A Dynamic Complexity
The annual financial impact of poor data quality on organisations is estimated to lie between $9.7 million and $3.1 trillion (Gartner & IBM).
Health Data Quality
Good data provides the right insights. Be prepared for new opportunities!
- Deliver safe, personalised and evidence-based patient care
- Optimise care coordination and care pathways
- Embrace value-based healthcare
- Participate in clinical studies, accelerate new medical advances and open up new revenue streams
Minor errors, major implications
Ensuring high-quality data seems evident and straightforward, but unfortunately this is not the case in practice. Several studies have demonstrated that routinely collected health data today is of variable quality, and is often not fit for the intended purpose.
- Financial cost
- Decreased productivity
- Suboptimal outcomes
Clinical decisions can be misguided when based on low quality health data. For example, a study found that 48% of patients required additional monitoring, examination or treatment after medication errors resulting from weight errors (Selbst et al., 1999 , Pediatric Emergency Care). Further, cleaning data in preparation of conducting research is time-consuming.
Incomplete or incorrect personal health data can result in erroneous clinical decision support output. In addition, data quality issues in patient subpopulations can yield biased research findings.
Our data quality assessment framework
9 data quality dimensions
Designed for patient care, organisational learning and research
For health data providers, users and supporters
Based on scientific literature
Guided by our Data Quality Task Force
Approved by 70 hospital representatives