ABPM Clinical Informatics key terms

Any electronic or non-electronic process that gives clinicians, staff, or patients person-specific information to help them make health decisions. It includes alerts, order sets, reference links, and calculators built into workflow.

A design framework requiring that decision support reach the right person, in the right workflow format, at the right moment, through the right channel, with the right evidence-based content. It helps teams avoid alert fatigue and ineffective interventions.

Desensitization that occurs when users face too many or poorly targeted warnings, causing them to override or ignore both useful and harmful alerts. Reducing low-value alerts and increasing specificity are common mitigations.

The proportion of decision support alerts that clinicians dismiss or bypass rather than accept. It is used as a monitoring metric for alert design quality and for detecting unsafe or noisy rules.

A system that lets clinicians enter orders electronically instead of on paper. It enables structured, legible orders and serves as the delivery channel for many medication and safety decision support rules.

A longitudinal, digital record of a patient's health information shared across care settings within an organization. It supports clinical documentation, orders, results review, and reporting.

The sequence of tasks, decisions, and handoffs clinicians perform while delivering care. Informatics interventions succeed when they are designed around real workflow rather than requiring users to abandon it.

The extent to which a system can be used by specified users to achieve goals with effectiveness, efficiency, and satisfaction. It is evaluated through methods such as heuristic review, think-aloud testing, and task timing.

The mental effort required to process information and complete a task. Poor interface design raises cognitive load, increasing errors and slowing clinicians, so interfaces should offload memory and reduce unnecessary choice.

The study of how people interact with systems, tools, and environments, including perception, memory, and physical limits. It informs design that fits human capabilities and reduces error-prone conditions.

The electronic movement of health information among organizations according to national standards. It supports continuity of care when patients receive treatment across separate health systems.

The ability of different information systems to exchange data and use the exchanged information correctly. It spans technical, syntactic, semantic, and organizational layers of agreement.

A modern Health Level Seven standard that represents health data as modular resources exchanged over web APIs. It supports both granular queries and document exchange and is widely used for apps and population reporting.

An older pipe-delimited messaging standard still widely used for admissions, orders, and results between systems. Its flexibility enables broad adoption but allows variation that requires interface tuning.

Middleware that receives, translates, routes, and monitors messages between systems, such as converting HL7 v2 feeds into EHR data. It often handles validation, queuing, and error alerting.

The process of linking local codes, terms, and values to standard vocabularies so that data can be compared, exchanged, and reported consistently across systems.

A quantified indicator of healthcare structure, process, outcome, or experience used to assess performance. Measures require defined denominators, numerators, exclusions, and data sources to be computed reliably.

An electronic clinical quality measure computed from structured EHR data using standardized logic and value sets. It allows automated performance reporting but depends on complete, correctly coded documentation.

A set of strategies that use aggregated data to identify, stratify, and intervene on the health needs of a defined group. It emphasizes prevention, care gaps, and coordination across the continuum.

The use of data and models to rank patients by their likelihood of adverse outcomes or high future cost. It helps target outreach and resources toward those who benefit most.

Statistical and machine-learning methods that estimate future events such as readmission or deterioration from historical data. Models require validation, monitoring for bias, and clear thresholds for action.

Systematic error in a model's outputs that unfairly disadvantages groups, often because training data reflect past inequities or omit relevant variables. It calls for subgroup performance testing and mitigation.

Organized, searchable collections of patients with a shared condition or characteristic, used to track care gaps, outcomes, and outreach. They underpin many population health and quality improvement programs.

The principle that everyone should have a fair opportunity to attain their best health, requiring attention to avoidable differences across groups. Informatics supports it through inclusive data collection and equitable design.

Conditions of birth, growth, living, working, and aging that shape health outcomes, such as housing, food security, and transportation. Capturing them in structured data enables screening and referral.

U.S. federal law establishing national standards for protecting individually identifiable health information. It defines covered entities, permitted uses and disclosures, and required administrative, physical, and technical safeguards.

Individually identifiable health information held or transmitted by a covered entity or business associate. It includes identifiers linked to health data and is subject to privacy and security protections.

A HIPAA principle requiring that uses, disclosures, and requests for protected health information be limited to the least amount needed for the intended purpose.

The process of removing or transforming identifiers so that health data no longer identify individuals, either by safe harbor removal of specified identifiers or by expert statistical determination.

An access model that grants permissions according to a user's job role rather than individual identity. It simplifies administration but requires periodic review to prevent excessive access.

A chronological record of system activity that captures who accessed or changed what and when. It supports security investigation, compliance review, and detection of inappropriate access.

An unintended occurrence that harmed or could have harmed a patient, including near misses. Reporting systems and structured review help identify system causes rather than blame individuals.

An organizational approach that distinguishes human error from at-risk and reckless behavior, encouraging reporting while holding people accountable for clear violations. It supports learning from safety events.

A structured team method for reconstructing a harmful event and identifying underlying system contributors rather than a single proximate cause. Findings should lead to tested, sustainable corrective actions.

A rapid-cycle improvement method in which a change is planned, tested on a small scale, studied for results, and acted upon. Repeated cycles refine interventions before wider spread.

The study of methods that promote the systematic uptake of evidence-based practices and informatics tools into routine care. It examines barriers, facilitators, and strategies for sustained adoption.

The structured approach to preparing people and processes for a system or workflow change, including communication, stakeholder engagement, training, and reinforcement. It addresses human resistance, not just technology.

The decision-making structures and processes that set priorities, allocate resources, and enforce standards for information systems. Effective governance aligns clinical, operational, and technical stakeholders.

The discipline that applies data, information, and knowledge to improve health and care delivery through information systems. It blends clinical practice, computer science, and organizational change.

Sensitivity is the proportion of true cases correctly identified by a test or rule; specificity is the proportion of non-cases correctly identified. Together they characterize discrimination at a chosen threshold.

The proportion of patients flagged by a test or alert who truly have the condition. It depends on prevalence and directly influences how much clinicians trust and act on an alert.