Informatics Foundations and Theory
What to know
Informatics foundations test whether you can name the models that explain how information becomes knowledge and how systems get adopted. Expect the data–information–knowledge–wisdom (DIKW) hierarchy, the informatics framework (data, information, knowledge, decision support), and nursing-specific models such as the Data-Information-Knowledge Model (Nelson) and the Foundation of Knowledge model. Also know systems theory (input, throughput, output, feedback) and change/adoption theories: Rogers' diffusion of innovations (innovators, early adopters, early/late majority, laggards), Lewin's unfreezing-changing-refreezing, and Kotter's steps. Benner's novice-to-expert and Patricia Benner-style competency framing appear in questions about user skill progression.
Traps
- Conflating data (discrete facts) with information (data with context/meaning) and knowledge (synthesized, actionable). A lab value is data; a trended value interpreted against a baseline is information; a decision rule built on it is knowledge/decision support.
- Assuming technology alone drives adoption. Diffusion theory says perceived relative advantage, compatibility, complexity, trialability, and observability drive uptake-complexity is the trap (higher complexity slows adoption).
- Mixing process models (how work flows) with theoretical models (why adoption happens).
Worked example / rule of thumb
A question describes nurses ignoring a new alert. Apply diffusion: ask which attribute is weak. If the alert fires constantly and is hard to interpret, complexity is high and observability low-the fix is usability and feedback, not more training alone. Rule of thumb: data → information → knowledge → decision, and each step needs context or rules added by a human or system.
Key concepts
DIKW, Foundation of Knowledge model, systems theory, diffusion of innovations, Lewin/Kotter change models, novice-to-expert.