WHY ENGLISH?
report structured electronic healthcare-record research so data sources, coding, linkage, governance, validation, analysis and reproducibility can be judged
Electronic healthcare records can support large studies without asking every research question through a new data collection exercise. English matters because “we used real-world data” hides crucial choices: which systems supplied the records, why codes were entered, how versions changed, how people were linked, how diseases and outcomes were defined, what remained missing and who was permitted to use the data.
CODE-EHR is a best-practice framework for structured electronic healthcare records in clinical research. Published by The BMJ on 29 August 2022, it offers minimum and preferred standards across coding, dataset construction and linkage, disease and outcome definitions, analysis and governance. It was developed through international stakeholder work involving patients, clinicians, scientists, regulators, editors and industry.
This guide shows how precise English turns a large dataset into an auditable research path. It helps students read digital-health claims, parents understand why routine records are not perfect memory, and future professionals document code lists, linkage and validation so others can reproduce or challenge the work. Privacy, public trust and multilingual clinical communication remain central.
Article contents
Open all chapters
- 1. State the Clinical Question and Why Routine Data Fit
- 2. Identify Every Data Source and Its Original Purpose
- 3. Report Coding Systems, Versions and Local Mappings
- 4. Publish or Describe Computable Phenotypes
- 5. Explain Dataset Construction and Data Flow
- 6. Report Linkage Methods and Linkage Quality
- 7. Define Population, Follow-up and Denominators
- 8. Define Outcomes, Exposures and Time Windows
- 9. Describe Data Quality, Missingness and Changes
- 10. Open the Analysis and Computational Environment
- 11. Address Confounding, Selection and Measurement Bias
- 12. Validate Codes, Phenotypes and Outcomes
- 13. Run Sensitivity and Reproducibility Checks
- 14. Report Governance, Privacy and Public Involvement
- 15. Interpret Clinical Meaning and Reuse Boundaries
ORIENTATION
What this guide covers
Current fact check
CODE-EHR was published in The BMJ volume 378 as e069048 on 29 August 2022, DOI 10.1136/bmj-2021-069048, PubMed ID 36562446 and PMCID PMC9403753. The framework identifies five areas: coding; dataset construction and linkage; disease and outcome definitions; analysis; and governance.
What this guide does not cover
This article focuses on reporting clinical research that uses structured or coded electronic healthcare data. It does not provide access to records, interpret an individual medical file, guarantee privacy, validate a particular algorithm, replace study-design guidance or claim that large datasets remove bias.
Worked example
Every chapter returns to a fictional multi-hospital study using coded records to examine follow-up after an emergency visit for asthma. It is fictional, low-stakes and educational; it does not report a real school, clinical decision or guaranteed outcome.
Did You Know? For CODE-EHR structured-record research reporting, complete reporting makes appraisal possible but does not perform the appraisal, remove bias or guarantee transfer.
Warm up with eduKate’s guides to explaining scientific evidence and asking better questions in class.
CHAPTER 1 OF 15
1. State the Clinical Question and Why Routine Data Fit
Why this move matters
Define population, exposure, comparison, outcome, time and why structured records are suitable.
Did You Know? A useful question connects clinical meaning to fields that records can support.
Trace coded records into a research claim
Define population, exposure, comparison, outcome, time and why structured records are suitable. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: The study asks whether timely follow-up after an asthma emergency visit is associated with fewer repeat visits within 30 days. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Data availability should not define the question by accident. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “State the Clinical Question and Why Routine Data Fit”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 2 OF 15
2. Identify Every Data Source and Its Original Purpose
Why this move matters
Name systems, organisations, care settings, coverage dates, update cycles and why data were collected.
Did You Know? Provenance explains what a record can and cannot remember.
Trace coded records into a research claim
Name systems, organisations, care settings, coverage dates, update cycles and why data were collected. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: Emergency, outpatient, pharmacy and mortality systems are listed separately with their operational purposes. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Billing and care fields may encode workflow as well as health. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Identify Every Data Source and Its Original Purpose”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 3 OF 15
3. Report Coding Systems, Versions and Local Mappings
Why this move matters
Name classifications, terminology versions, local codes, mapping methods and changes over time.
Did You Know? Versioned code lists are a core research material.
Trace coded records into a research claim
Name classifications, terminology versions, local codes, mapping methods and changes over time. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: Diagnosis and procedure codes use dated versions, while local clinic codes are mapped with documented review. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
The same label may change meaning across versions or sites. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Report Coding Systems, Versions and Local Mappings”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 4 OF 15
4. Publish or Describe Computable Phenotypes
Why this move matters
Show inclusion and exclusion logic, code lists, thresholds, windows, clinician input and executable details where safe.
Did You Know? Phenotype language connects clinical concepts to database operations.
Trace coded records into a research claim
Show inclusion and exclusion logic, code lists, thresholds, windows, clinician input and executable details where safe. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: Asthma visits require a diagnosis code plus an emergency setting, with sensitivity definitions tested. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
A disease name is not a reproducible phenotype. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Publish or Describe Computable Phenotypes”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 5 OF 15
5. Explain Dataset Construction and Data Flow
Why this move matters
Describe extraction, cleaning, transformations, deduplication, dates, derived variables and the path to the analytic file.
Did You Know? Data flow is the digital equivalent of a laboratory method.
Trace coded records into a research claim
Describe extraction, cleaning, transformations, deduplication, dates, derived variables and the path to the analytic file. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: A flow diagram shows raw encounters, duplicate resolution, eligibility and the final cohort. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
A polished table can hide many irreversible preprocessing choices. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Explain Dataset Construction and Data Flow”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 6 OF 15
6. Report Linkage Methods and Linkage Quality
Why this move matters
State deterministic or probabilistic rules, identifiers, privacy protection, match rates, false matches and unmatched records.
Did You Know? Linkage uncertainty can become outcome uncertainty.
Trace coded records into a research claim
State deterministic or probabilistic rules, identifiers, privacy protection, match rates, false matches and unmatched records. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: Hospitals use a trusted linkage service and report match quality without exposing identifiers. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Linked data are not automatically complete or correct. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Report Linkage Methods and Linkage Quality”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 7 OF 15
7. Define Population, Follow-up and Denominators
Why this move matters
Report eligibility, index date, entry, exit, censoring, migration, death and changing coverage.
Did You Know? Cohort time rules turn records into a population at risk.
Trace coded records into a research claim
Report eligibility, index date, entry, exit, censoring, migration, death and changing coverage. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: Patients enter at the emergency visit and are followed for 30 days unless coverage ends or death occurs. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Different denominators can make rates appear inconsistent. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Define Population, Follow-up and Denominators”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 8 OF 15
8. Define Outcomes, Exposures and Time Windows
Why this move matters
Explain clinical rationale, coding logic, competing events, recurrent events and measurement timing.
Did You Know? Time windows are substantive clinical choices.
Trace coded records into a research claim
Explain clinical rationale, coding logic, competing events, recurrent events and measurement timing. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: Follow-up means a documented outpatient review within seven days, while repeat emergency visits are counted over 30 days. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
A missing record may mean no event, care elsewhere or incomplete capture. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Define Outcomes, Exposures and Time Windows”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 9 OF 15
9. Describe Data Quality, Missingness and Changes
Why this move matters
Report completeness, plausibility, site variation, missing-data patterns, system migrations and remediation.
Did You Know? Missingness often reveals how care and software operate.
Trace coded records into a research claim
Report completeness, plausibility, site variation, missing-data patterns, system migrations and remediation. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: The study shows follow-up-field completeness by hospital and before and after an electronic-system change. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Treating missing as no can create bias. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Describe Data Quality, Missingness and Changes”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 10 OF 15
10. Open the Analysis and Computational Environment
Why this move matters
Report models, covariates, interactions, clustering, software, packages, versions, code and prespecification.
Did You Know? Computational detail makes analytic decisions inspectable.
Trace coded records into a research claim
Report models, covariates, interactions, clustering, software, packages, versions, code and prespecification. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: The model accounts for hospital clustering and records the exact software environment. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
A sophisticated model cannot repair a poorly defined cohort. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Open the Analysis and Computational Environment”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 11 OF 15
11. Address Confounding, Selection and Measurement Bias
Why this move matters
Explain causal assumptions, adjustment, unmeasured factors, care-seeking, informative missingness and surveillance differences.
Did You Know? Bias analysis begins with a causal story, not a long covariate list.
Trace coded records into a research claim
Explain causal assumptions, adjustment, unmeasured factors, care-seeking, informative missingness and surveillance differences. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: A directed diagram distinguishes severity, access and prior care from variables that may lie after follow-up. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Adjustment can introduce bias when variable roles are unclear. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Address Confounding, Selection and Measurement Bias”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 12 OF 15
12. Validate Codes, Phenotypes and Outcomes
Why this move matters
Report chart review, external comparison, clinician adjudication, sample selection, metrics and uncertainty.
Did You Know? Validation is an empirical result, not a permanent label.
Trace coded records into a research claim
Report chart review, external comparison, clinician adjudication, sample selection, metrics and uncertainty. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: A stratified sample of asthma definitions is reviewed against clinical notes with predictive values reported. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Validation in one hospital may not transfer to another. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Validate Codes, Phenotypes and Outcomes”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 13 OF 15
13. Run Sensitivity and Reproducibility Checks
Why this move matters
Test alternate definitions, windows, linkage assumptions, missing-data approaches and site exclusions; share materials where permitted.
Did You Know? Sensitivity analysis shows which conclusions depend on judgement calls.
Trace coded records into a research claim
Test alternate definitions, windows, linkage assumptions, missing-data approaches and site exclusions; share materials where permitted. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: Results are repeated with broader and narrower asthma phenotypes and different follow-up windows. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Robustness across chosen checks does not cover every unknown bias. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Run Sensitivity and Reproducibility Checks”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 14 OF 15
14. Report Governance, Privacy and Public Involvement
Why this move matters
Describe legal basis, approvals, access controls, minimisation, retention, public or patient input and disclosure safeguards.
Did You Know? Social licence grows from accountable practice, not secrecy alone.
Trace coded records into a research claim
Describe legal basis, approvals, access controls, minimisation, retention, public or patient input and disclosure safeguards. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: A secure environment limits access, and patient advisers review acceptable outputs and communication. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
De-identification does not eliminate every re-identification risk. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Report Governance, Privacy and Public Involvement”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
CHAPTER 15 OF 15
15. Interpret Clinical Meaning and Reuse Boundaries
Why this move matters
State generalisability, data limitations, code availability, update responsibility and what decisions the evidence can support.
Did You Know? A reproducible, bounded conclusion is more useful than a grand one.
Trace coded records into a research claim
State generalisability, data limitations, code availability, update responsibility and what decisions the evidence can support. Connect data provenance, coding systems, linkage, phenotypes, outcomes, governance, validation, analysis, reproducibility and limits.
Audit one electronic-record study
Worked example: The study identifies an association for the participating hospitals without claiming that follow-up alone caused every difference. Follow source systems, code lists, linkage, missingness, validation, modelling, sensitivity analysis and interpretation.
Keep routine data separate from ground truth
A structured field records what a system captured for a care or administrative purpose. It may omit context, reflect workflow and coding incentives, or change across sites and time.
Use CODE-EHR as a minimum-standards framework
Large sample size can make a narrow bias look precisely estimated. The 2022 CODE-EHR framework addresses structured electronic healthcare data in clinical research through minimum and preferred standards; it does not make every dataset complete, unbiased or suitable.
Practical reader check
- Point to the exact source or method sentence supporting the claim.
- Name the person, setting, date, input and decision rule that could change its meaning.
- Label the remaining uncertainty reported, inferred, contested or unknown.
Student, parent and career transfer
Make one evidence card for “Interpret Clinical Meaning and Reuse Boundaries”: source location, plain-language meaning, limit and next question. A parent or teacher can ask for one everyday explanation and one specialist explanation; both must preserve the same conditions. This habit supports health data science, medicine, nursing, pharmacy, epidemiology, informatics, statistics, public health, regulation, data governance, software and research ethics without promising a grade, admission, funding decision or job.
PRACTICE LAB
Build a CODE-EHR structured-record research reporting decision trail
Student route
- Choose one original report inside this guide’s scope.
- Copy its objective and underline actor, action, context, evidence and intended decision.
- Draw the specific reasoning chain used in this article.
- Mark each step reported, inferred, contested or unknown.
- Rewrite the conclusion so it preserves the narrowest important limitation.
Parent or teacher route
Ask the learner to explain how English helps us report structured electronic healthcare-record research so data sources, coding, linkage, governance, validation, analysis and reproducibility can be judged twice: once to a classmate and once to a method specialist. Praise a precise correction and a well-stated unknown; both show disciplined attention to evidence.
Early-career route
Create a one-page CODE-EHR structured-record research reporting audit with source link, date checked, scope, method logic, decisive wording, contrary evidence, limit and next action. A colleague should be able to continue without guessing.
Decision rule
If a shorter explanation removes the condition that made the original claim true, it is not clearer yet. Restore the condition.
Continue with eduKate’s guides to reading course and career information and giving useful feedback in group projects.
USEFUL NEXT READING
Continue with a related guide
Read the AGREE II guideline-appraisal guide for a related but different reader job. Return here when the question is specifically how to report structured electronic healthcare-record research so data sources, coding, linkage, governance, validation, analysis and reproducibility can be judged. Use the linked guide for its separate reader task, and this guide for the question defined here.
For English practice across research, school and career communication, visit the Singapore English Tuition Centre learning site.
FREQUENTLY ASKED QUESTIONS
Questions students and parents often ask
Is CODE-EHR a published framework?
Yes. It was published by The BMJ in 2022 as an open-access methods and reporting paper.
Does real-world data mean complete data?
No. Routine records reflect care, administration, software and coverage, with missingness and change over time.
What is a computable phenotype?
It is an operational, machine-implementable definition that maps a clinical concept to codes, fields, thresholds and time rules.
Why publish code lists?
They let readers inspect and reproduce how clinical concepts were identified, subject to privacy and governance limits.
Does a very large dataset remove bias?
No. Size can improve precision while confounding, selection and measurement bias remain.
How does governance affect research quality?
Clear permissions, secure access, minimisation and public involvement protect people and make data use more trustworthy.
PRIMARY SOURCES
Read the original CODE-EHR structured-record research reporting sources
Facts were checked against original publications and official records on 7 November 2026. Follow the live source for later corrections, completed updates or companion material.
- CODE-EHR original open-access publication, The BMJ
- CODE-EHR PubMed record
- CREWS real-world evidence guide on eduKate
- EMMA medicine-utilisation guide on eduKate
- Explaining scientific evidence on eduKate
A cheerful final note
Precise English helps us report structured electronic healthcare-record research so data sources, coding, linkage, governance, validation, analysis and reproducibility can be judged with greater generosity as well as greater precision. People can see how their evidence and values entered the work, where disagreement remains and what a responsible next step looks like.