Objective: We critically evaluated the surgical literature to explore the prevalence and describe how equity assessments occur when using clinical decision support systems. Background: Clinical decision support (CDS) systems are increasingly used to facilitate surgical care delivery. Despite formal recommendations to do so, equity evaluations are not routinely performed on CDS systems and underrepresented populations are at risk of harm and further health disparities. We explored surgical literature to determine frequency and rigor of CDS equity assessments and offer recommendations to improve CDS equity by appending existing frameworks. Methods: We performed a scoping review up to Augus 25, 2021 using PubMed and Google Scholar for the following search terms: clinical decision support, implementation, RE-AIM, Proctor, Proctor's framework, equity, trauma, surgery, surgical. We identified 1415 citations and 229 abstracts met criteria for review. A total of 84 underwent full review after 145 were excluded if they did not assess outcomes of an electronic CDS tool or have a surgical use case. Results: Only 6% (5/84) of surgical CDS systems reported equity analyses, suggesting that current methods for optimizing equity in surgical CDS are inadequate. We propose revising the RE-AIM framework to include an Equity element (RE2-AIM) specifying that CDS foundational analyses and algorithms are performed or trained on balanced datasets with sociodemographic characteristics that accurately represent the CDS target population and are assessed by sensitivity analyses focused on vulnerable subpopulations. Conclusion: Current surgical CDS literature reports little with respect to equity. Revising the RE-AIM framework to include an Equity element (RE2-AIM) promotes the development and implementation of CDS systems that, at minimum, do not worsen healthcare disparities and possibly improve their generalizability.
Bibliographical noteFunding Information:
C.J.T. is a PI for 2 RCTs investigating ARBs in the treatment of COVID-19 among inpatient and outpatients. Co-Is include N.E.I. C.J.T. is supported by an AHRQ K12HS026379 focused on scaling of thoracic trauma clinical decision support systems. NIH NHLBI T32HL07741 (N.E.I.). T.J.L. was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number K23 GM140268. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. G.B.M. is supported by AHRQ R01HS024532-01A1, NIH/NIGMS R01 GM120079, NIH/NCRR U01 TR002062, NIH/NIDA R33 DA046084, NIH/NCATS UL1 TR002494. The remaining authors report no conflicts of interest.
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- evidence-based medicine
- machine learning