Notes from module 3 of the Interprofessional Health Informatics course I’m working on (plus side side reading that I did to fill in some blanks/learn more about some things mentioned in the course)
- Data: observations (numbers, terms)
- Information: data with meaning (answering to questions about who, what, when, where, why)
- Knowledge: justifiable beliefs based on data and information
- e.g., “89” and “female” are data. But what do they mean? It might signify an “89 year old woman”, but it could also be an “89 lb woman”… or even an “89 kg woman”… or “a woman with a temperature of 89°F”! We need the “data definitions” in order to make sense of these data (i.e., to turn these data into information)
Semantic tools – used to help transform data into information and knowledge
- controlled vocabularies: words that have definitions and are represented by a code – e g. ICD-9
- taxonomies/ontologies: forms of formal hierarchical classifications. Consist of defined terms and their inter-relationships – e.g., the Omaha System is an ontology for population health
- interface terminologies: terms that facilitate direct entry of information into a computer (e.g., Clinical Care Classification System (CCC); NANDA, Nursing Interventions Classification (NIC))
- reference terminologies: a set of concepts and relationships that provide a common reference point for comparisons and aggregation of data (e.g., LOINC, Systematized Nomenclature of Medicine (SNOMED) CT)
- interoperability standards: technical standards for the exchange, integration, sharing, and retrieval of electronic health information) – e.g., HL7, EXtensible Markup Language (XML)
- The Informaticist him- or herself are also a semantic tool in that they transform data to knowledge based on their own worldview. Our cognitive function/reasoning converts data to information all the time.
- Especially useful when:
- semantic equivalence – more than one word or expression means the same thing (e.g., elevated blood glucose = high blood sugar; myocardial infarction = heart attack)
- semantic gap – data may not fully represent meaning, resulting in a large gap between data and information (e.g., elevated blood glucose – what did the patient eat/drink? what meds are they on? what is their typical blood glucose level?)
- IT professionals: manage data (using hardware, software, algorithms)
- Informaticists: deal with information and knowledge
- ideally, IT professionals understand the information/knowledge needs of Informaticists and Informaticists understand data – and everyone works together in an interprofessional way
