Implementation of an Anticoagulation Practice Guideline for COVID-19 via a Clinical Decision Support System in a Large Academic Health System and Its Evaluation: Observational Study

Abstract

Background Studies evaluating strategies for the rapid development, implementation, and evaluation of clinical decision support (CDS) systems supporting guidelines for diseases with a poor knowledge base, such as COVID-19, are limited. Objective We developed an anticoagulation clinical practice guideline (CPG) for COVID-19, which was delivered and scaled via CDS across a 12-hospital Midwest health care system. This study represents a preplanned 6-month postimplementation evaluation guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. Methods The implementation outcomes evaluated were reach, adoption, implementation, and maintenance. To evaluate effectiveness, the association of CPG adherence on hospital admission with clinical outcomes was assessed via multivariable logistic regression and nearest neighbor propensity score matching. A time-to-event analysis was conducted. Sensitivity analyses were also conducted to evaluate the competing risk of death prior to intensive care unit (ICU) admission. The models were risk adjusted to account for age, gender, race/ethnicity, non-English speaking status, area deprivation index, month of admission, remdesivir treatment, tocilizumab treatment, steroid treatment, BMI, Elixhauser comorbidity index, oxygen saturation/fraction of inspired oxygen ratio, systolic blood pressure, respiratory rate, treating hospital, and source of admission. A preplanned subgroup analysis was also conducted in patients who had laboratory values (D-dimer, C-reactive protein, creatinine, and absolute neutrophil to absolute lymphocyte ratio) present. The primary effectiveness endpoint was the need for ICU admission within 48 hours of hospital admission. Results A total of 2503 patients were included in this study. CDS reach approached 95% during implementation. Adherence achieved a peak of 72% during implementation. Variation was noted in adoption across sites and nursing units. Adoption was the highest at hospitals that were specifically transformed to only provide care to patients with COVID-19 (COVID-19 cohorted hospitals; 74%-82%) and the lowest in academic settings (47%-55%). CPG delivery via the CDS system was associated with improved adherence (odds ratio [OR] 1.43, 95% CI 1.2-1.7; P<.001). Adherence with the anticoagulation CPG was associated with a significant reduction in the need for ICU admission within 48 hours (OR 0.39, 95% CI 0.30-0.51; P<.001) on multivariable logistic regression analysis. Similar findings were noted following 1:1 propensity score matching for patients who received adherent versus nonadherent care (21.5% vs 34.3% incidence of ICU admission within 48 hours; log-rank test P<.001). Conclusions Our institutional experience demonstrated that adherence with the institutional CPG delivered via the CDS system resulted in improved clinical outcomes for patients with COVID-19. CDS systems are an effective means to rapidly scale a CPG across a heterogeneous health care system. Further research is needed to investigate factors associated with adherence at low and high adopting sites and nursing units.

Characterizing Thrombotic Complication Risk Factors Associated With COVID-19 via Heterogeneous Patient Data: Retrospective Observational Study

“…These medications were associated with thrombotic complications with ORs of 5.375, 3.185, and 4.835, respectively. These were also the medications considered in a previous model developed to predict the requirement of ICU and VTE for COVID-19 patients [ 28 ]. Patient lab data, including the D-dimer level, platelet count, prothrombin time, fibrin degradation products, and fibrinogen, are available only in the EHR data set.…”

“…The first involved missing at least one variable in a data set. Given the low rate of such missingness (<2.5% for any individual variable), imputation was deemed unnecessary [ 28 , 31 ]. The other involved missing a category of data in one data set.…”

“…This research characterizes thrombotic complication risk factors associated with COVID-19 using information from EHR and insurance claims databases. Comprehensive treatment guidelines and reviews can be found in prior literature [ 27 , 28 ].…”

A randomized controlled trial for evaluating the impact of integrating a computerized clinical decision support system and a socially assistive humanoid robot into grand rounds during pre/post-operative care

“…Despite the growing need and interest for digital computer-aided support and many machine learning algorithms for classification and inference leading to automated interpretation of clinical data, their large-scale uptake is progressing rather slowly. 30 , 49 , 50 This is especially disconcerting since existing studies suggest CDSS can contribute to patient safety, 7 , 51 , 52 to adherence to clinical guidelines, 53 and in management of patients on research/treatment protocols. 54 Moreover, CDSSs can contribute to the optimization of healthcare costs by decreasing inpatient length-of-stay and reducing test duplication, 55 and can even serve as computerized “consultation” or filtering (i.e.…”

Business Not As Usual

“…9 The EPC used evidence-grading approaches to examine the emerging knowledge and inform development of the CPG. 10 The CPG was then embedded into the electronic health record as an order set (a type of decision support tool) to promote rapid implementation and consistent provider adherence. 11 Development of the CPG, from the start of evidence collection by the EPC to implementation of the order set, took 4 weeks.…”