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: Analytics
Source: HIMSS Dictionary of Health Information and Technology Terms, Acronyms, and Organizations, 6th Edition (2025)
🔍 Plain-Language Definition
Analytics is the practice of examining data to find patterns, explain what happened, predict what may happen next, and guide decisions. In healthcare, it ranges from a simple dashboard of falls by unit to complex models that estimate a patient’s risk of deterioration.
The HIMSS Dictionary describes analytics as a process that applies mathematics, statistics, predictive modeling, and machine learning to recorded data in order to uncover meaningful patterns and knowledge.
Analytics is commonly described in four types, each answering a different question:
| Type | Question It Answers | Healthcare Example |
|---|---|---|
| Descriptive | What happened? | Monthly catheter-associated infection rates by unit |
| Diagnostic | Why did it happen? | Drilling into infections by line type, insertion location, and maintenance bundle compliance |
| Predictive | What is likely to happen? | A score estimating which patients are at risk for readmission |
| Prescriptive | What should we do about it? | Recommending a care management outreach list ranked by expected benefit |
⚠️ Exam Alert: Reporting presents data as it is, while analytics looks for patterns and meaning. Business intelligence refers to the tools and processes that deliver analytics to decision-makers. Data mining is a technique for discovering patterns in large data sets. Machine learning is one method used in predictive analytics. Exam questions often ask which type of analytics fits a scenario, so match the question being asked to the type.
🌐 Why It Matters: Real-World Healthcare IT Context
Healthcare organizations generate enormous amounts of data through EHRs, claims, devices, and operations. Analytics turns that data into decisions: where to add staff, which patients need outreach, which units need help with infection prevention, and how well the organization performs on quality measures and value-based contracts.
Predictive analytics deserves particular care. A 2021 study in JAMA Internal Medicine by Andrew Wong and colleagues externally validated a widely implemented proprietary sepsis prediction model and found much weaker performance than expected, with an area under the curve of 0.63. The model missed about two-thirds of sepsis cases while generating alerts on 18% of hospitalized patients. The lesson is that models should be validated locally before clinicians are asked to act on them.
Bias is another risk. A 2019 study in Science by Ziad Obermeyer and colleagues examined a widely used commercial algorithm for identifying patients with complex health needs. Because it predicted health care costs rather than illness, and less money was spent on Black patients at the same level of need, the algorithm underestimated how sick Black patients were. The authors estimated that correcting the bias would raise the share of Black patients flagged for extra help from 17.7% to 46.5%.
Timeliness matters as much as accuracy. A descriptive report refreshed monthly may be fine for strategic planning but useless for managing today’s bed capacity, and a real-time operational dashboard needs very different data pipelines than a research data set. Matching the analytics product to the decision it supports is one of the most important design choices an analytics team makes.
HIMSS measures how well organizations use analytics through its Analytics Maturity Assessment Model (AMAM), which looks at data governance, infrastructure, skills, and the use of analytics to improve outcomes. NIST’s AI Risk Management Framework offers guidance for managing the risks of models that rely on machine learning.
Common analytics pitfalls:
- Dashboards built on data definitions that differ between teams
- Predictive models deployed without local validation
- Proxy targets, such as cost, that encode existing inequities
- Alerts based on models that generate more noise than signal
- Analytics teams disconnected from the clinicians who use the results
- Poor data quality at the source, especially in structured documentation
Characteristics of mature analytics programs:
- Strong data governance with shared definitions
- Validated, monitored models with clear performance targets
- Equity reviews of model inputs, targets, and outcomes
- Self-service tools paired with trained analysts
- Clinician involvement in designing and interpreting analytics
- Feedback loops that measure whether analytics changed outcomes
🏥 The Nurse’s Perspective: Clinical, Bedside, & Workflow
Nurses are both producers and users of healthcare analytics.
At the Bedside
Much of the data behind clinical analytics comes from nursing documentation: vital signs, assessments, intake and output, pain scores, fall risk, skin assessments, and mobility. Early warning scores, deterioration models, and fall prediction tools depend on that data being entered accurately and on time. A set of vitals documented hours late can delay a score that should have triggered a rapid response.
Nurses also receive analytics at the bedside, often as scores, alerts, or icons on a patient list. Understanding what a score is designed to predict, and what it may miss, helps nurses use it as one input to clinical judgment rather than a replacement for it.
In Clinical Workflow
Nurse leaders use descriptive and diagnostic analytics every day: staffing and acuity reports, quality dashboards for falls and pressure injuries, patient experience results, and length-of-stay trends. Diagnostic drill-downs help explain why a unit’s falls increased, perhaps because of a staffing change, a new patient population, or a gap in hourly rounding.
Nurses can also question analytics constructively. When a dashboard shows a sudden change, asking whether the measure definition, the documentation workflow, or the patient population changed is often the fastest way to separate a real trend from a data artifact.
Care managers and population health nurses rely on predictive lists to decide whom to contact first. Their feedback on whether flagged patients actually needed help is valuable information for improving those models.
In EHR Implementation (Go-Live Perspective)
During an Epic Go-Live, analytics often takes a temporary step back while reports and dashboards are rebuilt for the new system. Operational dashboards for bed management, discharges, and staffing are typically prioritized for the first days, and leaders compare early results with legacy reports to confirm that definitions match.
At-the-elbow support analysts may hear “my report is wrong” or “my numbers look different.” Some differences are real errors, while others reflect new documentation workflows. Routing those questions to reporting teams with specific examples helps fix definitions before decisions are made on misleading data.
🎓 CAHIMS / CPHIMS & HIMSS Perspective
CAHIMS Exam Domain Mapping
Analytics appears across data, quality, and leadership questions:
| CAHIMS Domain | How Analytics Appears |
|---|---|
| Healthcare & Technology Environments | Value-based care; population health; AI and predictive modeling trends |
| Clinical Informatics | Early warning scores; quality dashboards; clinical use of predictive models |
| Healthcare Information & Systems Management | Data warehouses, reporting tools, model validation, data pipelines |
| Management & Leadership | Analytics strategy, data governance, analytics maturity, decision-making culture |
Key Terms to Know Alongside Analytics
- Descriptive Analytics — summarizing historical data to show what happened
- Predictive Analytics — using data and models to estimate the likelihood of future events
- Prescriptive Analytics — recommending actions based on predicted outcomes
- Business Intelligence (BI) — tools and practices that deliver data and analytics to decision-makers
- Algorithmic Bias — systematic errors in a model that produce unfair outcomes for certain groups
- Model Validation — testing a model’s performance on data different from the data used to build it
HIMSS Organizational Position
HIMSS promotes analytics maturity through its Analytics Maturity Assessment Model (AMAM), which helps organizations evaluate their data foundation, governance, and ability to use analytics to improve care and operations. Members can find related education and research through the HIMSS Resource Center.
🔗 Explore Further: External References
- HIMSS — Analytics Maturity Assessment Model (AMAM) https://www.himss.org/maturity-models/amam
- Wong et al. (2021), JAMA Internal Medicine — External Validation of a Widely Implemented Proprietary Sepsis Prediction Model https://pubmed.ncbi.nlm.nih.gov/34152373/
- Obermeyer et al. (2019), Science — Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations https://pubmed.ncbi.nlm.nih.gov/31649194/
- NIST — AI Risk Management Framework https://www.nist.gov/itl/ai-risk-management-framework
- OHDSI — Observational Health Data Sciences and Informatics https://www.ohdsi.org/
- HL7 — FHIR Bulk Data Access https://hl7.org/fhir/uv/bulkdata/
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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