HIM3620 answers the question: what does a health information manager actually do, across the entire life cycle of a patient's health record, from the moment data is captured to the moment it's archived or securely destroyed?
The health record life cycle
HIM3620 covers the full life cycle of a health record: creation (data capture at the point of care), maintenance (updates, amendments, and corrections), use (clinical, billing, quality, and research uses), disclosure (release of information under appropriate authorization), retention (how long different record types must legally be kept), and destruction (secure disposal once retention requirements are satisfied). Students learn that HIM responsibility spans this entire life cycle, not just the initial documentation step.
Data quality standards and the scope of the HIM profession
The course covers the AHIMA data quality management model — ensuring data is accurate, accessible, comprehensive, consistent, and timely — and surveys the breadth of the HIM profession: clinical coding, cancer registry, release of information, privacy officer roles, and increasingly, health data analytics. Students examine how the profession has evolved from a primarily paper-record-focused role into one deeply involved in digital health data governance and analytics.
Key topics in HIM3620
- The health record life cycle: creation, maintenance, use, disclosure, retention, destruction
- AHIMA data quality management model: accuracy, accessibility, comprehensiveness, consistency, timeliness
- State and federal record retention requirements and secure destruction standards
- The scope of HIM careers: coding, registry, ROI, privacy, and health data analytics
- AHIMA professional standards and the Certified Health Data Analyst / RHIA credentials
- The evolution of HIM from paper-record management to digital health data governance
Working on a health record life cycle analysis or a data-quality assessment?
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Worked example: applying the AHIMA data quality model to a real problem
- Problem: A hospital's readmission report shows inconsistent numbers depending on which department pulls the data
- Accuracy check: Are the underlying diagnosis codes correctly assigned?
- Consistency check: Are different departments using the same definition of "readmission" (e.g., 30-day vs. 90-day window)?
- Timeliness check: Is one department pulling data before final coding is complete, producing incomplete counts?
- Resolution: A standardized definition and a single validated data source resolve the discrepancy
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Frequently asked questions
The American Health Information Management Association's data quality model identifies several characteristics that healthcare data must have to be trustworthy and useful: accuracy (data correctly reflects what actually happened), accessibility (authorized users can obtain the data when needed), comprehensiveness (all required data elements are present, without significant gaps), consistency (the data is reliable and the same across different points of collection or reporting), currency/timeliness (data is up to date and available within an appropriate timeframe for its intended use), definition (data elements are clearly defined so everyone interprets them the same way), granularity (the data is captured at the appropriate level of detail for its intended use), and precision (the data has the appropriate degree of exactness). HIM3620 teaches this model as a diagnostic framework — when a data quality problem surfaces (like inconsistent reporting numbers), the model gives HIM professionals a structured way to identify exactly which quality dimension is failing, rather than treating "bad data" as one undifferentiated problem.
Health information management historically centered on managing physical paper charts — filing, retrieving, and controlling access to paper records, along with coding and release of information functions built around that paper-based workflow. As healthcare has shifted almost entirely to electronic health records, the HIM role has expanded significantly: HIM professionals are now deeply involved in health IT system implementation and governance, data quality management across complex digital systems, health data analytics supporting organizational decision-making, and increasingly sophisticated privacy and security responsibilities for electronic protected health information. HIM3620 teaches that this evolution means the modern HIM professional needs a broader skill set than the historical "medical records clerk" stereotype — combining traditional coding and compliance knowledge with genuine health IT and data analytics literacy, which is why HIM curricula increasingly include courses like HIM2670 (health IT systems) and HIM4630 (statistical analysis) alongside the traditional health record management foundation.