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Background We examined neighborhood characteristics concerning breast cancer screening annual adherence during the COVID-19 pandemic. Methods We analyzed 6673 female patients aged 40 or older at increased inherited cancer risk in 2 large health care systems (NYU Langone Health [NYULH] and the University of Utah Health [UHealth]). Multinomial models were used to identify predictors of mammogram screening groups (non-adherent, pre-pandemic adherent, pandemic period adherent) in comparison to adherent females. Potential determinants included sociodemographic characteristics and neighborhood factors. Results Comparing each cancer group in reference to the adherent group, a reduced likelihood of being non-adherent was associated with older age (OR: 0.97, 95% CI: 0.95, 0.99), a greater number of relatives with cancer (OR: 0.80, 95% CI: 0.75, 0.86), and being seen at NYULH study site (OR: 0.42, 95% CI: 0.29, 0.60). More relatives with cancer were correlated with a lesser likelihood of being pandemic period adherent (OR: 0.89, 95% CI: 0.81, 0.97). A lower likelihood of being pre-pandemic adherent was seen in areas with less education (OR: 0.77, 95% CI: 0.62, 0.96) and NYULH study site (OR: 0.35, 95% CI: 0.22, 0.55). Finally, greater neighborhood deprivation (OR: 1.47, 95% CI: 1.08, 2.01) was associated with being non-adherent. Conclusion Breast screening during the COVID-19 pandemic was associated with being older, having more relatives with cancer, residing in areas with less educational attainment, and being seen at NYULH; non-adherence was linked with greater neighborhood deprivation. These findings may mitigate risk of clinically important screening delays at times of disruptions in a population at greater risk for breast cancer. Plain Language Summary Breast Cancer Screening Adherence in the US During COVID-19: We examined predictors of breast cancer screening adherence during COVID-19 at two large healthcare systems. Adherence was associated with older age, having more relatives with a cancer history, and living in areas with less educational attainment. Nonadherence was associated with greater neighborhood deprivation.

Patient-centered care promotes the involvement of patients in decision-making related to their health care. The adoption and implementation of shared decision-making (SDM) into routine care are constrained by several obstacles, including technical and time constraints, clinician and patient attitudes and perceptions, and processes that exist outside the standardized clinical workflow. We aimed to understand the integration and implementation characteristics of reported SDM interventions integrated into an electronic health record (EHR) system. We conducted a scoping review using the methodological framework by Arksey and O'Malley with guidance from the Joanna Briggs Institute. Eligibility criteria included original research and reviews focusing on SDM situations in a real-world clinical setting and EHR integration of SDM tools and processes. We excluded retrospective studies, conference abstracts, simulation studies, user design studies, opinion pieces, and editorials. To identify eligible studies, we searched the following databases on January 11, 2021: MEDLINE, Embase, CINAHL Complete, Cochrane Library including CENTRAL, PsycINFO, Scopus, and Web of Science Core Collection. We systematically categorized descriptive data and key findings in a tabular format using predetermined data charting forms. Results were summarized using tables and associated narratives related to the review questions. Of the 2153 studies, 18 (0.84%) were included in the final review. There was a high degree of variation across studies, including SDM definitions, standardized measures, technical integration, and implementation strategies. SDM tools that targeted established health care processes promoted their use. Integrating SDM templates and tools into an EHR appeared to improve the targeted outcomes of most (17/18, 94%) studies. Most SDM interventions were designed for clinicians. Patient-specific goals and values were included in 56% (10/18) of studies. The 2 most common study outcome measures were SDM-related measures and SDM tool use. Understanding how to integrate SDM tools directly into a clinician's workflow within the EHR is a logical approach to promoting SDM into routine clinical practice. This review contributes to the literature by illuminating features of SDM tools that have been integrated into an EHR system. Standardization of SDM tools and processes, including the use of patient decision aids, is needed for consistency across SDM studies. The implementation approaches for SDM applications showed varying levels of planning and effort to promote SDM intervention awareness. Targeting accepted and established clinical processes may enhance the adoption and use of SDM tools. Future studies designed as randomized controlled trials are needed to expand the quality of the evidence base. This includes the study of integration methods into EHR systems as well as implementation methods and strategies deployed to operationalize the uptake of the SDM-integrated tools. Emphasizing patients' goals and values is another key area for future studies.

Background Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature. Objective Our overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions. Methods We used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters. Results Twenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87). Conclusion We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.

Cancer genetic testing to assess an individual's cancer risk and to enable genomics-informed cancer treatment has grown exponentially in the past decade. Because of this continued growth and a shortage of health care workers, there is a need for automated strategies that provide high-quality genetics services to patients to reduce the clinical demand for genetics providers. Conversational agents have shown promise in managing mental health, pain, and other chronic conditions and are increasingly being used in cancer genetic services. However, research on how patients interact with these agents to satisfy their information needs is limited. Our primary aim is to assess user interactions with a conversational agent for pretest genetics education. We conducted a feasibility study of user interactions with a conversational agent who delivers pretest genetics education to primary care patients without cancer who are eligible for cancer genetic evaluation. The conversational agent provided scripted content similar to that delivered in a pretest genetic counseling visit for cancer genetic testing. Outside of a core set of information delivered to all patients, users were able to navigate within the chat to request additional content in their areas of interest. An artificial intelligence-based preprogrammed library was also established to allow users to ask open-ended questions to the conversational agent. Transcripts of the interactions were recorded. Here, we describe the information selected, time spent to complete the chat, and use of the open-ended question feature. Descriptive statistics were used for quantitative measures, and thematic analyses were used for qualitative responses. We invited 103 patients to participate, of which 88.3% (91/103) were offered access to the conversational agent, 39% (36/91) started the chat, and 32% (30/91) completed the chat. Most users who completed the chat indicated that they wanted to continue with genetic testing (21/30, 70%), few were unsure (9/30, 30%), and no patient declined to move forward with testing. Those who decided to test spent an average of 10 (SD 2.57) minutes on the chat, selected an average of 1.87 (SD 1.2) additional pieces of information, and generally did not ask open-ended questions. Those who were unsure spent 4 more minutes on average (mean 14.1, SD 7.41; P=.03) on the chat, selected an average of 3.67 (SD 2.9) additional pieces of information, and asked at least one open-ended question. The pretest chat provided enough information for most patients to decide on cancer genetic testing, as indicated by the small number of open-ended questions. A subset of participants were still unsure about receiving genetic testing and may require additional education or interpersonal support before making a testing decision. Conversational agents have the potential to become a scalable alternative for pretest genetics education, reducing the clinical demand on genetics providers.

Background Individuals with high risk for lung cancer may benefit from lung cancer screening, but there are associated risks as well as benefits. Shared decision‐making (SDM) tools with personalized information may provide key support for patients. Understanding patient perspectives on educational tools to facilitate SDM for lung cancer screening may support tool development. Aim This study aimed to explore patient perspectives related to a SDM tool for lung cancer screening using a qualitative approach. Methods We elicited patient perspectives by showing a provider‐facing SDM tool. Focus group interviews that ranged in duration from 1.5 to 2 h were conducted with 23 individuals with high risk for lung cancer. Data were interpreted inductively using thematic analysis to identify patients' thoughts on and desires for a patient‐facing SDM tool. Results The findings highlight that patients would like to have educational information related to lung cancer screening. We identified several key themes to be considered in the future development of patient‐facing tools: barriers to acceptance, preference against screening and seeking empowerment. One further theme illustrated effects of patient–provider relationship as a limitation to meeting lung cancer screening information needs. Participants also noted several suggestions for the design of technology decision aids. Conclusion These findings suggest that patients desire additional information on lung cancer screening in advance of clinical visits. However, there are several issues that must be considered in the design and development of technology to meet the information needs of patients for lung cancer screening decisions. Patient or Public Contribution Patients, service users, caregivers or members of the public were not involved in the study design, conduct, analysis or interpretation of the data. However, clinical experts in health communication provided detailed feedback on the study protocol, including the focus group approach. The study findings contribute to a better understanding of patient expectations for lung cancer screening decisions and may inform future development of tools for SDM.

Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. We examined users' interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users' clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. Findings highlight the chatbot's effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot's scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

Background Research on social determinants of genetic testing uptake is limited, particularly among unaffected patients with inherited cancer susceptibility. Methods We conducted a secondary analysis of the Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE) trial at University of Utah Health and NYU Langone Health, involving 2,760 unaffected patients meeting genetic testing criteria for inherited cancer susceptibility and who were initially randomized to either an automated chatbot or an enhanced standard of care (SOC) genetic services delivery model. We used encounters from the electronic health record (EHR) to measure the uptake of genetic counseling and testing, including dichotomous measures of (1) whether participants initiated pre-test cancer genetic services, (2) completed pre-test cancer genetic services, (3) had genetic testing ordered, and (4) completed genetic testing. We merged zip codes from the EHR to construct census tract-weighted social measures of the Social Vulnerability Index. Multilevel models estimated associations between social vulnerability and genetic services utilization. We tested whether intervention condition (i.e., chatbot vs. SOC) moderated the association of social vulnerability with genetic service utilization. Covariates included study arm, study site, age, sex, race/ethnicity, language preference, rural residence, having a recorded primary care provider, and number of algorithm criteria met. Results Patients living in areas of medium socioeconomic status (SES) vulnerability had lower odds of initiating pre-test genetic services (adjusted OR [aOR] = 0.81, 95% CI: 0.67, 0.98) compared to patients living in low SES vulnerability areas. Patients in medium household vulnerability areas had a lower likelihood of completing pre-test genetic services (aOR = 0.80, 95% CI: 0.66–0.97) and having genetic testing ordered (aOR = 0.79, 95% CI: 0.63–0.99) relative to patients in low household vulnerability areas. We did not find that social vulnerability associations varied by intervention condition. Conclusions These results underscore the importance of investigating social and structural mechanisms as potential pathways to increasing genetic testing uptake among patients with increased inherited risk of cancer. Census information is publicly available but seldom used to assess social determinants of genetic testing uptake among unaffected populations. Existing and future cohort studies can incorporate census data to derive analytic insights for clinical scientists. Trial registration BRIDGE was registered as NCT03985852 on June 6, 2019 at clinicaltrials.gov.

Digital health interventions could be a scalable approach to delivering at-home COVID-19 testing. SCALE-UP II aimed to investigate the effectiveness of three digital health interventions on the delivery of mailed at-home COVID-19 testing: text messaging (TM), automated chatbot (CA), and patient navigation upon request (PN). Pragmatic randomized controlled trial. Participants who self-reported that they had a smartphone were randomized in a 2x2x2 factorial design (Smartphone study) to receive (i) chatbot or TM; (ii) option to request PN; and (iii) intervention frequency every 10 or 30 days. All other participants were randomized in a 2x2 factorial design (Non-Smartphone study) to receive (i) option to request PN; and (ii) intervention frequency every 10 or 30 days. Study settings were safety net community health centers (CHCs) located across the state of Utah, USA. Eligible patients were >18 years old, with a primary care visit in the last three years, and a valid cellphone in the CHC electronic health record. The primary outcome was proportion of participants requesting at-home COVID-19 tests. The trial enrolled 2,117 in the Smartphone study and 31,439 in the Non-Smartphone study. In the Smartphone study, the proportion of participants who requested test kits in the Chatbot arm was lower than in TM (16.6% vs. 52.1%, aRR=0.317 [98.33% CI 0.27-0.38], P<.0001). In the Non-Smartphone study, the proportion of participants who requested test kits was higher if they were messaged every 10 days rather than every 30 days (5.5% vs 4.8%, aRR=1.144 [97.5% CI 1.03-1.28], P=.005). Yet, participants in the 10-day vs. 30-day condition were more likely to opt out of receiving study interventions (12.6% vs 7.3%, aRR=1.72 [97.5% CI 1.59-1.86], P<.0001). In the Non-Smartphone study, the proportion of participants who requested test kits was lower for those in the PN condition compared to No PN (4.3% vs 5.9%, aRR=0.729 [97.5% CI 0.65-0.81], P<.0001). Simple bidirectional TM was more effective than an interactive Web-based chatbot on the delivery of COVID-19 testing. Although messaging every 10 days was more effective than every 30 days, it also led to a larger opt-out rate. Digital health interventions based on automated bidirectional text messaging is a simple, scalable, and low-cost strategy to offer access to at-home COVID-19 testing. Similar approaches may be used to support public health response and other forms of at-home testing. Clinicaltrials.gov (NCT05533918 and NCT05533359). RR2-doi: 10.1136/bmjopen-2023-081455.

IntroductionEarly lung cancer screening (LCS) through low-dose CT (LDCT) is crucial but underused due to various barriers, including incomplete or inaccurate patient smoking data in the electronic health record and limited time for shared decision-making. The objective of this trial is to investigate a patient-centred intervention, MyLungHealth, delivered through the patient portal. The intervention is designed to improve LCS rates through increased identification of eligible patients and informed decision-making.Methods and analysisMyLungHealth is a multisite pragmatic trial, involving University of Utah Health and New York University Langone Health primary care clinics. The MyLungHealth intervention was developed using a user-centred design process, informed by patient and provider focus groups and interviews. The intervention’s effectiveness will be evaluated through a patient-randomised trial, comparing the combined use of MyLungHealth and DecisionPrecision+ (a provider-focused shared decision-making intervention) against DecisionPrecision+ alone. The first study hypothesis is that among patients aged 50–79 with uncertain LCS eligibility (eg, 10–19 pack-years or unknown pack-years or unknown quit date for individuals who used to smoke), MyLungHealth eligibility questionnaires will result in increased identification of LCS-eligible patients (n~26 729 patients). The second study hypothesis is that among patients aged 50–79 with documented LCS eligibility (20+ pack-years, quit within the last 15 years if individuals who used to smoke, and no recent screening or screening discussion), MyLungHealth education will result in increased LDCT ordering (n~4574 patients). Primary outcomes will be identification of LCS-eligible patients among individuals with uncertain LCS eligibility and LDCT ordering rates among individuals with documented LCS eligibility.Ethics and disseminationThe protocol was approved by the University of Utah Institutional Review Board (# 00153806). The patient data collected for this study will not be shared publicly due to the sensitive nature of the patient health information and the fact that we will not be obtaining written informed consent to allow public sharing of their data. Results will be disseminated through peer-reviewed publications.Trial registration numberClinicaltrials.gov, NCT06338592.

Background Tobacco use remains the leading cause of death and disability in the USA and is disproportionately concentrated among low socioeconomic status (SES) populations. Community Health Centers (CHCs) are a key venue for reaching low SES populations with evidence-based tobacco cessation treatment such as Quitlines. Electronic health record (EHR)-based interventions at the point-of-care, text messaging (TM), and phone counseling have the potential to increase Quitline reach and are feasible to implement within CHCs. However, there is a lack of data to inform how, when, and in what combination these strategies should be implemented. The aims of this cluster-randomized trial are to evaluate multi-level implementation strategies to increase the Reach (i.e., proportion of tobacco-using patients who enroll in the Quitline) and Impact (i.e., Reach × Efficacy [efficacy is defined as the proportion of tobacco-using patients who enroll in Quitline treatment that successfully quit]) and to evaluate characteristics of healthcare system, providers, and patients that may influence tobacco-use outcomes. Methods This study is a multilevel, three-phase, Sequential Multiple Assignment Randomized Trial (SMART), conducted in CHCs ( N  = 33 clinics; N  = 6000 patients). In the first phase, clinics will be randomized to two different EHR conditions. The second and third phases are patient-level randomizations based on prior treatment response. Patients who enroll in the Quitline receive no further interventions. In phase two, patients who are non-responders (i.e., patients who do not enroll in Quitline) will be randomized to receive either TM or continued-EHR. In phase three, patients in the TM condition who are non-responders will be randomized to receive either continued-TM or TM + phone coaching. Discussion This project will evaluate scalable, multi-level interventions to directly address strategic national priorities for reducing tobacco use and related disparities by increasing the Reach and Impact of evidence-based tobacco cessation interventions in low SES populations. Trial registration This trial was registered at ClinicalTrials.gov ( NCT03900767 ) on April 4th, 2019.