The Effect of Laboratory Test–Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review. (23rd October 2019)

• Record Type: Journal Article
• Authors: Whitehead, Nedra S; Williams, Laurina; Meleth, Sreelatha; Kennedy, Sara; Ubaka-Blackmoore, Nneka; Kanter, Michael; O'Leary, Kevin J; Classen, David; Jackson, Brian; Murphy, Daniel R; Nichols, James; Stockwell, David; Lorey, Thomas; Epner, Paul; Taylor, Jennifer; Graber, Mark L
• Is Part Of: Journal of applied laboratory medicine. Volume 3:Number 6(2019)
• Page Start: 1035
• Page End: 1048
• Publication Date: 2019-10-23
• Subjects: Clinical chemistry -- Periodicals; Diagnosis, Laboratory -- Periodicals; 616.0756
• DOI: 10.1373/jalm.2018.028019
• Languages: English
• ISSNs: 2576-9456

Abstract

Abstract: Background: Laboratory and medication data in electronic health records create opportunities for clinical decision support (CDS) tools to improve medication dosing, laboratory monitoring, and detection of side effects. This systematic review evaluates the effectiveness of such tools in preventing medication-related harm. Methods: We followed the Laboratory Medicine Best Practice (LMBP) initiative's A-6 methodology. Searches of 6 bibliographic databases retrieved 8508 abstracts. Fifteen articles examined the effect of CDS tools on ( a ) appropriate dose or medication (n = 5), ( b ) laboratory monitoring (n = 4), ( c ) compliance with guidelines (n = 2), and ( d ) adverse drug events (n = 5). We conducted meta-analyses by using random-effects modeling. Results: We found moderate and consistent evidence that CDS tools applied at medication ordering or dispensing can increase prescriptions of appropriate medications or dosages [6 results, pooled risk ratio (RR), 1.48; 95% CI, 1.27–1.74]. CDS tools also improve receipt of recommended laboratory monitoring and appropriate treatment in response to abnormal test results (6 results, pooled RR, 1.40; 95% CI, 1.05–1.87). The evidence that CDS tools reduced adverse drug events was inconsistent (5 results, pooled RR, 0.69; 95% CI, 0.46–1.03). Conclusions: The findings support the practice of healthcare systems with the technological capability incorporating test-based CDS tools into their computerized physician ordering systems to ( a ) identify and flag prescription orders of inappropriate dose or medications at the time of ordering or dispensing and ( b ) alert providers to missing laboratory tests for medication monitoring or results that warrant a change in treatment. More research is needed to determine the ability of these tools to prevent adverse drug events.