BackgroundAccording to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis.ObjectiveThe overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management.MethodsTo achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3).ResultsBetween 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021.ConclusionsThe results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics.International Registered Report Identifier (IRRID)DERR1-10.2196/16047

BACKGROUND:The usefulness of circulating tumor DNA (ctDNA) in detecting mutations and monitoring treatment response has not been well studied beyond a few actionable biomarkers in non-small cell lung cancer (NSCLC).RESEARCH QUESTION: How does the usefulness of ctDNA analysis compare with that of solid tumor biopsy analysis in patients with NSCLC? METHODS: We retrospectively evaluated 370 adult patients with NSCLC treated at the City of Hope between November 2015 and August 2019 to assess the usefulness of ctDNA in mutation identification, survival, concordance with matched tissue samples in 32 genes, and tumor evolution.RESULTS: A total of 1,688 somatic mutations were detected in 473 ctDNA samples from 370 patients with NSCLC. Of the 473 samples, 177 showed at least one actionable mutation with currently available Food and Drug Administration-approved NSCLC therapies. MET and CDK6 amplifications co-occurred with BRAF amplifications (false discovery rate [FDR], < 0.01), and gene-level mutations were mutually exclusive in KRAS and EGFR (FDR, 0.0009). Low cumulative percent ctDNA levels were associated with longer progression-free survival (hazard ratio [HR], 0.56; 95% CI, 0.37-0.85; P ¼ .006). Overall survival was shorter in patients harboring BRAF mutations (HR, 2.35; 95% CI, 1.24-4.6; P ¼ .009), PIK3CA mutations (HR, 2.77; 95% CI, 1.56-4.9; P < .001) and KRAS mutations (HR, 2.32; 95% CI, 1.30-4.1; P ¼ .004). Gene-level concordance was 93.8%, whereas the positive concordance rate was 41.6%. More mutations in targetable genes were found in ctDNA than in tissue biopsy samples. Treatment response and tumor evolution over time were detected in repeated ctDNA samples.INTERPRETATION: Although ctDNA analysis exhibited similar usefulness to tissue biopsy analysis, more mutations in targetable genes were missed in tissue biopsy analyses. Therefore, the evaluation of ctDNA in conjunction with tissue biopsy samples may help to detect additional targetable mutations to improve clinical outcomes in advanced NSCLC.

Background The ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. Although vaccines are  now available, effective treatment options are still out of reach. Results To characterize the transcriptional activity of SARS-CoV-2 infection, a gene signature consisting of 25 genes was generated using a publicly available RNA-Sequencing (RNA-Seq) dataset of cultured cells infected with SARS-CoV-2. The signature estimated infection level accurately in bronchoalveolar lavage fluid (BALF) cells and peripheral blood mononuclear cells (PBMCs) from healthy and infected patients (mean 0.001 vs. 0.958; P < 0.0001). These signature genes were investigated in their ability to distinguish the severity of SARS-CoV-2 infection in a single-cell RNA-Sequencing dataset. TNFAIP3, PPP1R15A, NFKBIA, and IFIT2 had shown bimodal gene expression in various immune cells from severely infected patients compared to healthy or moderate infection cases. Finally, this signature was assessed using the publicly available ConnectivityMap database to identify potential disease mechanisms and drug repurposing candidates. Pharmacological classes of tricyclic antidepressants, SRC-inhibitors, HDAC inhibitors, MEK inhibitors, and drugs such as atorvastatin, ibuprofen, and ketoconazole showed strong negative associations (connectivity score < − 90), highlighting the need for further evaluation of these candidates for their efficacy in treating SARS-CoV-2 infection. Conclusions Thus, using the 25-gene SARS-CoV-2 infection signature, the SARS-CoV-2 infection status was captured in BALF cells, PBMCs and postmortem lung biopsies. In addition, candidate SARS-CoV-2 therapies with known safety profiles were identified. The signature genes could potentially also be used to characterize the COVID-19 disease severity in patients’ expression profiles of BALF cells.

Background Understanding complexity in healthcare has the potential to reduce decision and treatment uncertainty. Therefore, identifying both patient and task complexity may offer better task allocation and design recommendation for next generation health information technology system design. Objective To identify the specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians. Method We observed and audio recorded the clinical rounds of three infectious disease teams. Thirty cases were observed for a period of four consecutive days. Transcripts were coded based on the clinical complexity-contributing factors from the clinical complexity model. Ratings of complexity on day 1 for each case were collected. We then used statistical methods to identify complexity-contributing factors in relationship to perceived complexity of clinicians. Results A factor analysis (principal component extraction with varimax rotation) of specific items revealed three factors (eigenvalues>2.0) explaining 47% of total variance, namely task interaction and goals (10 items, 26%, Cronbach’s Alpha=0.87), urgency and acuity (6 items, 11%, Cronbach’s Alpha=0.67), and psychosocial behavior (4 items, 10%, Cronbach’s alpha=0.55). A linear regression analysis showed no statistically significant association between complexity perceived by the physicians and objective complexity, which was measured from coded transcript by three clinicians (Multiple R-squared=0.13, p=0.61). There were no physician effects on the rating of perceived complexity. Conclusion Task complexity contributes significantly to overall complexity in the infectious disease domain. The different complexity-contributing factors found in this study can guide health information technology system designers and researchers for intuitive design. Different types of decision support tools can help to reduce the specific complexity- contributing factors found in this study. Future studies aimed at understanding clinical domain-specific complexity-contributing factors can ultimately improve task allocation and design for intuitive clinical reasoning.