Count me in: using a patient portal to minimize implicit bias in clinical research recruitment.

OBJECTIVE: Determine whether women and men differ in volunteering to join a Research Recruitment Registry when invited to participate via an electronic patient portal without human bias. MATERIALS AND METHODS: Under-representation of women and other demographic groups in clinical research studies could be due either to invitation bias (explicit or implicit) during screening and recruitment or by lower rates of deciding to participate when offered. By making an invitation to participate in a Research Recruitment Registry available to all patients accessing our patient portal, regardless of demographics, we sought to remove implicit bias in offering participation and thus independently assess agreement rates. RESULTS: Women were represented in the Research Recruitment Registry slightly more than their proportion of all portal users (n = 194 775). Controlling for age, race, ethnicity, portal use, chronic disease burden, and other questionnaire use, women were statistically more likely to agree to join the Registry than men (odds ratio 1.17, 95% CI, 1.12-1.21). In contrast, Black males, Hispanics (of both sexes), and particularly Asians (both sexes) had low participation-to-population ratios; this under-representation persisted in the multivariable regression model. DISCUSSION: This supports the view that historical under-representation of women in clinical studies is likely due, at least in part, to implicit bias in offering participation. Distinguishing the mechanism for under-representation could help in designing strategies to improve study representation, leading to more effective evidence-based recommendations. CONCLUSION: Patient portals offer an attractive option for minimizing bias and encouraging broader, more representative participation in clinical research.

Extracting actionable findings of appendicitis from radiology reports using natural language processing.

Radiology reports often contain findings about the condition of a patient which should be acted upon quickly. These actionable findings in a radiology report can be automatically detected to ensure that the referring physician is notified about such findings and to provide feedback to the radiologist that further action has been taken. In this paper we investigate a method for detecting actionable findings of appendicitis in radiology reports. The method identifies both individual assertions regarding the presence of appendicitis and other findings related to appendicitis using syntactic dependency patterns. All relevant individual statements from a report are collectively considered to determine whether the report is consistent with appendicitis. Evaluation on a corpus of 400 radiology reports annotated by two expert radiologists showed that our approach achieves a precision of 91%, a recall of 83%, and an F1-measure of 87%.

A machine learning approach for identifying anatomical locations of actionable findings in radiology reports.

Recognizing the anatomical location of actionable findings in radiology reports is an important part of the communication of critical test results between caregivers. One of the difficulties of identifying anatomical locations of actionable findings stems from the fact that anatomical locations are not always stated in a simple, easy to identify manner. Natural language processing techniques are capable of recognizing the relevant anatomical location by processing a diverse set of lexical and syntactic contexts that correspond to the various ways that radiologists represent spatial relations. We report a precision of 86.2%, recall of 85.9%, and F(1)-measure of 86.0 for extracting the anatomical site of an actionable finding. Additionally, we report a precision of 73.8%, recall of 69.8%, and F(1)-measure of 71.8 for extracting an additional anatomical site that grounds underspecified locations. This demonstrates promising results for identifying locations, while error analysis reveals challenges under certain contexts. Future work will focus on incorporating new forms of medical language processing to improve performance and transitioning our method to new types of clinical data.