To explore the effect of apps measuring patient-reported outcomes (PROs) on patient-provider interaction in the rheumatic diseases in an observational setting.
 
  Patients in the Swiss Clinical Quality Management in Rheumatic Diseases Registry were offered mobile apps (iDialog and COmPASS) to track disease status between rheumatology visits using validated PROs (Rheumatoid Arthritis Disease Activity Index-5 score, Bath Ankylosing Spondylitis Disease Activity Index score, Routine Assessment of Patient Index Data-3 score and Visual Analogue Scale score for pain, disease activity and skin symptoms). We assessed two aspects of patient-provider interaction shared decision making (SDM) and physician awareness of disease fluctuations. We used logistic regressions to compare outcomes among patients who (1) used an app and discussed app data with their physician (app+discussion group), (2) used an app without discussing the data (app-only group) or (3) did not use any app (non-app users).
 
  2111 patients were analysemeasuring PROs may contribute little to patient-provider interactions without integration of app data into care processes.Cancer risk associations with commonly prescribed medications have been mainly evaluated in hypothesis-driven studies that focus on one drug at a time. Agnostic drug-wide association studies (DWAS) offer an alternative approach to simultaneously evaluate associations between a large number of drugs with one or more cancers using large-scale electronic health records. Although cancer DWAS approaches are promising, a number of challenges limit their applicability.  https://www.selleckchem.com/products/blu-451.html  This includes the high likelihood of false positivity; lack of biological considerations; and methodological shortcomings, such as inability to tightly control for confounders. As such, the value of DWAS is currently restricted to hypothesis generation with detected signals needing further evaluation. In this commentary, we discuss those challenges in more detail and summarize the approaches to overcome them by using published cancer DWAS studies, including the accompanied article by Støer and colleagues. Despite current concerns, DWAS future is filled with opportunities for developing innovative analytic methods and techniques that incorporate pharmacology, epidemiology, cancer biology, and genetics.See related article by Støer et al., p. 682.Medical financial hardship, including problems paying medical bills, distress, and forgoing care because of cost, is increasingly common among patients receiving cancer treatment and cancer survivors across the economic spectrum. Little is known, however, about provider practices for identifying patients who experience financial hardship and the strategies for mitigating hardship and addressing patient needs. In this editorial, we discuss a study of practices within the NCI Community Oncology Research Program. McLouth and colleagues found disparities in the use of screening and financial navigation and reliance on inadequate screening methods. To address these disparities, we emphasize the importance of comprehensive and ongoing financial hardship screening throughout the course of cancer treatment and survivorship care, as well as the necessity of accompanying counseling, navigation, and referrals. We also recommend key attributes of screening tools and a process for systematic implementation within clinical practice. With adverse health and economic consequences of the COVID-19 pandemic disproportionately affecting people who are racial or ethnic minorities, uninsured or underinsured, or living in poverty, the need to address medical financial hardship is more urgent than ever, to ensure that all people have an equal opportunity for high quality cancer treatment and survival.See related article by McLouth et al., p. 669.Early-life body size has been consistently associated with breast cancer risk. The direction of the association changes over time, with high birth weight, smaller adolescent body size, and adult weight gain all increasing breast cancer risk. There is also a clear positive association between larger body size and increased breast adipose tissue measured by mammograms, but less is known about how body size changes across life stages affect stromal and epithelial breast tissue. Using breast tissue slides from women with benign breast disease, Oh and colleagues applied machine learning methods to evaluate body size across the life course and adipose, epithelial, and stromal tissue concentrations in adulthood. They found consistent patterns for higher adipose and lower stromal tissue concentrations with larger childhood and adult body size at age 18 years. They reported lower levels of epithelial tissue with larger body size at 18 years, but not at other time periods. Additional studies examining how body size at different life stages may affect breast tissue composition will be important. Noninvasive methods that can provide measures of breast tissue composition may offer potential ways forward to ensure generalizability, and repeated measurements by life stage.See related article by Oh et al., p. 608.Breast cancer risk models increasingly are including mammographic density (MD) and polygenic risk scores (PRS) to improve identification of higher-risk women who may benefit from genetic screening, earlier and supplemental breast screening, chemoprevention, and other targeted interventions. Here, we present additional considerations for improved clinical use of risk prediction models with MD, PRS, and questionnaire-based risk factors. These considerations include whether changing risk factor patterns, including MD, can improve risk prediction and management, and whether PRS could help inform breast cancer screening without MD measures and prior to the age at initiation of population-based mammography. We further argue that it may be time to reconsider issues around breast cancer risk models that may warrant a more comprehensive head-to-head comparison with other methods for risk factor assessment and risk prediction, including emerging artificial intelligence methods. With the increasing recognition of limitations of any single mathematical model, no matter how simplified, we are at an important juncture for consideration of these different approaches for improved risk stratification in geographically and ethnically diverse populations.