Medical leadership and management (MLM) skills are essential in preventing failings of healthcare; it is unknown how these attitudes can be developed during undergraduate medical education. This paper aims to quantify interest in MLM and recommends preferred methods of teaching and assessment at UK medical schools. Two questionnaires were developed, one sent to all UK medical school faculties, to assess executed and planned curriculum changes, and the other sent to medical students nationally to assess their preferences for teaching and assessment. Forty-eight percent of UK medical schools and 260 individual student responses were recorded. Student responses represented 60% of UK medical schools. 65% of schools valued or highly valued the importance of teaching MLM topics, compared with 93.2% of students. Students' favoured teaching methods were seminars or lectures (89.4%) and audit and quality improvement (QI) projects (77.8%). Medical schools preferred portfolio entries (55%) and presentations (35%) as assessment methods, whilst simulation exercises (76%) and audit reports (61%) were preferred by students. Preferred methods encompass experiential learning or simulation and a greater emphasis should be placed on encouraging student audit and QI projects. The curriculum changes necessary could be achieved via further integration into future editions of Tomorrow's Doctors.

Background The use of real-world data to generate evidence requires careful assessment and validation of critical variables before drawing clinical conclusions. Prospective clinical trial data suggest that anatomic origin of colon cancer impacts prognosis and treatment effectiveness. As an initial step in validating this observation in routine clinical settings, we explored the feasibility and accuracy of obtaining information on tumor sidedness from electronic health records (EHR) billing codes. Methods Nine thousand four hundred three patients with metastatic colorectal cancer (mCRC) were selected from the Flatiron Health database, which is derived from de-identified EHR data. This study included a random sample of 200 mCRC patients. Tumor site data derived from International Classification of Diseases (ICD) codes were compared with data abstracted from unstructured documents in the EHR (e.g. surgical and pathology notes). Concordance was determined via observed agreement and Cohen’s kappa coefficient (κ). Accuracy of ICD codes for each tumor site (left, right, transverse) was determined by calculating the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and corresponding 95% confidence intervals, using abstracted data as the gold standard. Results Study patients had similar characteristics and side of colon distribution compared with the full mCRC dataset. The observed agreement between the ICD codes and abstracted data for tumor site for all sampled patients was 0.58 (κ = 0.41). When restricting to the 62% of patients with a side-specific ICD code, the observed agreement was 0.84 (κ = 0.79). The specificity (92–98%) of structured data for tumor location was high, with lower sensitivity (49–63%), PPV (64–92%) and NPV (72–97%). Demographic and clinical characteristics were similar between patients with specific and non-specific side of colon ICD codes. Conclusions ICD codes are a highly reliable indicator of tumor location when the specific location code is entered in the EHR. However, non-specific side of colon ICD codes are present for a sizable minority of patients, and structured data alone may not be adequate to support testing of some research hypotheses. Careful assessment of key variables is required before determining the need for clinical abstraction to supplement structured data in generating real-world evidence from EHRs. Electronic supplementary material The online version of this article (10.1186/s12874-019-0824-7) contains supplementary material, which is available to authorized users.

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Purpose: Our objective was to describe differences in telemedicine use among women with metastatic breast cancer (mBC) by race, age, and geographic region.Methods: This was a retrospective cohort study of women with recurrent or de novo mBC treated in US community cancer practices that initiated a new line of therapy between March 2020 and February 2021. Multivariable modified Poisson regression models were used to calculate adjusted rate ratios (RR) and robust 95% confidence intervals (CI) associated with telemedicine visits within 90 days of therapy initiation.Results: Overall, among 3412 women with mBC, 751 (22%) patients had telemedi-

568 Background: The anatomical side of the colon from which a tumor arises is prognostic. Real-world evidence (RWE) studies in colon cancer should include this information. Can International Classification of Diseases (ICD) codes be reliably used to determine side of tumor, and, if so, when? Should electronic health records (EHR) be reviewed to supplement? Methods: Flatiron Health maintains an EHR-derived registry for metastatic colon cancer patients (current N = 9403). A random sample of 100 patients was included in this study. Data about tumor site was compared using ICD codes versus data abstracted from unstructured documents in the EHR (e.g., surgical notes). Concordance was determined via observed agreement and Cohen’s kappa coefficient (𝜿). Accuracy of ICD codes was determined by calculating the sensitivity, specificity, and positive predictive values, and corresponding 95% confidence intervals (CI), using abstracted data as the gold standard. Results: Sampled patients had similar side of colon distribution compared with the full registry: left colon (32% vs. 30%, respectively), right (17% vs. 25%), transverse (7% vs. 5%), unspecified side (39% vs. 37%), and rectum (5% vs. 2%). ICD codes can be general, or specific to the side of tumor. The observed agreement between the ICD codes and abstracted data for tumor site for all sampled patients was 0.57 (𝜿 = 0.42). When restricting to the 56% of patients with a side-specific ICD code, the observed agreement was 0.91 (𝜿 = 0.85). See table for all accuracy estimates. Conclusions: RWE offers growing opportunities for oncology research. Before meaningful RWE analyses can take place, data must be carefully characterized. Here, we describe tumor side EHR data in colon cancer and find that ICD codes specifying tumor side are available over half of the time. When these ICD codes are available, the accuracy of tumor side information in ICD codes is high. When not available, the dataset can be supplemented by chart abstraction. [Table: see text]