Add On Clinical Decision Support Systems

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Overview

While many Clinical Decision Support (CDS) tools are integrated into electronic health record (EHR) systems, there are some limits to the functionality and customizability of these tools. Individual health systems often build a significant portion of their own CDS within the EHR. Given the amount of time and resources it takes to develop, implement, and maintain CDS, as well as the desire for more advanced CDS functionality, there is a market third party add-on CDS systems. [1]

Add-on CDS systems are commonly secure Web-based programs that are customizable and integrate with the EHR. They can perform a number of functions, including:

  • Real-time surveillance with rule-based alerts
  • Adverse drug event monitoring
  • Prescriber utilization tracking
  • Support antimicrobial stewardship (notification for resistant pathogens, notification about pathogen-antimicrobial mismatch, track antimicrobial utilization, generate antibiograms)
  • Generate reports
  • Automatic safety reporting (for example, to the National Healthcare Safety Network)

These advanced CDS functions have the potential for cost savings and cost avoidance, and can support quality improvement work. They can also potentially be implemented at hospitals and health systems with less ability to customize and optimize CDS within their own EHR.

Example Uses

Adverse drug event monitoring

An add-on CDS system was used to monitor for potential adverse drug events at a community hospital. Authors prospectively evaluated alerts generated by the CDS system and found that 23% of high priority alerts were associated with an adverse drug event. They concluded that the add-on surveillance system was effective at identifying adverse drug events and could be used to prevent medication errors and patient harm. [2]

Pathogen-antimicrobial mismatch monitoring

An add-on CDS system was used to monitor for pathogen-antimicrobial mismatch. Authors conducted a quality improvement project to decrease the time between identification of resistant Gram-positive bacteremia and initiation of adequate antibiotics. Using the rule-based surveillance function of the CDS system, a member of the antimicrobial stewardship team was notified in real time and there was a patient was identified as having a resistant infection and was not on appropriate antibiotics. The project decreased the time until adequate coverage from 38 hours to 4.7 hours. [3]

Example Products

Below is a sampling of available add-on third party CDS system products.

Challenges

Despite their advantages, implementing add-on CDS systems pose a number of challenges for healthcare systems [4]:

  • Cost: Implementation cost and ongoing subscription fees. Healthcare systems may hesitate to purchase after investing in EHRs.
  • Data interfacing: Must be able to interface with healthcare system data sources, which takes time and resources to establish.
  • Data merging: Can interface with multiple sources and need to merge these datasets.
  • Data quality: Validity of CDS reports and tools is dependent on the underlying quality of the data.
  • Workflow integration: External Web-based programs need to be added to existing workflows.

References

  1. ↑ Forrest GN, Van Schooneveld TC, Kullar R, et al. Use of electronic health records and clinical decision support system for antimicrobial stewardship. Clin Infect Disease. 2014 Oct; 59(suppl_3):S122-S133. https://academic.oup.com/cid/article/59/suppl_3/S122/318775
  2. ↑ Jha AK, Laguette J, Seger A, Bates DW. Can surveillance systems identify and avert adverse drug events? A prospective evaluation of a commercial application. J Am Med Inform Assoc. 2008 Sep-Oct; 15(5):647-53. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2528042/
  3. ↑ Tchou MJ, Andersen H, Robinette E, Mortensen JE, Powell EA, Ankrum A, et al. Accelerating initiation of adequate antimicrobial therapy using real-time decision support and microarray testing. Pediatr Qual Saf. 2019 Jul-Aug; 4(4):e191. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6708656/
  4. ↑ Kirkendall ES, Ni Y, Lingren T, Leonard M, Hall ES, Melton K. Data challenges with real-time safety event detection and clinical decision support. J Med Internet Res. 2019 May; 21(5):e13047. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549472/

Submitted by Matthew Molloy

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