Measures of User experience in a Streptococcal pharyngitis and Pneumonia Clinical Decision Support Tools

Objective: To understand clinician adoption of CDS tools as this may provide important insights for the implementation and dissemination of future CDS tools. Materials and Methods: Clinicians (n=168) at a large academic center were randomized into intervention and control arms to assess the impact of strep and pneumonia CDS tools. Intervention arm data were analyzed to examine provider adoption and clinical workflow. Electronic health record data were collected on trigger location, the use of each component and whether an antibiotic, other medication or test was ordered. Frequencies were tabulated and regression analyses were used to determine the association of tool component use and physician orders. Results: The CDS tool was triggered 586 times over the study period. Diagnosis was the most frequent workflow trigger of the CDS tool (57%) as compared to chief complaint (30%) and diagnosis/antibiotic combinations (13%). Conversely, chief complaint was associated with the highest rate (83%) of triggers leading to an initiation of the CDS tool (opening the risk prediction calculator). Similar patterns were noted for initiation of the CDS bundled ordered set and completion of the entire CDS tool pathway. Completion of risk prediction and bundled order set components were associated with lower rates of antibiotic prescribing (OR 0.5; CI 0.2-1.2 and OR 0.5; CI 0.3-0.9, respectively). Discussion: Different CDS trigger points in the clinician user workflow lead to substantial variation in downstream use of the CDS tool components. These variations were important as they were associated with significant differences in antibiotic ordering. Conclusions: These results highlight the importance of workflow integration and flexibility for CDS success

Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system

Abstract. The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and population

Multi-Institutional Implementation of Clinical Decision Support for APOL1, NAT2, and YEATS4 Genotyping in Antihypertensive Management

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the APOL1, NAT2, and YEATS4 genes to guide optimal selection of antihypertensive medications among the African American population cared for at multiple participating institutions in a clinical trial. (2) Methods: The CDS committee, made up of clinical content and CDS experts, developed a framework and contributed to the creation of the CDS using the following guiding principles: 1. medical algorithm consensus; 2. actionability; 3. context-sensitive triggers; 4. workflow integration; 5. feasibility; 6. interpretability; 7. portability; and 8. discrete reporting of lab results. (3) Results: Utilizing the principle of discrete patient laboratory and vital information, a novel CDS for APOL1, NAT2, and YEATS4 was created for use in a multi-institutional trial based on a medical algorithm consensus. The alerts are actionable and easily interpretable, clearly displaying the purpose and recommendations with pertinent laboratory results, vitals and links to ordersets with suggested antihypertensive dosages. Alerts were either triggered immediately once a provider starts to order relevant antihypertensive agents or strategically placed in workflow-appropriate general CDS sections in the electronic health record (EHR). Detailed implementation instructions were shared across institutions to achieve maximum portability. (4) Conclusions: Using sound principles, the created genetic algorithms were applied across multiple institutions. The framework outlined in this study should apply to other disease-gene and pharmacogenomic projects employing CDS

Implementing a pragmatic clinical trial to tailor opioids for acute pain on behalf of the IGNITE ADOPT PGx investigators.

Opioid prescribing for postoperative pain management is challenging because of inter-patient variability in opioid response and concern about opioid addiction. Tramadol, hydrocodone, and codeine depend on the cytochrome P450 2D6 (CYP2D6) enzyme for formation of highly potent metabolites. Individuals with reduced or absent CYP2D6 activity (i.e., intermediate metabolizers [IMs] or poor metabolizers [PMs], respectively) have lower concentrations of potent opioid metabolites and potentially inadequate pain control. The primary objective of this prospective, multicenter, randomized pragmatic trial is to determine the effect of postoperative CYP2D6-guided opioid prescribing on pain control and opioid usage. Up to 2020 participants, age ≥8 years, scheduled to undergo a surgical procedure will be enrolled and randomized to immediate pharmacogenetic testing with clinical decision support (CDS) for CYP2D6 phenotype-guided postoperative pain management (intervention arm) or delayed testing without CDS (control arm). CDS is provided through medical record alerts and/or a pharmacist consult note. For IMs and PM in the intervention arm, CDS includes recommendations to avoid hydrocodone, tramadol, and codeine. Patient-reported pain-related outcomes are collected 10 days and 1, 3, and 6 months after surgery. The primary outcome, a composite of pain intensity and opioid usage at 10 days postsurgery, will be compared in the subgroup of IMs and PMs in the intervention (n = 152) versus the control (n = 152) arm. Secondary end points include prescription pain medication misuse scores and opioid persistence at 6 months. This trial will provide data on the clinical utility of CYP2D6 phenotype-guided opioid selection for improving postoperative pain control and reducing opioid-related risks

Longitudinal adoption rates of complex decision support tools in primary care

Translating research findings into practice promises to standardise care. Translation includes the integration of evidence-based guidelines at the point of care, discerning the best methods to disseminate research findings and models to sustain the implementation of best practices.By applying usability testing to clinical decision support(CDS) design, overall adoption rates of 60% can be realised.What has not been examined is how long adoption rates are sustained and the characteristics associated with long-term use. We conducted secondary analysis to decipher the factors impacting sustained use of CD Stools. This study was a secondary data analysis from a clinical trial conducted at an academic institution in New York City. Study data was identified patients electronic health records (EHR). The trial was to test the implementation of an integrated clinical prediction rule(iCPR) into the EHR. The primary outcome variable was iCPR tool acceptance of the tool. iCPR tool completion and iCPR smartest completion were additional outcome variables of interest. The secondary aim was to examine user characteristics associated with iCPR tool use in later time periods. Characteristics of interest included age, resident year, use of electronic health records (yes/no) and use of best practice alerts (BPA) (yes/no). Generalised linear mixed models (GLiMM) were used to compare iCPR use over time for each outcome of interest: namely, iCPR acceptance, iCPR completion and iCPR smartset completion.GLiMM was also used to examine resident characteristics associated with iCPR tool use in later time periods; specifically, intermediate and long-term (ie, 90+days). The tool was accepted, on average, 82.18% in the first 90 days (short-term period). The use decreases to 56.07% and 45.61% in intermediate and long-term time periods, respectively. There was a significant association between iCPR tool completion and time periods(