Headings Unified Medical Language System DRG - Diagnostic Related Groups – for financeDRG - Diagnostic Related Groups – for financeCase-Mix

3.5. DB CLASSIFICATION On data distribution:On data distribution: –Local DB (all on 1 computer) –Distributed DB (on several computers) On structure:On structure: –RELATIONAL DB –HIERARCHICAL DB –NETWORK DB

a) RELATIONAL DB Logical structure (rows & columns)Logical structure (rows & columns) Several searching criteriaSeveral searching criteria Easy changesEasy changes b) HIERARCHICAL DB b) HIERARCHICAL DB Tree structure: each element is subordinated to only one elementTree structure: each element is subordinated to only one element Fast search and processingFast search and processing No flexibility for procedure changesNo flexibility for procedure changes

4. DBMS DataBase Management System a) DEFINITION:a) DEFINITION: DBMS = a set of software tools for: DBMS = a set of software tools for: –building a DB –control access to data –assure data security and integrity Represented by:Represented by: –specialized languages –dictionaries, nomenclature

b) DBMS Functionsb) DBMS Functions –DESCRIPTION: data structuredata structure relationsrelations access conditionsaccess conditions –DATA MANIPULATION: create, delete, update a recordcreate, delete, update a record search, sort, edit virtual recordssearch, sort, edit virtual records –USE FUNCTION: USER - DB dialogueUSER - DB dialogue

c) RELATIONAL MODEL FOR DATA REPRESENTATION - DBMS Languages Languages based on relational algebraLanguages based on relational algebra Languages using relational operatorsLanguages using relational operators –( >, , ,  etc) Transform oriented languages (SQL)Transform oriented languages (SQL) Graphical relational languages (QBE, Paradox)Graphical relational languages (QBE, Paradox) Examples: dBase, Foxpro, Access, OracleExamples: dBase, Foxpro, Access, Oracle

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