An Integrated, Scalable, Electronic Video Consent Process to Power Precision Health Research: Large, Population-Based, Cohort Implementation and Scalability Study.

BACKGROUND: Obtaining explicit consent from patients to use their remnant biological samples and deidentified clinical data for research is essential for advancing precision medicine. OBJECTIVE: We aimed to describe the operational implementation and scalability of an electronic universal consent process that was used to power an institutional precision health biobank across a large academic health system. METHODS: The University of California, Los Angeles, implemented the use of innovative electronic consent videos as the primary recruitment tool for precision health research. The consent videos targeted patients aged ≥18 years across ambulatory clinical laboratories, perioperative settings, and hospital settings. Each of these major areas had slightly different workflows and patient populations. Sociodemographic information, comorbidity data, health utilization data (ambulatory visits, emergency room visits, and hospital admissions), and consent decision data were collected. RESULTS: The consenting approach proved scalable across 22 clinical sites (hospital and ambulatory settings). Over 40,000 participants completed the consent process at a rate of 800 to 1000 patients per week over a 2-year time period. Participants were representative of the adult University of California, Los Angeles, Health population. The opt-in rates in the perioperative (16,500/22,519, 73.3%) and ambulatory clinics (2308/3390, 68.1%) were higher than those in clinical laboratories (7506/14,235, 52.7%; P<.001). Patients with higher medical acuity were more likely to opt in. The multivariate analyses showed that African American (odds ratio [OR] 0.53, 95% CI 0.49-0.58; P<.001), Asian (OR 0.72, 95% CI 0.68-0.77; P<.001), and multiple-race populations (OR 0.73, 95% CI 0.69-0.77; P<.001) were less likely to participate than White individuals. CONCLUSIONS: This is one of the few large-scale, electronic video-based consent implementation programs that reports a 65.5% (26,314/40,144) average overall opt-in rate across a large academic health system. This rate is higher than those previously reported for email (3.6%) and electronic biobank (50%) informed consent rates. This study demonstrates a scalable recruitment approach for population health research.

Risk estimation, anxiety, and breast cancer worry in women at risk for breast cancer: A single-arm trial of personalized risk communication.

OBJECTIVE: Elevated anxiety and breast cancer worry can impede mammographic screening and early breast cancer detection. Genetic advances and risk models make personalized breast cancer risk assessment and communication feasible, but it is unknown whether such communication of risk affects anxiety and disease-specific worry. We studied the effect of a personalized breast cancer screening intervention on risk perception, anxiety, and breast cancer worry. METHODS: Women with a normal mammogram but elevated risk for breast cancer (N = 122) enrolled in the Athena Breast Health risk communication program were surveyed before and after receiving a letter conveying their breast cancer risk and a breast health genetic counselor consultation. We compared breast cancer risk estimation, anxiety, and breast cancer worry before and after risk communication and evaluated the relationship of anxiety and breast cancer worry to risk estimation accuracy. RESULTS: Women substantially overestimated their lifetime breast cancer risk, and risk communication somewhat mitigated this overestimation (49% pre-intervention, 42% post-intervention, 13% Gail model risk estimate, P < .001). Both general anxiety and breast cancer worry declined significantly after risk communication in women with high baseline anxiety. Baseline anxiety and breast cancer worry were essentially unrelated to risk estimation accuracy, but risk communication increased alignment of worry with accuracy of risk assessment. CONCLUSIONS: Personalized communication about breast cancer risk was associated with modestly improved risk estimation accuracy in women with relatively low anxiety and less anxiety and breast cancer worry in women with higher anxiety. We detected no negative consequences of informing women about elevated breast cancer risk.

Strategies to promote language inclusion at 17 CTSA hubs.

The prioritization of English language in clinical research is a barrier to translational science. We explored promising practices to advance the inclusion of people who speak languages other than English in research conducted within and supported by NIH Clinical Translational Science Award (CTSA) hubs. Key informant interviews were conducted with representatives (n = 24) from CTSA hubs (n = 17). Purposive sampling was used to identify CTSA hubs focused on language inclusion. Hubs electing to participate were interviewed via Zoom. Thematic analysis was performed to analyze interview transcripts. We report on strategies employed by hubs to advance linguistic inclusion and influence institutional change that were identified. Strategies ranged from translations, development of culturally relevant materials and consultations to policies and procedural changes and workforce initiatives. An existing framework was adapted to conceptualize hub strategies. Language justice is paramount to bringing more effective treatments to all people more quickly. Inclusion will require institutional transformation and CTSA hubs are well positioned to catalyze change.

Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study.

BACKGROUND: On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation. OBJECTIVE: The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients' changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility. METHODS: From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission. RESULTS: Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (ß=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (ß=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (ß=-.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (ß=-.13; SE 0.06; P=.04) was associated with toileting activities. CONCLUSIONS: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients' progress or decline.

Correction: Electronic Video Consent to Power Precision Health Research: A Pilot Cohort Study.

[This corrects the article DOI: 10.2196/29123.].

Electronic Video Consent to Power Precision Health Research: A Pilot Cohort Study.

BACKGROUND: Developing innovative, efficient, and institutionally scalable biospecimen consent for remnant tissue that meets the National Institutes of Health consent guidelines for genomic and molecular analysis is essential for precision medicine efforts in cancer. OBJECTIVE: This study aims to pilot-test an electronic video consent that individuals could complete largely on their own. METHODS: The University of California, Los Angeles developed a video consenting approach designed to be comprehensive yet fast (around 5 minutes) for providing universal consent for remnant biospecimen collection for research. The approach was piloted in 175 patients who were coming in for routine services in laboratory medicine, radiology, oncology, and hospital admissions. The pilot yielded 164 completed postconsent surveys. The pilot assessed the usefulness, ease, and trustworthiness of the video consent. In addition, we explored drivers for opting in or opting out. RESULTS: The pilot demonstrated that the electronic video consent was well received by patients, with high scores for usefulness, ease, and trustworthiness even among patients that opted out of participation. The revised more animated video pilot test in phase 2 was better received in terms of ease of use (P=.005) and the ability to understand the information (P<.001). There were significant differences between those who opted in and opted out in their beliefs concerning the usefulness of tissue, trusting researchers, the importance of contributing to science, and privacy risk (P<.001). The results showed that "I trust researchers to use leftover biological specimens to promote the public's health" and "Sharing a biological sample for research is safe because of the privacy protections in place" discriminated opt-in statuses were the strongest predictors (both areas under the curve were 0.88). Privacy concerns seemed universal in individuals who opted out. CONCLUSIONS: Efforts to better educate the community may be needed to help overcome some of the barriers in engaging individuals to participate in precision health initiatives.

Implementing a Population-Based Breast Cancer Risk Assessment Program.

BACKGROUND: Personalized breast cancer risk assessment is important in identifying and managing women at increased risk for breast cancer. However, there has been little evaluation of the practical aspects of implementing a population-based program that identifies and refers high-risk patients for further evaluation. PATIENTS AND METHODS: We implemented a semiautomated approach to collect personal and family history to identify women at high risk of breast cancer. On the basis of the survey, women identified as elevated risk received letters inviting them to telephone consultations with licensed breast health genetic counselors (BHGCs). High-risk women's history was verified and counseling and referrals provided, as appropriate. RESULTS: Among 20,558 women screened, 2000 (9.7%) women were identified as high risk on the basis of patient initial report. However, most (1,580) were excluded from receiving risk communication after BHGC review of risk information with the woman or because of previous attention to breast cancer risk or an abnormal mammogram. Among 420 subjects who received risk letters, 225 received a BHGC consultation. Of these 225 women, 63 were reclassified as average risk, 158 were referred to high-risk clinics, and 5 consultations were incomplete after determining that further information was needed. Of the 158 women referred to high-risk breast clinics, 51 attended an appointment. CONCLUSION: This study highlights the complex nature of a population-based breast cancer screening program in a clinical setting and shows the substantial effort needed to identify newly discovered women at high risk for breast cancer and refer them to appropriate services.

Role of Clinical and Imaging Risk Factors in Predicting Breast Cancer Diagnosis Among BI-RADS 4 Cases.

PURPOSE: To analyze women with suspicious findings (assessed as Breast Imaging Reporting and Data System [BI-RADS] 4), examining the value of clinical and imaging predictors in predicting cancer diagnosis. PATIENTS AND METHODS: A set of 2138 examinations (1978 women) given a BI-RADS 4 with matching pathology results were analyzed. Predictors such as patient demographics, clinical risk factors, and imaging-derived features such as BI-RADS assessment and qualitative breast density were considered. Independent predictors of breast cancer were determined by univariate analysis and multivariate logistic regression. RESULTS: In univariate analysis, age, race, body mass index, age at first live birth, BI-RADS assessment, qualitative breast density, and risk triggers were found to be independent predictors. In multivariate analysis, age, BI-RADS score, breast density, race, presence of a lump, and number of risk triggers were the most predictive. An integrative logistic regression model achieved a performance of 0.84 cross-validated area under the curve. No variable was a constant independent predictor when stratifying the population on the basis of the BI-RADS score. CONCLUSION: While BI-RADS assessment remains the strongest predictor of breast cancer, the inclusion of clinical risk factors such as age, breast density, presence of a lump, and number of risk triggers derived from guidelines improves the specificity of identifying individuals with imaging descriptors associated with BI-RADS 4A and 4B that are more likely to be diagnosed with breast cancer.