This study evaluated oral medication adherence among adolescents and young adults (AYAs) with cancer during a trial of a smartphone-based medication reminder application (app). Methods: Twenty-three AYAs receiving at least one prescribed, scheduled oral medication related to their outpatient cancer treatment participated in this 12-week single-group interrupted time series longitudinal design study. Baseline oral medication adherence was monitored using electronic monitoring caps for 4 weeks. Participants then used a medication reminder app and continued to have their oral medication adherence monitored for 8 weeks. Participants completed an electronically administered weekly survey addressing perceived adherence and reasons for nonadherence. Results: Four adherence phenotypes were identified using visual graphical analysis of individual participants' weekly adherence: (1) high adherence during the preintervention and intervention periods (n = 13), (2) low preintervention adherence and improved adherence during the intervention period (n = 3), (3) low adherence during both periods (n = 6), and (4) high preintervention adherence and low adherence during the intervention period (n = 1). Growth curve models did not show significant changes in adherence by preintervention versus intervention trajectories (p > 0.05); however, the variance in adherence during the intervention narrowed for more highly adherent AYAs. ''Forgetfulness'' was the most frequently reported reason for nonadherence. Conclusion: Although overall adherence did not improve following use of the app, the variance decreased for more highly adherent participants. Additional or alternative interventions are needed for AYAs with persistently poor adherence. Assessment of adherence patterns may support individualized recommendation of tailored interventions.

Objective To elicit novel ideas for informatics solutions to support individuals through the menopausal transition. (Note: We use “individuals experiencing menopause” and “experiences” rather than “symptoms” when possible to counter typical framing of menopause as a cisgender women’s medical problem.) Methods A participatory design study was conducted 2015–2017 in the Western US. Two sessions were held with individuals experiencing menopause recruited from the general public; and 3 sessions with healthcare practitioners (HCPs) including nurses, physicians, and complementary and integrative health (CIH) practitioners were held. Participants designed technologies addressing informational needs and burdensome experiences. HCPs reflected on designs from participants experiencing menopause. Directed content analysis was used to analyze transcripts. Results Eight individuals experiencing menopause (n = 4 each session) and 18 HCPs (n = 10 CIH, n = 3 nurses, n = 5 physicians) participated. All participants provided ideas for solution purpose, hardware, software, features and functions, and data types. Individuals experiencing menopause designed technologies to help understand and prevent burdensome menopause experiences. HCPs designed technologies for tracking and facilitating communication. Compared to nurses and physicians, CIH practitioners suggested designs reframing menopause as a positive experience and accounted for the complex lives of individuals experiencing menopause, including stigma; these ideas corresponded to comments made by participants experiencing menopause. Participants from both populations were concerned about data confidentiality and technology accessibility. Conclusions Participant generated design ideas included novel ideas and incorporated existing technologies. This study can inform the development of new technologies or repurposing of existing technologies to support individuals through the menopausal transition.

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Background Family health history has been recognized as an essential factor for cancer risk assessment and is an integral part of many cancer screening guidelines, including genetic testing for personalized clinical management strategies. However, manually identifying eligible candidates for genetic testing is labor intensive. Objective The aim of this study was to develop a natural language processing (NLP) pipeline and assess its contribution to identifying patients who meet genetic testing criteria for hereditary cancers based on family health history data in the electronic health record (EHR). We compared an algorithm that uses structured data alone with structured data augmented using NLP. Methods Algorithms were developed based on the National Comprehensive Cancer Network (NCCN) guidelines for genetic testing for hereditary breast or ovarian and colorectal cancers. The NLP-augmented algorithm uses both structured family health history data and the associated unstructured free-text comments. The algorithms were compared with a reference standard of 100 patients with a family health history in the EHR. Results Regarding identifying the reference standard patients meeting the NCCN criteria, the NLP-augmented algorithm compared with the structured data algorithm yielded a significantly higher recall of 0.95 (95% CI 0.9-0.99) versus 0.29 (95% CI 0.19-0.40) and a precision of 0.99 (95% CI 0.96-1.00) versus 0.81 (95% CI 0.65-0.95). On the whole data set, the NLP-augmented algorithm extracted 33.6% more entities, resulting in 53.8% more patients meeting the NCCN criteria. Conclusions Compared with the structured data algorithm, the NLP-augmented algorithm based on both structured and unstructured family health history data in the EHR increased the number of patients identified as meeting the NCCN criteria for genetic testing for hereditary breast or ovarian and colorectal cancers.