DQ 2 :Describe the process of a retrospective chart review
Five questions, and the last is the one that separates a chart review from a spreadsheet: how you know the data you pulled is actually what the chart says.
Editorial process
Last reviewed · August 18, 2026
A chart review is a study, with all a study's obligations
Describe the process as a protocol rather than as an activity. A retrospective chart review starts with a written question and an operational definition for every variable: not 'infection' but the exact criteria, coded fields and date windows that count. Then inclusion and exclusion criteria, a defined time period, and a sampling approach — all eligible charts or a random sample, and how many you need. Then a data collection instrument built before you open a single record, because a form designed while abstracting is a form that changes as you go. Then abstraction, ideally by trained abstractors blind to the study hypothesis where blinding is possible. Then cleaning, analysis and reporting. Access runs in parallel: institutional review board determination, organisational data governance approval, a data use agreement, and the minimum necessary standard under privacy rules — and the sentence worth including is that these take longer than students expect and should start first.
The validity and accuracy questions are where the marks are. Chart data is recorded for care and billing rather than for research, so it has known weaknesses: variables are missing non-randomly, since sicker patients get documented more; free text carries the clinical detail and resists extraction; coding reflects reimbursement incentives; and documentation practice changes over time, which fakes a trend. Reliability is addressed by an explicit codebook, abstractor training, and inter-rater agreement measured on a subset with a kappa reported. Accuracy of the pull is a separate problem and is addressed separately: validate the query against a manually reviewed sample of records, check counts against an independent source such as a census or billing total, run range and logic checks for impossible values, and re-run the extraction to confirm it returns the same rows. Saying that a query returning plausible numbers is not evidence it returned the right ones is the point of the question.
Likely learning objectives
- Set out a chart review as a written protocol with pre-defined variables.
- Identify the access approvals required and their lead time.
- Name the specific validity threats of data recorded for care and billing.
- Distinguish abstraction reliability from query accuracy.
Assignment instructions
Read the full question
Review every instruction before using the planning guidance that follows.
DNP 830 Topic 6 DQ 2 DQ 2 :Describe the process of a retrospective chart review Describe the process of a retrospective chart review. How are these data collected? How would you access the data? What is the validity and reliability of these data? What steps would you need to take to ensure these data were accurately pulled from the database?
Turn the brief into deliverables
- 01A protocol with operational definitions and eligibility criteria.
- 02A data collection instrument built before abstraction.
- 03The approvals required for access.
- 04Validity and reliability threats specific to chart data.
- 05Query validation steps, distinct from abstraction checks.
The process, access, then validity and accuracy checks
The protocol
Set out question, operational definitions, eligibility and period.
What the assessor is likely looking for
An operational definition specific enough to apply.
Instrument and abstraction
Describe building the form first and training abstractors.
What the assessor is likely looking for
The form existing before the first record is opened.
Access and approvals
Cover review board, data governance, agreements and minimum necessary.
What the assessor is likely looking for
A lead time acknowledged.
Why chart data is imperfect
Name non-random missingness, free text, coding incentives and drift.
What the assessor is likely looking for
A threat that would bias a specific finding.
Validating the pull
Give manual sample checks, independent counts, range checks and re-runs.
What the assessor is likely looking for
A check that would catch a wrong-but-plausible query.
Where chart review methodology is set out
Recommended databases
- NCBI Bookshelf
- PubMed Central
- Office of Research Integrity
- Agency for Healthcare Research and Quality
Search sequence
- 1.Read on human subjects research design for the approvals and minimum necessary standard.
- 2.Find a study using routine records and read its limitations closely.
- 3.Read on documentation quality for the drift and completeness problems.
- 4.Write the codebook before drafting anything else.
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.
Human Subjects Research Design
StatPearls, NCBI Bookshelf · 2023
Human subjects research design, including approvals and minimum necessary data.
The Impact of Structured and Standardized Documentation on Documentation Quality; a Multicenter, Retrospective Study
Journal of Medical Systems · 2022
Structured and standardised documentation and its effect on documentation quality.
Challenges using electronic nursing routine data for outcome analyses: A mixed methods study
PubMed Central · 2022
Challenges using routine electronic nursing data for outcome analysis.
Data Collection
Office of Research Integrity, U.S. Department of Health and Human Services · 2024
Research integrity guidance on data collection and management.
Study Bias
StatPearls, NCBI Bookshelf · 2023
Study bias, including the selection and information biases chart review carries.
Review before submission
Common mistakes
- Describing the review as reading charts rather than as a protocol.
- Building the collection form while abstracting.
- Conflating abstractor reliability with whether the query pulled the right records.
- Treating a plausible-looking output as a validated one.
Submission checklist
- Are variables operationally defined before extraction?
- Have you named the approvals and their sequence?
- Is non-random missingness addressed?
- Is inter-rater agreement measured and reported?
- Are there query validation steps separate from abstraction checks?
Use this guide to plan and review your own work. Follow your institution's rules and read Brinevia's academic-integrity policy.
Written by
Maren Caldwell
MSN, RN, CNE
Medical-surgical nursing, pharmacology and NCLEX preparation
Maren is a registered nurse with over 15 years of clinical and educational experience in medical-surgical nursing. She writes on NCLEX preparation, patient care fundamentals, pharmacology and evidence-based practice.
Reviewed by
Dr. Tessa Redmond
DNP, RN, CNE
Evidence-based practice and clinical education
Tessa is a doctorally-prepared nurse educator. She reviews Brinevia content for clinical accuracy and alignment with current evidence-based guidelines.