DNP 805: EHR database design and data management
The structured-versus-unstructured question is the technical heart of this paper, and it is only interesting if your chosen problem has both — a lab value your database can query, and a nursing note it cannot.
Editorial process
Last reviewed · August 15, 2026
Structured against unstructured is the technical core
Choose the clinical problem for its data rather than for its clinical importance, because the paper is a data-management exercise. The best choices generate both kinds of data: pressure injury prevention, sepsis recognition, readmission risk and falls all have structured elements — Braden scores, vital signs, laboratory values, coded diagnoses, timestamps — and unstructured ones, which are the nursing notes, the wound descriptions and the handover narratives where most of the clinical judgement actually lives. That contrast is what makes the structured-versus-unstructured requirement answerable rather than a definition. Structured data is stored in defined fields with constrained values, so it can be queried, aggregated and trended directly. Unstructured data is free text, images or scanned documents, and a database cannot act on it without natural language processing or somebody re-entering it — which is a cost, not a detail, and naming that cost is part of what the classification is for.
Then build the paper around what the data would let you do. Identify the specific elements you would need, say where each lives in the EHR and who enters it, and be honest about quality: a field that exists is not a field that is reliably completed, and a risk score entered once on admission cannot support a trend. That observation is worth more than a longer list of elements. For the design half, cover what you would store, how it relates, and what the query must answer — the design serves an output, so name the output first. Then the outcome connection: data alone changes nothing, while data reaching a clinician at a moment when a decision is open changes something, and naming that moment is what turns the paper into an argument for the database rather than a description of one. Keep the whole thing to 1,000-1,250 words, which is tight for five requirements.
Likely learning objectives
Inferred from the brief — check these against your own rubric.
- 01Select a clinical problem whose data supports a database management approach.
- 02Distinguish structured from unstructured data by what a system can act on.
- 03Assess data quality as distinct from data availability.
- 04Trace a chain from managed data to a changed clinical decision.
Read the full question
Review every instruction before using the planning guidance that follows.
Turn the brief into deliverables
- 01A 1,000-1,250 word paper on a selected clinical problem.
- 02The data elements needed, with their location and source.
- 03A structured-versus-unstructured classification with explanation.
- 04A design serving a named output.
- 05An explicit chain from data to outcome, with APA citations.
Problem, data, structure, then the outcome
Select the problem
Choose a clinical problem with mixed data and state why it benefits from database management.
Identify the data elements
Name what is needed, where it lives and who enters it.
Structured against unstructured
Classify each element and explain what a system can and cannot act on.
Data quality
Distinguish fields that exist from fields that are reliably completed.
From data to outcome
Name the decision point at which managed data changes what happens.
Sources on clinical data and databases
Recommended databases
- Office of the National Coordinator for Health IT
- PubMed Central
- Journal of the American Medical Informatics Association
- Your organisation's EHR documentation
Search sequence
- 1.Choose the problem, then list the actual fields your EHR holds for it before writing anything.
- 2.Look up how structured and unstructured clinical data are defined in the informatics literature.
- 3.Search for evidence connecting a data-driven intervention to an outcome in your chosen problem.
- 4.Check what natural language processing can currently extract, since that bounds what unstructured data is worth.
Reference shortlist
These are authoritative starting points, not a ready-made bibliography. A qualified reviewer must confirm that each source fits the assignment and supports the claim beside which it is cited.
Nothing here is cleared for citation until you have read it.
- 01
Interoperability
Office of the National Coordinator for Health Information Technology · 2024
How clinical data is structured for exchange, which is the standards basis for the classification.
- 02
From glitter to gold: recommendations for effective dashboards from design through sustainment
Implementation Science · 2025
Design guidance for turning stored data into something a clinician acts on.
- 03
What Is Database Normalization?
IBM · 2024
Database structure fundamentals, relevant to the design half of the paper.
- 04
The association between perceived electronic health record usability and professional burnout among US nurses
Journal of the American Medical Informatics Association, 28(8), 1632-1641 · 2021
Evidence on documentation burden, which is why data quality differs from data availability.
Review before submission
Common mistakes
- Choosing a problem whose data is entirely structured, which makes the classification trivial.
- Defining structured and unstructured without applying the distinction to your own elements.
- Listing data elements without saying whether they are reliably completed.
- Designing a database with no stated output it serves.
- Asserting better outcomes without naming the decision the data would change.
Submission checklist
- Does your problem generate both structured and unstructured data?
- Is each element located in the EHR and attributed to who enters it?
- Have you addressed completeness as well as existence?
- Does the design serve a named query or output?
- Is the outcome chain concrete rather than asserted?
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Written by
Aaron Bishop
MA, Education
assignment interpretation and research-methods coaching across disciplines
Aaron leads the EssayCrackers editorial desk. He works on how assignment briefs are read — what a rubric is actually asking for, and where students most often answer a different question than the one set.

Reviewed by
Dr. Nathan Cole
PhD, Rhetoric & Composition
Argumentation and thesis development
Nathan teaches first-year composition and directs a university writing center. He reviews EssayCrackers guides for argumentative soundness and citation accuracy.