Clinical decision support systems provide healthcare professionals assistance with clinical decision-making tasks at the point of care
Clinical decision support systems (CDSS) achieve clinical advice for patient care based on multiple items of patient data and knowledge management. Clinicians interact with decision support systems utilizing both their own knowledge and the CDSS, to make a better analysis of the patient’s data.
Decision support systems are used to help to analyse, diagnose and prescribe based on patient data and current evidence. Typically, a CDSS makes suggestions for the clinician to look through, and the clinician is expected to select useful information from the results.
Biological systems are profoundly complicated
A clinical decision support system may utilize an enormous range of potentially relevant data. For example, TreatGx is a medication support software that considers a patient’s condition(s), medical history, biophysical data and genetics, as well as the best published clinical data on treatments to provide medication options.
Overrides of alerts and patient risk
Traditional medical support systems produce massive number of alerts which is not a successful strategy because such alerts are ignored in 50 – 95% of cases. In addition alerts are unlikely to be effective within the time constraints of usual family practice. Overrides of alerts have the potential to cause patient harm.
TreatGx is an innovative tool that starts by considering a patient’s condition and provides options that are safe and effective for a given patient rather than alerts. TreatGx starts with all the possible options for treatment and results in a list of optimal, individualized drug therapy options. These drug options are adjusted for renal and hepatic function, comorbidities, concomitant medications and genetics.
Keeping up with the evidence
A major challenge for health care professionals is incorporating the extensive clinical research being published on an ongoing basis. Medication support systems, such as TreatGx , search, evaluate and incorporate the highest level of evidence into the system. A recent feasibility study showed that family physicians and pharmacists found TreatGx easy to implement in their practices, and to be a valuable tool to reduce inappropriate prescribing 1.
Knowledge-based CDSS often consist of three parts:
-
- The knowledge base (contains the rules and associations of compiled data)
-
- An inference engine (combines the rules from the knowledge base with the patient’s data)
- A mechanism to communicate (shows the results to the user).
[button size=’small’ style=” text= ‘Subscribe for updates’ icon=” icon_color=” link=’/content/subscribe/’ target=’_self’ color=” hover_color=” border_color=” hover_border_color=” background_color=” hover_background_color=” font_style=” font_weight=” text_align=” margin=”]
1. Dawes M, Aloise M, Ang S, Cullis P, Dawes D, Fraser R, Liknaitzky G, Paterson A, Stanley P, Suarez-Gonzalez A, and Katzov-Eckert H (2016). Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study. cmajo 4:E528-E534; doi:10.9778/cmajo.20150070