The Clinical Information Framework: Linking Informativeness, Utilisation, and Value in Clinical Information Processes

Rationale: Clinical information is central to clinical judgement and decision-making, but its informativeness, processing, utilisation, and consequences have largely been examined through separate theoretical perspectives. Aims and Objectives: To develop the Clinical Information Framework (CIF), an information-centred framework linking clinical states, information representations, information-processing agents, clinical responses, and subsequent consequences. Method: A conceptual synthesis integrated information theory, information-theoretic models of bounded rationality, Social Judgment Theory, decision-threshold approaches, and decision-analytic and value-of-information perspectives. These traditions were compared according to the aspects of clinical information they address and integrated within a common analytical architecture. Results: CIF distinguishes three evaluative dimensions: clinical informativeness, defined by uncertainty reduction about a task-relevant target; clinical information utilisation, concerning how information is incorporated into clinical judgement or action; and clinical information value, concerning differences in clinically meaningful consequences between information conditions. The framework also addresses information transformation and task-relevant preservation, the conditional contribution of new information, and constraints on information acquisition and processing. Information-response coupling provides an empirical indicator of utilisation but does not by itself establish how information was used, and a change in clinical response does not establish clinical value. Conclusion: CIF provides a common structure for distinguishing what clinical information conveys, how it is transformed and used, and what consequences follow. It may support empirical evaluation of documentation, communication, decision-support, and artificial intelligence-mediated information processes across healthcare.