Enriching knowledge and enhancing care through health data

Do you want to make better use of your EHR?
Do you want to improve the quality of your health data?


The quality of hospital EHR data is vital to the delivery of safe and effective patient care, and for the reuse of the data across the Learning Health System.

  • Patients and clinicians want healthcare to be safe, rapid and evidence based
  • Health care managers also want to use healthcare resources efficiently, and need insights for strategic planning
  • Public health agencies need reliable data to guide health care and prevention programmes
  • Healthcare funders need good quality data to reward high-quality and value-based care
  • Pharma want to reuse EHRs to accelerate clinical research
  • Regulators and HTA agencies want to be able to trust Real World Evidence in decision making
  • Everyone wants to achieve the best patient outcomes - and they all know that good data is a critical success factor

The vision of i~HD is to improve health care and to accelerate research through more trustworthy learning from health data. We engage with all stakeholders in order to co-create high- quality and secure environments for data sharing and data use. We aim to maximise efficiency and effectiveness, have better data quality, improve connectivity between practitioners and keep the patient centre stage.

To enable engagement with all stakeholders, i~HD is a neutral, not-for-profit institute.

Many European hospitals have expressed to us a wish to be better Learning Health Systems:

  • they want to measure and improve their outcomes
  • they want to collaborate with other hospitals working on similar outcome-related challenges
  • they want to scale up their participation in clinical research

In response to this, i~HD has developed assessment methods to guide data quality improvement, and is actively promoting the importance of data quality across Europe.

The i~HD DQS4H can assess and help improve the quality of EHR data for hospitals wishing to:

  • track evidence based care pathways
  • measure and improve health outcomes
  • attract more clinical trials
  • conduct more research and learning from their data

The i~HD Data Quality Task Force members bring over a decade of expertise in electronic health records, health data quality, assessment methodologies and certification programmes.

Based on extensive literature reviews the Task Force defined nine Data Quality Dimensions. As a result of original research projects (including PhD publications) algorithms were developed to generate validated and reproducible quantitative assessments of these data quality dimensions. The data quality assessments are conducted using both quantitative and qualitative analyses.



Hospitals choose which data to focus on for the assessment, depending on their main objectives.


The DQS4H service sequence


Preparation and planning

  • Webinar on data quality
  • Needs & objectives
  • Define the scope and domain(s)
  • Define the project
  • Scope the data set(s)
  • Partnership agreement
  • Assigning roles
  • Contracts

Dataset generation

  • Onsite visit
  • Prioritising the quality dimensions
  • Selecting the EHR variables
  • Agreeing the validation Rules
  • Pre-assessment of the data
  • Fine-tuning the data
  • Final data set for assessment

DQ Assessment

  • Validation of the dataset
  • Tools based analysis of the variables for agreed imensions
  • Graphical outputs + descriptive interpretation by i~HD informatics and medical experts
  • Preliminary findings discussed with the hospital, to exchange insights on causative factors

DQ Outputs

  • Final written report
  • Presentation to the team
  • Discussion of recommendations
  • Improvement strategy planning
  • Workshops, online tutorials

Extracts from a sample DQS4H report

In collaboration with:



After providing feedback on the report, we can provide advice on improvement strategies, and run workshops for your teams.

What makes the i~HD DQS4H unique?

Pragmatic: minimally invasive to hospital operations

Evidence based: well researched, published, assessment methodology

Flexible: can be tailored to your data quality drivers

Focussed: we can help you choose the most suitable dimensions and EHR data variables

Staged: clear sequence of steps with regular interactions and feedback loops

Holistic: we consider quality in the context of your user workflows and your EHR system

Extendable: data sets can be added incrementally, to chart out a data quality improvement journey


For further information please contact via DQS4H@i-hd.eu


Having great data to use and making great use of data


Published on: 19 November 2018