Technology, Data & AI Glossary

Begin with systems and data ownership

A practice management system organizes administrative and operational work such as scheduling, intake, provider data, authorization tracking, claims, and reporting. Its scope varies by product. Identify which system is authoritative for each field and how changes propagate.

Data governance assigns decision rights for data definitions, collection, quality, access, retention, correction, sharing, lineage, and disposal. A data warehouse consolidates data for reporting or analysis. The warehouse should preserve source, extraction time, transformation logic, version, and refresh status so a dashboard can be traced back.

Data migration moves data between systems or formats. Inventory records, fields, attachments, relationships, audit trails, user identities, consents, retention classes, and open work before mapping. Reconcile counts and critical fields, preserve originals, test permissions, and keep a rollback or correction path.

Interfaces move information and events

An application programming interface is a defined way for software to request or exchange functions and data. A webhook sends an event notification to a configured endpoint when a trigger occurs. APIs and webhooks need authentication, authorization, versioning, validation, retries, idempotency, logging, and error handling.

Health Level Seven refers to the HL7 standards organization and its standards. Fast Healthcare Interoperability Resources, or FHIR, is an HL7 standard built around modular resources, profiles, terminology, and exchange patterns. The official FHIR specification defines the standard. A FHIR label alone does not prove that two systems use the same profile, fields, terminology, version, authorization, or workflow.

Robotic process automation uses software to perform repeatable rule-based actions across interfaces. It can reduce manual copying, but screen changes, ambiguous states, expired credentials, and partial failures need detection and human routing.

Identity and encryption protect different layers

Single sign-on lets a user authenticate through a central identity provider for multiple applications. Multi-factor authentication requires more than one factor category. Each control needs enrollment, recovery, termination, privileged-access, and emergency-access procedures.

Encryption at rest protects stored data through cryptographic controls. Encryption in transit protects data moving across networks. Key management, endpoints, backups, logs, exports, and authorized access still matter.

For HIPAA regulated entities, 45 CFR 164.312 contains technical-safeguard standards and implementation specifications. HHS cloud guidance explains that a cloud provider maintaining ePHI for a covered entity or business associate is a business associate even when it holds encrypted data without the key. Entity status, permissible use, risk analysis, agreements, configuration, and each party's duties require separate review.

Vendor risk management evaluates a vendor's role, data, subcontractors, access, security, privacy, resilience, legal terms, performance, change process, incidents, and exit plan. The NIST Cybersecurity Framework provides voluntary risk-management guidance. The framework does not certify a product or replace healthcare obligations.

Backups and recovery answer different questions

A data backup is a recoverable copy of data or systems. Test whether the copy can be restored, authenticated, reconciled, and used within the required workflow.

Disaster recovery covers restoration of technology and data after a disruption. A recovery point objective identifies the point in time to which data should be recovered, reflecting tolerated data loss. A recovery time objective is the planned maximum time to restore a function or system before unacceptable impact.

NIST SP 800-34 Rev. 1 is federal information-system contingency-planning guidance that private practices may adapt. Targets guide design and testing; they are not guarantees. Technical availability is only one recovery milestone. Access, data integrity, clinical safety, records, payroll, claims, and open work still need reconciliation.

Govern AI as a complete use case

Artificial intelligence is a broad category of systems performing tasks associated with prediction, generation, classification, reasoning, or decision support. Machine learning develops models from data to make predictions or produce outputs without encoding every rule directly.

AI governance assigns accountability for permitted uses, data, model selection, validation, bias, human review, monitoring, incidents, change, vendor management, and retirement. Algorithmic bias is systematic unfair or distorted performance associated with data, design, deployment, or context. Test relevant subgroups, error types, missing cases, accessibility, and downstream effects.

The NIST AI Risk Management Framework is voluntary guidance. CASP's Practice Parameters for AI address ABA service-provider use. Neither validates a product, dataset, clinical output, or legal conclusion.

A controlled migration example

A fictional practice locks 25 client-record packages for migration. Twenty-three transfer with expected notes and attachments. Twenty-one also pass identity, relationship, permission, timestamp, and checksum review, so validated migration completeness is 21/25, 84%. The four failed or held packages remain visible.

A restore exercise then recovers the test copy to the planned point in 42 minutes. That result describes one scenario. The exercise cannot prove the production RPO, RTO, every data type, clinical readiness, or vendor compliance. Repeat it across critical dependencies and failure conditions.

Start or grow your ABA practice with Finni. Confirm current product scope, security terms, interoperability, validation, human-review boundaries, recovery evidence, and export options during diligence.