Nursing data is any information nurses generate or use about patients, care processes, and outcomes. It includes structured EHR data (vital signs, flowsheets, medication administration), unstructured data (nursing notes, which need natural language processing to analyze), standardized terminologies (NANDA-I, NIC, NOC, SNOMED CT, LOINC), nursing-sensitive quality indicators such as falls, pressure injuries, and CAUTI (tracked by NDNQI), patient-reported outcomes, and device or wearable data. Data science turns these into insight, while governance (HIPAA, consent, bias review) keeps its use safe.
A taxonomy of nursing data
| Type | Examples | Analytic use |
|---|---|---|
| Structured EHR | Vital signs, I&O, MAR, flowsheet assessments, Braden/Morse scores | Early warning scores, dashboards, regression models |
| Unstructured text | Nursing narrative notes, handoff text | NLP to detect delirium, falls risk, symptom trajectories |
| Standardized terminologies | NANDA-I, NIC, NOC, Omaha System, SNOMED CT, LOINC | Interoperability; measuring nursing's contribution |
| Workforce data | Staffing, skill mix, overtime, turnover | Staffing–outcome studies |
| Patient-reported outcomes | PROMIS measures, HCAHPS, symptom diaries | Person-centered outcome evaluation |
| Device and wearable | Continuous monitoring, smart pumps, remote monitoring | Real-time deterioration detection |
Nursing-sensitive quality indicators (NDNQI)
Nursing-sensitive indicators reflect the structure, process, and outcomes of nursing care (Donabedian). Common measures tracked through the National Database of Nursing Quality Indicators include:
- Patient falls and falls with injury
- Hospital-acquired pressure injuries
- CAUTI and CLABSI rates
- Physical/sexual assault and restraint prevalence
- Nursing hours per patient day and skill mix
- RN education, certification, and nurse turnover
- RN survey measures of the practice environment
From data to insight: nursing data science
- Define the question (e.g., which patients are at risk of a fall in the next 24 hours?).
- Extract and clean data — handle missing values, standardize units, de-identify.
- Analyze with descriptive statistics, regression, or machine learning; use NLP for notes.
- Validate on separate data and check performance across patient subgroups.
- Translate into a clinical decision support tool with nurses in the loop.
Data governance and ethics
- Privacy: HIPAA minimum-necessary principle; de-identification per Safe Harbor or Expert Determination.
- Consent and IRB: secondary use of EHR data still needs IRB review or a determination of exemption.
- Algorithmic bias: evaluate model performance by race, sex, age, and language; biased training data produces biased predictions.
- Nurse-in-the-loop: predictive tools support, not replace, clinical judgment; alerts must be actionable to avoid fatigue.
- Data quality: documentation burden and copy-forward reduce accuracy — design workflows that capture data once.
Frequently Asked Questions
Sources & Further Reading
- Nursing Informatics: Scope and Standards of Practice. American Nurses Association (3rd ed.), 2022.
- NDNQI Nursing-Sensitive Indicators. Press Ganey / American Nurses Association.
- Big data science: A literature review of nursing research exemplars. Westra, B. L., et al. Nursing Outlook, 65(5), 549–561, 2017.