RHIA

Information governance (IG) is the strategic framework through which a healthcare organization manages, controls, and uses its information assets across the entire data lifecycle. This section covers IG principles and program components, data quality and integrity, EHR management and interoperability, data governance structures and maturity, information risk management, and the health record content, documentation, and data standards that make health data reliable enough for care, reimbursement, and quality reporting.

In healthcare, information governance encompasses the policies, procedures, and technologies that keep health information accurate, accessible, secure, and compliant. It emerged as organizations recognized data as a critical organizational asset that requires systematic management. Traditional records management focuses mainly on document storage and retention; IG takes a holistic view of how information is created, captured, valued, stored, used, controlled, accessed, archived, and deleted.

AHIMA defines information governance as an organization-wide framework for managing information throughout its lifecycle and for supporting the organization's strategy, operations, regulatory, legal, risk, and environmental requirements. The definition makes IG an enterprise-wide responsibility, not an IT function. IG must balance the needs of many stakeholders, including clinicians, administrators, patients, payers, regulators, and technology vendors: accessibility against security, usability against compliance, and innovation against standardization.

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Documentation, devices,

Repositories, backups,

Care, payment,

Retention schedules,

Secure destruction or

interfaces

integrity controls

operations, reporting

legal holds

transfer

Core Principles of Healthcare Information Governance

Principle	WhatItRequires
Accountability	Individualsanddepartmentsunderstandtheirrolesandresponsibilitiesformanaginginformation
Transparency	IGpoliciesandproceduresaredocumentedandcommunicated
Integrity	Informationremainsaccurateandunalteredexceptthroughauthorizedprocesses
Protection	Safeguardspreventunauthorizedaccess use ordisclosure , ,
Compliance	Practicesadheretoapplicablelaws regulations andstandards , ,
Availability	Authorizeduserscanaccessneededinformationwhenrequired
Retention	Informationiskeptfortheperiodthatlaw regulation andbusinessneedrequire , ,
Disposition	Informationissecurelydestroyedortransferredattheendofitslifecycle

Components of an IG Program

The hierarchy and reporting

The rules and processes that govern

The systems and tools that support

relationships for information

how information is handled, often

information management.

management, usually an executive

launched by an IG charter that states

sponsor and an IG steering committee

scope, principles, roles, and objectives.

or council.

Organizations implement IG programs for regulatory compliance (the primary driver), operational efficiency, risk management, quality improvement (which depends on accurate data), legal defensibility (demonstrable evidence that information was properly managed, which matters in litigation and e-discovery), and cost reduction from eliminating redundant systems, reducing storage, and avoiding penalties. HIM professionals have traditionally managed health records and documentation quality; IG extends that expertise to all organizational information, and RHIAs are well positioned to lead enterprise IG programs.

Data quality is the degree to which data meets the requirements of its intended use. It directly affects patient safety, care coordination, clinical decisions, research validity, reimbursement accuracy, and regulatory compliance; poor data quality leads to medical errors, delayed diagnoses, inappropriate treatment, billing denials, and legal liability. Data integrity is the accuracy and consistency of data throughout its lifecycle: whether data has stayed in its original state except through authorized modification. Data quality asks whether data is fit for use; data integrity asks whether it has been corrupted, manipulated, or inappropriately changed.

Dimension	Defi nition	ExampleofaFailure
Accuracy	Data correctly represents the real -world entity or event	Wrongdateofbirthenteredatregistration
Completeness	Allrequireddataelementsarepresent	Dischargedispositionleftblank
Consistency	Dataisuniformacrosssystemsandtimeperiods	DifferentaddressesforonepatientintheEHR
		andbillingsystem
Timeliness	Dataisavailablewhenneededforitspurpose	Operativereportdictateddaysaftersurgery
Validity	Dataconformstodefinedformats ranges andbusiness , ,	Adateofbirthinthefuture
	rules
Accessibility Relevance	Authorizeduserscanretrievedatawithoutundue difficulty Dataisappropriateandusefulforitsintendedpurpose	Resultstrappedinalegacysystem Collectingfieldsnobodyuses	1

Common Data Quality Problems

Data entry errors, duplicate records (one patient holding more than one medical record number, which fragments the record), missing data, inconsistent recording of the same information, outdated data, and integration problems when information moves between systems with different structures or standards.

Data Quality Management Techniques

Data governance supports but differs from data quality management. Data quality addresses the technical characteristics of data; data governance addresses the people, policies, and processes that decide who may create, modify, and delete data, what standards and definitions apply, when data must be captured and retained, where it is stored and how it is protected, why it is collected, and how its quality is measured and improved.

Measuring and Protecting Data

DataQualityMetric	HowItIsCalculatedorUsed
Errorrate	Recordscontainingerrors÷recordsreviewed×100
Completenessrate	Requiredfieldspopulated÷requiredfieldsexpected×100
Timeliness	Elapsedtimebetweenaneventanditsdocumentationorentry
Consistencychecks	Discrepanciesbetweenrelateddataelementsoracrosssystems
Validitychecks	Valuesfallwithinexpectedrangesanddefinedformats

EHRs build quality into capture with required fields, pick lists, and validation (edit) rules. Natural language processing extracts structured data from notes, machine learning flags likely errors, integration engines validate data in transit, and analytics platforms monitor quality trends. Integrity controls include access controls, audit trails that record every access and change, version control, checksums and hash values that detect unauthorized alteration, and backup and recovery procedures.

Match the failure to the dimension. A future birth date is a validity failure (a rule was violated); a wrong but plausible birth date is an accuracy failure; a blank required field is a completeness failure; a late entry is a timeliness failure.