This is by no means an all encompassing list, but I wanted to have a place to jot down some ideas for why CDSS are largely just mediocre. The big picture summary is that there are problems with the models to base the decisions on, access to quality, well organized data, a lack of standards for driving the CDS, and an ability to well integrate them into current hospital systems and workflows. Specifically:
Our models of disease aren’t good enough:
if we are making decision support for Rx and we still have not identified the correct representation of a disease, how can we predict it?
The data isn’t there / not accessible:
There are privacy issues, or infrastructure of hospital that the system will be used in is not setup to allow for connection and use of relevant data sources
Lack of communication between developers and users:
leading to a system that doesn’t fit in with clinical workflow
Integration with current hospital systems:
A CDS system can only work in practice when it is wellintegrated with existing medical information systems
Lack of standards:
Good CDSS are reliant on having standards for data, information models, language, and methods for referencing them. For example,
- Medications: RxNorm
- Information models: HL7
- Patient Data: some virtual medical record? (vMR)
- Approaches for terminology / ontology inferencing (OWL)
- and of course, Arden Syntax for taking data –> executable model
Suggested Citation:
Sochat, Vanessa. "Why have clinical decision support systems (CDSS) not worked?." @vsoch (blog), 21 Jul 2013, https://vsoch.github.io/2013/why-have-clinical-decision-support-systems-cdss-not-worked/ (accessed 28 Nov 24).