Clinical Decision Support (CDS)
Genomic medicine has the potential to improve diagnosis and inform treatments however in order to fulfil this potential genomic data and interpretation needs to be incorporated into CDS processes. Freimuth et al used the Agency for Healthcare Research and Quality (AHRQ) “Five Rights” framework to explore challenges to implementing effective pharmacogenomic CDS. The “Five Rights” framework states improvements in desired healthcare outcomes can be achieved with:
- The right information: evidence-based, suitable to guide action, pertinent to the circumstance
- To the right person: considering all members of the care team, including clinicians, patients, and their caretakers
- In the right CDS intervention format: such as an alert, order set, or reference information to answer a clinical question
- Through the right channel: for example, a clinical information system (CIS) such as an electronic medical record (EMR), personal health record (PHR), or a more general channel such as the Internet or a mobile device
- At the right time in workflow: for example, at time of decision/action/need
Freimuth et al found that the challenges for pharmacogenomics CDS fell into two categories: those that are related to information, including data representation and knowledge management, and those that are related to the delivery of information through clinical systems and workflows. These challenges are likely to be the same for other genomics applications.
As Kawamoto et al noted, the task of creating and maintaining clinical decision support materials requires significant expertise and effort and is most likely to be effectively done through centrally managed repositories. They suggest that organizations that could lead this process include academic medical centers, professional associations, governmental agencies, and commercial entities. EviQ is an example of an online CDS resource that is supported by the Australian Government, which provides evidence-based, consensus-driven cancer treatment protocols and information, including cancer genomics guidance, for use at the point of care.
Resource on Clinical Decision Support
Freimuth RR, Formea CM, Hoffman JM, Matey E, Peterson JF, Boyce RD. Implementing Genomic Clinical Decision Support for Drug‐Based Precision Medicine. CPT Pharmacometrics Syst Pharmacol. 2017;6(3):153-5. http://doi.org/10.1002/psp4.12173
Doig KD, Fellowes A, Bell AH, Seleznev A, Ma D, Ellul J, et al. PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories. Genome Med. 2017;9(1):38. http://doi.org/10.1186/s13073-017-0427-z
Herr T, Bielinski S, Bottinger E, Brautbar A, Brilliant M, Chute C, et al. Practical considerations in genomic decision support: The eMERGE experience. J Pathol Inform. 2015;6(1):50. http://doi.org/10.4103/2153-3539.165999
Watt S, Jiao W, Brown AM, Petrocelli T, Tran B, Zhang T, et al. Clinical genomics information management software linking cancer genome sequence and clinical decisions. Genomics. 2013;102(3):140-7. http://doi.org/10.1016/j.ygeno.2013.04.007
Overby CL, Kohane I, Kannry JL, Williams MS, Starren J, Bottinger E, et al. Opportunities for genomic clinical decision support interventions. Genet Med. 2013;15(10):10.1038/gim.2013.128. http://doi.org/10.1038/gim.2013.128
Kawamoto K, Lobach DF, Willard HF, Ginsburg GS. A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine. BMC Med Inform Decis Mak. 2009;9(1):17. http://doi.org/10.1186/1472-6947-9-17