Summary
Objectives
The use of clinical decision support systems (CDSS) can improve the overall safety
and quality of health care delivery, but may also introduce machine-related errors.
Recent concerns about the potential for CDSS to harm patients have generated much
debate, but there is little research available to identify the nature of such errors,
or quantify their frequency or clinical impact.
Methods
A review of recent literature into electronic prescribing systems, as well as related
literature in decision support.
Results
There seems to be some evidence for variation in the outcomes of using CDSS, most
likely reflecting variations in clinical setting, culture, training and organizational
process, independent of technical variables. There is also preliminary evidence that
poorly implemented CDSS can lead to increased mortality in some settings. Studies
in the US, UK and Australia have found commercial prescribing systems often fail to
uniformly detect significant drug interactions, probably because of errors in their
knowledge base. Electronic medication management systems may generate new types of
error because of user-interface design, but al so because of events in the workplace
such as distraction affecting the actions of system users. Another potential source
of CDSS influenced errors are automation biases, including errors of omission where
individuals miss important data because the system does not prompt them to notice
them, and errors of commission where individuals do what the decision aid tells to
do, even when this contradicts their training and other available data. Errors of
dismissal occur when relevant alerts are ignored. On-line decision support systems
may also result in errors where clinicians come to an incorrect assessment of the
evidence, possibly shaped in part by cognitive decision biases.
Conclusions
The effectiveness of decision support systems, like all other health IT, cannot be
assessed purely by evaluating the usability and performance of the software, but is
the outcome of a complex set of cognitive and socio-technical interactions. A deeper
understanding of these issues can result in the design of systems which are not just
intrinsically ‘safe’ but which also result in safe outcomes in the hands of busy or
poorly resourced clinicians.
Keywords
Decision support systems - safety - error - automation bias - electronic prescribing
