By Eric Andrew Kristof, RN, CAHIMS | Healthcare IT Professional | HIMSS Arkansas Chapter Member
About This Series: The HIMSS Word of the Day is a running blog series drawing from the HIMSS Dictionary of Health Information and Technology Terms, Acronyms, and Organizations, 6th Edition (2025) — the authoritative reference for healthcare IT professionals and the primary study resource for CAHIMS and CPHIMS certification. Each post paraphrases the term in plain language, grounds it in real-world healthcare IT context, and adds a bedside nursing perspective you won’t find in a textbook.
📖 The Term: Data Quality
Source: HIMSS Dictionary of Health Information and Technology Terms, Acronyms, and Organizations, 6th Edition (2025)
🔍 Plain-Language Definition
Data quality describes how well data does the job it is being used for. High-quality data is correct, complete enough, current, and clearly understood by the people and systems that rely on it.
The HIMSS Dictionary offers three overlapping senses. The first treats data quality as a judgment about whether data can serve its purpose in a particular context, based on qualities such as accuracy, completeness, reliability, relevance, and currency. The second focuses on the built-in characteristics that make data accurate and complete and let it carry its intended meaning. The third is user-centered: data quality is the extent to which a data set meets the needs of the person using it. The Dictionary stresses that good data quality is essential if data-driven decisions are to be sound.
The phrase “in a given context” is the key idea. A blood pressure recorded to the nearest 10 mmHg may be fine for a population trend report and inadequate for titrating an IV drip.
Different frameworks name the dimensions slightly differently, but most cover the same ground:
| Dimension | Question It Asks | Healthcare Example of a Problem |
|---|---|---|
| Accuracy (Correctness) | Does the value reflect reality? | A weight entered in pounds in a field expecting kilograms |
| Completeness | Is required data present? | Missing allergy status or smoking history |
| Consistency (Concordance) | Does the data agree across sources and fields? | A problem list that says “no diabetes” next to an insulin order |
| Plausibility | Is the value believable? | A recorded heart rate of 400 or a birth date in the future |
| Timeliness (Currency) | Is the data current enough for its use? | A medication list last reviewed two years ago |
| Conformance (Validity) | Does the data follow the expected format and code set? | A lab result stored as free text instead of a coded value |
⚠️ Exam Alert: Data quality asks whether data is fit for its purpose. Data integrity focuses on whether data has been protected from improper change and remains trustworthy over its life cycle. Data governance is the decision-making framework that assigns ownership, policies, and standards, including data quality rules. Exam questions often pair these terms, so watch for which one the scenario describes.
🌐 Why It Matters: Real-World Healthcare IT Context
Healthcare collects data for one purpose and then reuses it for many others. A vital sign recorded for bedside care later feeds early warning scores, quality measures, research data sets, and machine learning models. Each reuse inherits whatever errors and gaps were there at the start.
Researchers have worked to make data quality measurable. A 2013 review in the Journal of the American Medical Informatics Association identified five common dimensions in EHR data quality research: completeness, correctness, concordance, plausibility, and currency. A 2016 harmonized framework led by Michael Kahn organized checks into three categories, conformance, completeness, and plausibility, assessed through verification and validation. The OHDSI community’s open-source Data Quality Dashboard organizes its automated checks according to that framework.
Data quality also shapes interoperability. Standard vocabularies such as SNOMED CT for clinical findings and LOINC for laboratory tests make data consistent across systems, and national data sets such as USCDI depend on the data being coded correctly at the source.
Artificial intelligence has raised the stakes. A model trained on incomplete or biased data will reproduce those gaps at scale, and model performance can drift when documentation habits or source systems change.
Common causes of poor data quality:
- Copy-forward documentation that carries outdated findings into new notes
- Default values and required fields completed just to move on
- Duplicate patient records and registration errors
- Free-text entries where coded data is needed
- Interfaces that map codes or units incorrectly
- Workarounds, such as documenting on paper and backfilling later
Practices that improve data quality:
- Clear data definitions and ownership through data governance
- Smart form design that captures structured data in the natural workflow
- Validation rules and plausibility checks at the point of entry
- Routine data quality monitoring with dashboards and feedback to users
- Patient identity management and duplicate record cleanup
- Training that explains how documentation is reused downstream
🏥 The Nurse’s Perspective: Clinical, Bedside, & Workflow
Nurses enter a large share of the structured data in the EHR, which makes them central to data quality whether or not they think of it that way.
At the Bedside
Vital signs, intake and output, pain scores, fall risk, skin assessments, height, weight, and allergies are often documented by nurses. Each one may drive an alert, a dose calculation, or a quality measure. A weight entered in the wrong unit can change medication dosing. An allergy documented as “unknown” when the patient could have been asked can lead to unnecessary alerts or missed ones.
Nurses also correct the record. When a home medication list is outdated, or a problem list contains a condition the patient does not have, the bedside nurse is often the first to spot it and route it for correction.
In Clinical Workflow
Flowsheet design has a strong effect on data quality. When a required field has no sensible “not applicable” option, nurses may pick whatever answer allows them to move on, and the data becomes misleading. When rows are well designed, with clear choices and logical defaults, accurate documentation becomes the easiest path.
Device integration helps but does not remove the nurse’s role. Vital signs from monitors still need validation before they file, because artifacts from movement or a loose lead can create implausible values that later appear in reports.
In EHR Implementation (Go-Live Perspective)
During an Epic Go-Live, data quality issues often appear first as “the report looks wrong.” Common causes include data converted from the old system without consistent mapping, flowsheet rows used differently from one unit to another, and new users documenting in free text because they have not found the structured field yet. At-the-elbow support analysts can help by showing nurses the structured location for frequently missed items, such as admission height and weight or allergy reactions.
Early data quality monitoring pays off. Reviewing completion rates for key fields during the first weeks after Go-Live helps teams catch training gaps and build problems while habits are still forming.
🎓 CAHIMS / CPHIMS & HIMSS Perspective
CAHIMS Exam Domain Mapping
Data quality appears across analytics, safety, and governance questions:
| CAHIMS Domain | How Data Quality Appears |
|---|---|
| Healthcare & Technology Environments | Quality reporting programs; AI and analytics trends; national data standards |
| Clinical Informatics | Documentation design; structured versus free-text data; clinical decision support accuracy |
| Healthcare Information & Systems Management | Validation rules, interface mapping, master data management, data conversion, monitoring dashboards |
| Management & Leadership | Data governance, data stewardship, accountability for data quality metrics |
Key Terms to Know Alongside Data Quality
- Data Governance — the framework of decision rights, policies, and standards that guide how data is managed
- Data Integrity — the assurance that data remains complete, accurate, and unaltered except by authorized processes
- Data Steward — a person accountable for the definitions, quality, and appropriate use of a set of data
- Master Data Management — practices for keeping core shared data, such as patient and provider records, consistent across systems
- Data Validation — checks that confirm data meets format, range, and business rules before or after it is stored
- Structured Data — data captured in defined fields and code sets rather than free text
HIMSS Organizational Position
HIMSS connects data quality to analytics maturity. Its Analytics Maturity Assessment Model (AMAM) evaluates how organizations build the data foundation, governance, and skills needed to use analytics well, and data quality is part of that foundation. Members can find related education and research through the HIMSS Resource Center.
🔗 Explore Further: External References
- Weiskopf and Weng (2013), Journal of the American Medical Informatics Association — Methods and Dimensions of EHR Data Quality Assessment https://pubmed.ncbi.nlm.nih.gov/22733976/
- Kahn et al. (2016), eGEMs — A Harmonized Data Quality Assessment Terminology and Framework https://pubmed.ncbi.nlm.nih.gov/27713905/
- OHDSI — Data Quality Dashboard https://ohdsi.github.io/DataQualityDashboard/
- ONC — United States Core Data for Interoperability (USCDI) https://isp.healthit.gov/united-states-core-data-interoperability-uscdi
- AHIMA — American Health Information Management Association https://www.ahima.org/
- HIMSS — Analytics Maturity Assessment Model (AMAM) https://www.himss.org/maturity-models/amam
Term source: HIMSS Dictionary of Health Information and Technology Terms, Acronyms, and Organizations, 6th Edition (2025). All definitions in this series are paraphrased for editorial purposes. Readers are encouraged to consult the primary source for exact language.
Eric Andrew Kristof, RN, CAHIMS is a Healthcare IT professional and HIMSS Arkansas Chapter member based in Hot Springs Village, AR, with hands-on Epic Go-Live experience and a diverse healthcare and IT background. He writes at kristof.org at the intersection of clinical care and healthcare technology.
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