Are abbreviations such as EHR, EMR, MCR, ... in the context of Real-World Evidence sometimes confusing you?
Prof. Dr Thomas Wilke compiled this list for the participants of his 1-day course Generating RWE for Optimising Market/Patient Access. It covers 29 acronyms that come up in RWE work, each with a short explanation in plain English.
Below you will find eight of them in full, and the complete index of all 29. The formatted PDF, with the explanation of every term, is yours to download and keep.
What is in the PDF
- 29 acronyms used in Real-World Evidence, from BOI to STC
- A short explanation of each one, in plain English, not textbook language
- A 7-page PDF, formatted for printing or for sharing with your team
- Compiled by Prof. Dr Thomas Wilke, who has directed RWE studies for more than 25 years
A sample from the list
These are the full entries, exactly as they appear in the PDF:
EHR: Electronic Healthcare Record
A digital version of a patient's comprehensive medical history maintained over time. It includes data such as diagnoses, medications, and treatment plans.
EMR: Electronic Medical Records
A digital system for storing a patient's medical information within one healthcare organisation. Unlike EHRs, EMRs are less easily shared across facilities.
MCR: Medical Chart Review
A retrospective method of reviewing patient records to extract clinical data for research or quality improvement purposes.
RWD: Real-World Data
Data collected outside of randomised controlled trials, such as from electronic health records, insurance claims, or patient registries, used to assess real-world treatment effectiveness.
RWE: Real-World Evidence
Insights derived from analysing real-world data, helping to understand treatment outcomes in everyday clinical settings.
RWI: Real-World Insights
In-depth analyses of real-world data to uncover patterns and trends that can inform healthcare decisions and strategies.
HEOR: Health Economics and Outcomes Research
Combines economic evaluation with patient outcomes to assess the value of healthcare interventions. It supports evidence-based decision-making in healthcare policy and practice.
MAIC: Matching-Adjusted Indirect Comparison
A statistical method used in health economics to compare treatments when direct head-to-head trials are unavailable. It adjusts patient-level data to improve comparability.
All 29 acronyms in the list
Here is everything the PDF covers. The explanation of each term is in the download.
BOI: Burden of Illness
CI: Confidence Interval
CPRD: Clinical Practice Research Datalink
EC: Ethical Committee
eCRF: Electronic Case Report Form
EDC: Electronic Data Capture
EHR: Electronic Healthcare Record
EMR: Electronic Medical Records
HCRU: Health Care Resource Use
HE: Health Economics
HEOR: Health Economics and Outcomes Research
HES: Hospital Episode Statistics
HrQoL: Health-related Quality of Life
HTA: Health Technology Assessment
MAIC: Matching-Adjusted Indirect Comparison
MCR: Medical Chart Review
NMA: Network Meta-Analysis
OS: Overall Survival
PFS: Progression-Free Survival
PRO: Patient-Reported Outcome
QBA: Quantitative Bias Analysis
RR: Risk Ratio
RWD: Real-World Data
RWE: Real-World Evidence
RWI: Real-World Insights
SCA: Synthetic Control Arm
SI: Site Investigator
SLR: Systematic Literature Review
STC: Simulated Treatment Comparison
Download the full list
Who compiled this list
Prof. Dr Thomas Wilke has directed real-world evidence studies across Europe for more than 25 years, covering database analyses, linked data studies, medical chart reviews and surveys. He heads the university-affiliated IPAM institute and works as a senior scientist at GIPAM, a European RWE consultancy. He has published widely on the subject.
He has been a CELforPharma faculty member since 2023. Participants consistently name the clarity of his teaching and the practical, pragmatic way he handles methodology.
Continue your learning from Thomas
If you’d like to learn more from Thomas, CELforPharma also offers a 1-day, hands-on course where you'll:
- Understand the full spectrum of RWE study types and data sources
- Learn how RWE can support payer, HTA, and access decision-making
- Gain insight into study design and statistical considerations
- Exchange experiences through expert-led discussions and practical examples
Generating RWE for Optimising Market/Patient Access
With Prof. Dr Thomas Wilke
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