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: Quality Measures
Source: HIMSS Dictionary of Health Information and Technology Terms, Acronyms, and Organizations, 6th Edition (2025)
🔍 Plain-Language Definition
Quality measures are tools that put a number on the quality of care by comparing what happened with a defined criterion. The HIMSS Dictionary describes them as mechanisms for expressing quality as a quantity. A measure might ask what share of eligible patients received a recommended treatment, how often a complication occurred, or how patients rated their own function after care.
Every measure needs a precise definition: who is counted (the denominator), who meets the standard (the numerator), who is excluded, and what data are used. When those definitions are written in a form that software can run against EHR data, the result is an electronic clinical quality measure (eCQM).
| Measure Type | What It Examines | Example |
|---|---|---|
| Structure | Resources and capacity | Presence of a rapid response team |
| Process | Whether recommended steps were done | Confirmatory imaging for a diagnosis |
| Outcome | Results for patients | Complication or readmission rate |
| Patient-reported outcome | Patient’s own report of health or function | Pain or mobility survey score |
| Patient experience | Perception of care | Communication rating |
| Efficiency / cost | Resources used for a result | Spending per episode |
⚠️ Exam Alert: A process measure checks what was done; an outcome measure checks what resulted. An eCQM is a measure specified for electronic data. Risk adjustment accounts for patient differences so comparisons are fair, and attribution decides which clinician or organization is responsible for a patient’s result.
🌐 Why It Matters: Real-World Healthcare IT Context
Quality measures sit at the center of value-based care. Payers use them to adjust payment, regulators publish them for public reporting, accreditors use them in surveys, and health systems use them to find where care needs to improve. CMS maintains a public inventory of the measures it uses across its programs, and many of them sit within its value-based programs. For informatics teams, measures are also a build specification: the EHR must capture the right data in the right fields so that the measure can be calculated.
Newer measures try to use electronic data to look at areas that older, chart-review-based measures missed. A 2026 study in the Joint Commission Journal on Quality and Patient Safety describes the development of an electronic diagnostic quality measure for pneumonia. Traditional pneumonia measures required manual chart review and focused on treatment and outcomes, producing inconsistent target populations and leaving diagnosis unexamined. The research team used EHR data from 2015 to 2022 in two U.S. health systems, one federal and one academic, to build a measure of the percentage of patients hospitalized with pneumonia who also had confirmatory chest imaging. Clinician review of 102 charts served as the reference standard, and the measure showed sensitivity of 98.3 percent and 97.8 percent in the two systems and a positive predictive value of 100 percent in both. Among 92,797 pneumonia hospitalizations at 101 hospitals, overall diagnostic quality was 90.1 percent, and it varied widely across facilities, from 100 percent in the best-performing hospital to 78.3 percent at the low end. The study shows both the promise and the work involved: electronic measures can be accurate and scalable, but they depend on validated definitions and routinely captured data.
Patient-reported outcomes raise a different set of issues. A 2024 article in Value in Health reviewed patient-reported outcome-based performance measures, which aggregate survey data into entity-level assessments of clinicians, hospitals, or accountable care organizations. The authors reviewed the CMS Measures Inventory Tool in May 2023 and found 54 instances of active use of such measures across CMS programs, covering 46 unique measures in 14 programs. They identified five barriers: instrument development, sufficiency of response rates, provider burden, concern about fairness, and attribution of outcomes. They proposed five principles for success: clinical salience, adequate sample size, a meaningful range of performance with the ability to detect change in a reasonable time, an equity focus, and appropriate risk adjustment. The same concerns apply to nearly every measure: whether the numbers are reliable, whether they are fair, and whether the burden of collecting them is justified.
Measures can also mislead. Small numbers produce unstable rates. Poor risk adjustment penalizes organizations that care for sicker or more socially disadvantaged patients. Incentives may encourage documentation of the measure and not the care behind it. And a measure that is easy to calculate may crowd out one that matters more. Sound measurement programs combine several measure types, test reliability, examine equity across patient groups, and review whether the measures actually predict better care.
Common quality measure problems:
- Data captured in free text, so measures cannot find it
- Definitions that differ between reports for the same concept
- Attribution rules that assign patients to clinicians who did not control their care
- Inadequate risk adjustment that penalizes safety-net organizations
- Measure burden that consumes staff time without improving care
- Reported rates that reflect documentation habits more than practice
What supports meaningful measurement:
- Clearly specified numerators, denominators, and exclusions
- Documentation workflows that capture data once, in discrete fields
- Validation of electronic measures against chart review
- Stratified results that reveal differences across patient groups
- Feedback to frontline teams in time to act
- Periodic review to retire measures that no longer help
🏥 The Nurse’s Perspective: Clinical, Bedside, & Workflow
Nurses generate much of the data behind quality measures and are directly affected by the results.
At the Bedside
Many nursing-sensitive measures, such as falls, pressure injuries, catheter-associated infections, pain reassessment, and medication timing, depend on timely and accurate documentation. A fall risk assessment completed at the right time, in the right field, protects the patient and also makes the measure correct. Where a measure looks wrong, the nurse’s report of how the work actually happens helps analysts find the gap.
In Clinical Workflow
Nurse managers review unit-level dashboards, interpret variation, and turn results into action. They ask whether a drop reflects care, documentation, or a change in the measure itself. Quality nurses and infection preventionists validate cases and report to regulators and payers.
Nurse informaticists work with quality departments to translate measure specifications into flowsheet rows, orders, and reports. They test that the right patients fall into each denominator, and they help reduce the burden of measure-driven documentation by embedding it in the natural flow of care.
In EHR Implementation (Go-Live Perspective)
An Epic Go-Live changes where and how data are captured, so quality reporting is a standard readiness item. Analysts compare pre- and post-go-live measures to find build gaps. At-the-elbow support analysts notice when nurses chart a required assessment in a free-text note because the discrete field is hard to find, and they relay it so that the build and the training can correct it before reports are affected.
🎓 CAHIMS / CPHIMS & HIMSS Perspective
CAHIMS Exam Domain Mapping
Quality measures appear in reporting, value-based care, and data quality questions:
| CAHIMS Domain | How Quality Measures Appear |
|---|---|
| Healthcare & Technology Environments | Quality programs, public reporting, payment models |
| Clinical Informatics | Measure specification, documentation design, clinical decision support |
| Healthcare Information & Systems Management | Data extraction, validation, eCQM reporting, dashboards |
| Management & Leadership | Performance improvement, governance, incentives, burden |
Key Terms to Know Alongside Quality Measures
- eCQM — clinical quality measure specified for electronic data
- Numerator and Denominator — the patients meeting the standard and the patients eligible to be counted
- Risk Adjustment — statistical method that accounts for patient differences
- Attribution — assignment of patients to a clinician or organization for measurement
- Process vs. Outcome Measure — what was done compared with what resulted
- Patient-Reported Outcome Measure — patient’s own report of health or function
HIMSS Organizational Position
HIMSS supports data-driven quality improvement and value-based care through its research, education, and community work. Members can find related resources through the HIMSS Resource Center.
🔗 Explore Further: External References
- Visnovsky et al. (2026), Joint Commission Journal on Quality and Patient Safety — The Next Generation of Quality Measurement: Development and Evaluation of an Electronic Diagnostic Quality Measure in Pneumonia https://pubmed.ncbi.nlm.nih.gov/42586928/
- Gettel et al. (2024), Value in Health — Patient-Reported Outcome-Based Performance Measures in Alternative Payment Models: Current Use, Implementation Barriers, and Principles to Succeed https://pubmed.ncbi.nlm.nih.gov/38042334/
- CMS — Measures Inventory Tool (CMIT) https://cmit.cms.gov/cmit/
- CMS — Value-Based Programs https://www.cms.gov/medicare/quality/value-based-programs
- eCQI Resource Center https://ecqi.healthit.gov/
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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