Advancing health equity through implementation science: Identifying and examining measures of the outer setting.

BACKGROUND: Implementation science (IS) could accelerate progress toward achieving health equity goals. However, the lack of attention to the outer setting where interventions are implemented limits applicability and generalizability of findings to different populations, settings, and time periods. We developed a data resource to assess outer setting across seven centers funded by the National Cancer Institute's IS Centers in Cancer Control (ISC3) Network Program. OBJECTIVE: To describe the development of the Outer Setting Data Resource and characterize the county-level outer context across Centers. METHODS: Our Data Resource captures seven key environments, including: (1) food; (2) physical; (3) economic; (4) social; (5) health care; (6) cancer behavioral and screening; and (7) cancer-related policy. Data were obtained from public sources including the US Census and American Community Survey. We present medians and interquartile ranges based on the distribution of all counties in the US, all ISC3 centers, and within each Center for twelve selected measures. Distributions of each factor are compared with the national estimate using single sample sign tests. RESULTS: ISC3 centers' catchment areas include 458 counties and over 126 million people across 28 states. The median percentage of population living within ½ mile of a park is higher in ISC3 counties (38.0%, interquartile range (IQR): 16.0%-59.0%) compared to nationally (18.0%, IQR: 7.0%-38.0%; p < 0.0001). The median percentage of households with no broadband access is significantly lower in ISC3 counties (28.4%, IQR: 21.4%-35.6%) compared the nation overall (32.8%, IQR: 25.8%-41.2%; p < 0.0001). The median unemployment rate was significantly higher in ISC3 counties (5.2%, IQR: 4.1%-6.4%) compared to nationally (4.9%, 3.6%-6.3%, p = 0.0006). CONCLUSIONS: Our results indicate that the outer setting varies across Centers and often differs from the national level. These findings demonstrate the importance of assessing the contextual environment in which interventions are implemented and suggest potential implications for intervention generalizability and scalability.

The Academic Learning Health System: A Framework for Integrating the Multiple Missions of Academic Medical Centers.

The learning health system (LHS) has emerged over the past 15 years as a concept for improving health care delivery. Core aspects of the LHS concept include: promoting improved patient care through organizational learning, innovation, and continuous quality improvement; identifying, critically assessing, and translating knowledge and evidence into improved practices; building new knowledge and evidence around how to improve health care and health outcomes; analyzing clinical data to support learning, knowledge generation, and improved patient care; and engaging clinicians, patients, and other stakeholders in processes of learning, knowledge generation, and translation. However, the literature has paid less attention to how these LHS aspects may integrate with the multiple missions of academic medical centers (AMCs). The authors define an academic learning health system (aLHS) as an LHS built around a robust academic community and central academic mission, and they propose 6 features that emphasize how an aLHS differs from an LHS. An aLHS capitalizes on embedded academic expertise in health system sciences; engages the full spectrum of translational investigation from mechanistic basic sciences to population health; builds pipelines of experts in LHS sciences and clinicians with fluency in practicing in an LHS; applies core LHS principles to the development of curricula and clinical rotations for medical students, housestaff, and other learners; disseminates knowledge more broadly to advance the evidence for clinical practice and health systems science methods; and addresses social determinants of health, creating community partnerships to mitigate disparities and improve health equity. As AMCs evolve, the authors expect that additional differentiating features and ways to operationalize the aLHS will be identified and hope this article stimulates further discussion around the intersection of the LHS concept and AMCs.

Monitoring the Implementation of Tobacco Cessation Support Tools: Using Novel Electronic Health Record Activity Metrics.

BACKGROUND: Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias. OBJECTIVE: This study aims to develop a novel monitoring method leveraging EHR activity data and demonstrate its use in monitoring the CDS tools implemented by a tobacco cessation program sponsored by the National Cancer Institute's Cancer Center Cessation Initiative (C3I). METHODS: We developed EHR-based metrics to monitor the implementation of two CDS tools: (1) a screening alert reminding clinic staff to complete the smoking assessment and (2) a support alert prompting health care providers to discuss support and treatment options, including referral to a cessation clinic. Using EHR activity data, we measured the completion (encounter-level alert completion rate) and burden (the number of times an alert was fired before completion and time spent handling the alert) of the CDS tools. We report metrics tracked for 12 months post implementation, comparing 7 cancer clinics (2 clinics implemented the screening alert and 5 implemented both alerts) within a C3I center, and identify areas to improve alert design and adoption. RESULTS: The screening alert fired in 5121 encounters during the 12 months post implementation. The encounter-level alert completion rate (clinic staff acknowledged completion of screening in EHR: 0.55; clinic staff completed EHR documentation of screening results: 0.32) remained stable over time but varied considerably across clinics. The support alert fired in 1074 encounters during the 12 months. Providers acted upon (ie, not postponed) the support alert in 87.3% (n=938) of encounters, identified a patient ready to quit in 12% (n=129) of encounters, and ordered a referral to the cessation clinic in 2% (n=22) of encounters. With respect to alert burden, on average, both alerts fired over 2 times (screening alert: 2.7; support alert: 2.1) before completion; time spent postponing the screening alert was similar to completing (52 vs 53 seconds) the alert, and time spent postponing the support alert was more than completing (67 vs 50 seconds) the alert per encounter. These findings inform four areas where the alert design and use can be improved: (1) improving alert adoption and completion through local adaptation, (2) improving support alert efficacy by additional strategies including training in provider-patient communication, (3) improving the accuracy of tracking for alert completion, and (4) balancing alert efficacy with the burden. CONCLUSIONS: EHR activity metrics were able to monitor the success and burden of tobacco cessation alerts, allowing for a more nuanced understanding of potential trade-offs associated with alert implementation. These metrics can be used to guide implementation adaptation and are scalable across diverse settings.

Creating research-ready partnerships: the initial development of seven implementation laboratories to advance cancer control.

BACKGROUND: In 2019-2020, with National Cancer Institute funding, seven implementation laboratory (I-Lab) partnerships between scientists and stakeholders in 'real-world' settings working to implement evidence-based interventions were developed within the Implementation Science Centers in Cancer Control (ISC3) consortium. This paper describes and compares approaches to the initial development of seven I-Labs in order to gain an understanding of the development of research partnerships representing various implementation science designs. METHODS: In April-June 2021, members of the ISC3 Implementation Laboratories workgroup interviewed research teams involved in I-Lab development in each center. This cross-sectional study used semi-structured interviews and case-study-based methods to collect and analyze data about I-Lab designs and activities. Interview notes were analyzed to identify a set of comparable domains across sites. These domains served as the framework for seven case descriptions summarizing design decisions and partnership elements across sites. RESULTS: Domains identified from interviews as comparable across sites included engagement of community and clinical I-Lab members in research activities, data sources, engagement methods, dissemination strategies, and health equity. The I-Labs use a variety of research partnership designs to support engagement including participatory research, community-engaged research, and learning health systems of embedded research. Regarding data, I-Labs in which members use common electronic health records (EHRs) leverage these both as a data source and a digital implementation strategy. I-Labs without a shared EHR among partners also leverage other sources for research or surveillance, most commonly qualitative data, surveys, and public health data systems. All seven I-Labs use advisory boards or partnership meetings to engage with members; six use stakeholder interviews and regular communications. Most (70%) tools or methods used to engage I-Lab members such as advisory groups, coalitions, or regular communications, were pre-existing. Think tanks, which two I-Labs developed, represented novel engagement approaches. To disseminate research results, all centers developed web-based products, and most (n = 6) use publications, learning collaboratives, and community forums. Important variations emerged in approaches to health equity, ranging from partnering with members serving historically marginalized populations to the development of novel methods. CONCLUSIONS: The development of the ISC3 implementation laboratories, which represented a variety of research partnership designs, offers the opportunity to advance understanding of how researchers developed and built partnerships to effectively engage stakeholders throughout the cancer control research lifecycle. In future years, we will be able to share lessons learned for the development and sustainment of implementation laboratories.

Effect of a Machine Learning Recommender System and Viral Peer Marketing Intervention on Smoking Cessation: A Randomized Clinical Trial.

Importance: Novel data science and marketing methods of smoking-cessation intervention have not been adequately evaluated. Objective: To compare machine learning recommender (ML recommender) computer tailoring of motivational text messages vs a standard motivational text-based intervention (standard messaging) and a viral peer-recruitment tool kit (viral tool kit) for recruiting friends and family vs no tool kit in a smoking-cessation intervention. Design, Setting, and Participants: This 2 ×2 factorial randomized clinical trial with partial allocation, conducted between July 2017 and September 2019 within an online tobacco intervention, recruited current smokers aged 18 years and older who spoke English from the US via the internet and peer referral. Data were analyzed from March through May 2022. Interventions: Participants registering for the online intervention were randomly assigned to the ML recommender or standard messaging groups followed by partially random allocation to access to viral tool kit or no viral tool kit groups. The ML recommender provided ongoing refinement of message selection based on user feedback and comparison with a growing database of other users, while the standard system selected messages based on participant baseline readiness to quit. Main Outcomes and Measures: Our primary outcome was self-reported 7-day point prevalence smoking cessation at 6 months. Results: Of 1487 participants who smoked (444 aged 19-34 years [29.9%], 508 aged 35-54 years [34.1%], 535 aged ≥55 years [36.0%]; 1101 [74.0%] females; 189 Black [12.7%] and 1101 White [78.5%]; 106 Hispanic [7.1%]), 741 individuals were randomly assigned to the ML recommender group and 746 individuals to the standard messaging group; viral tool kit access was provided to 745 participants, and 742 participants received no such access. There was no significant difference in 6-month smoking cessation between ML recommender (146 of 412 participants [35.4%] with outcome data) and standard messaging (156 of 389 participants [40.1%] with outcome data) groups (adjusted odds ratio, 0.81; 95% CI, 0.61-1.08). Smoking cessation was significantly higher in viral tool kit (177 of 395 participants [44.8%] with outcome data) vs no viral tool kit (125 of 406 participants [30.8%] with outcome data) groups (adjusted odds ratio, 1.48; 95% CI, 1.11-1.98). Conclusions and Relevance: In this study, machine learning-based selection did not improve performance compared with standard message selection, while viral marketing did improve cessation outcomes. These results suggest that in addition to increasing dissemination, viral recruitment may have important implications for improving effectiveness of smoking-cessation interventions. Trial Registration: ClinicalTrials.gov Identifier: NCT03224520.

Characterizing Pandemic-Related Changes in Smoking Over Time in a Cohort of Current and Former Smokers.

INTRODUCTION: We used a longitudinal cohort of US adults who were current or former smokers to explore how three participant-reported factors-general stress, coronavirus disease of 2019 (COVID-19) distress, and perceived risk of complications from COVID-19 related to smoking-were associated with changes in smoking status. METHODS: Smoking status was assessed at three time points. Timepoint 1 status was assessed at a prior study completion (2018-2020). Timepoint 2 (start of the pandemic), and Timepoint 3 (early phase of the pandemic) statuses were assessed using an additional survey in 2020. After classifying participants into eight groups per these time points, we compared the means of participant-reported factors and used a linear regression model to adjust for covariates. RESULTS: Participants (n = 392) were mostly female (73.9%) and non-Hispanic White (70.1%). Between Timepoints 2 and 3, abstinence rates decreased by 11%, and 40% of participants reported a smoking status change. Among those reporting a change and the highest general stress levels, newly abstinent participants had higher perceived risk of complications from COVID-19 related to smoking than those who relapsed during pandemic (mean (SD): 14.2 (3.3) vs. 12.6 (3.8)). Compared to participants who sustained smoking, those who sustained abstinence, on average, scored 1.94 less on the general stress scale (ßeta Coefficient (ß): -1.94, p-value < .01) and 1.37 more on the perceived risk of complications from COVID-19 related to smoking scale (ß: 1.37, p-value .02). CONCLUSIONS: Decreased abstinence rates are concerning. Patterns of reported factors were as expected for individuals who sustained their smoking behavior but not for those who changed. IMPLICATIONS: We observed an increase in smoking rates during the COVID-19 pandemic. In exploring how combinations of general stress levels, COVID-19 distress levels, and perceived risk of complications from COVID-19 related to smoking were associated with changes in smoking, we observed expected patterns of these factors among individuals who sustained abstinence or smoking. Among individuals who changed smoking status and reported high stress levels, those who reported a higher perceived risk of complications from COVID-19 related to smoking abstained from smoking. In contrast, those who reported a lower perceived risk of complications from COVID-19 related to smoking, started smoking. An intersectional perspective may be needed to understand smokers' pandemic-related behavior changes.

Understanding Patients' Intention to Use Digital Health Apps That Support Postdischarge Symptom Monitoring by Providers Among Patients With Acute Coronary Syndrome: Survey Study.

BACKGROUND: After hospital discharge, patients with acute coronary syndrome (ACS) often experience symptoms that prompt them to seek acute medical attention. Early evaluation of postdischarge symptoms by health care providers may reduce unnecessary acute care utilization. However, hospital-initiated follow-up encounters are insufficient for timely detection and assessment of symptoms. While digital health tools can help address this issue, little is known about the intention to use such tools in ACS patients. OBJECTIVE: This study aimed to assess ACS patients' intention to use digital health apps that support postdischarge symptom monitoring by health care providers and identify patient-perceived facilitators and barriers to app use. METHODS: Using email invitations or phone calls, we recruited ACS patients discharged from a central Massachusetts health care system between December 2020 and April 2021, to participate in the study. Surveys were delivered online or via phone to individual participants. Demographics and access to technology were assessed. The intention to use a symptom monitoring app was assessed using 5-point Likert-type (from strongly agree to strongly disagree) items, such as "If this app were available to me, I would use it." Responses were compared across demographic subgroups and survey delivery methods. Two open-ended questions assessed perceived facilitators and barriers to app use, with responses analyzed using qualitative content analysis. RESULTS: Among 100 respondents (response rate 8.1%), 45 (45%) completed the survey by phone. The respondents were on average 68 years old (SD 13 years), with 90% (90/100) White, 39% (39/100) women, and 88% (88/100) having access to the internet or a mobile phone. Most participants (65/100, 65%) agreed or strongly agreed that they would use the app, among which 53 (82%) would use the app as often as possible. The percentage of participants with the intention to use the app was 75% among those aged 65-74 years and dropped to 44% among those older than 75 years. The intention to use was higher in online survey respondents (vs phone survey respondents; odds ratio 3.07, 95% CI 1.20-7.88) after adjusting for age and access to technology. The analysis of open-ended questions identified the following 4 main facilitators (motivations): (1) easily reaching providers, (2) accessing or providing information, (3) quickly reaching providers, and (4) consulting providers for symptoms, and the following 4 main barriers: (1) privacy/security concerns, (2) uncomfortable using technology, (3) user-unfriendly app interface, and (4) preference for in-person/phone care. CONCLUSIONS: There was a strong intention to use a symptom monitoring app postdischarge among ACS patients. However, this intent decreased in patients older than 75 years. The survey identified barriers related to technology use, privacy/security, and the care delivery mode. Further research is warranted to determine if such intent translates into app use, and better symptom management and health care quality.

Effect of Technology-Assisted Brief Abstinence Game on Long-term Smoking Cessation in Individuals Not Yet Ready to Quit: A Randomized Clinical Trial.

IMPORTANCE: Most trials of behavioral or pharmaceutical interventions for people who smoke are limited to individuals reporting they are ready to quit smoking. Engaging individuals who initially report they are not yet ready to quit in brief, precessation, skills-building interventions (eg, practice quit attempts or nicotine replacement therapy [NRT] sampling) is challenging. OBJECTIVE: To test an integrated behavioral plus NRT-sampling intervention using a gamification approach supported by mobile health. DESIGN, SETTING, AND PARTICIPANTS: A multisite randomized clinical trial with site-level 1-to-1 allocation into 2 conditions was conducted in 4 US health care systems. A total of 433 individuals who were currently smoking and reported at enrollment that they were not ready to quit smoking were enrolled. The study was conducted from November 7, 2016, to July 31, 2020. INTERVENTIONS: Take a Break (TAB) was a 3-week game experience and included 5 behavioral components (motivational messaging, challenge quizzes, brief abstinence goal setting, mobile health apps for cravings management, and reward points for participation) integrated with NRT sampling. TAB draws on social cognitive theory and game mechanics concepts to engage participants in health behavior change. The comparison included NRT sampling only. MAIN OUTCOMES AND MEASURES: Time to first quit attempt (duration from TAB experience to primary outcome) and carbon monoxide level-verified smoking cessation at 6-month follow-up. All analyses used an intention-to-treat approach. RESULTS: Of the 433 individuals included in the trial, 223 were women (52%); mean (SD) age was 54 (13) years. More than half (53% [112 of 213]) of the TAB participants completed 100% of the daily challenge quizzes in the first week, 73% (145 of 199) of participants who completed the goal-setting call set a brief abstinence goal (most frequently 1-2 days of abstinence from cigarettes), and 75% (159 of 213) of participants used the mobile health apps to manage nicotine cravings. Time to the first quit attempt was lower for the TAB vs comparison group (hazard ratio, 1.68; 95% CI, 1.09-2.60; P = .02). At the 6-month follow-up, 18% (28 of 160) of TAB participants and 10% (17 of 171) of the comparison (χ2 test, P = .045) participants obtained carbon monoxide level-verified smoking cessation (accounting for clustering of outcomes by site; odds ratio, 1.92; 95% CI, 1.01-3.68; P = .048). CONCLUSIONS AND RELEVANCE: The findings of this randomized clinical trial demonstrate that individuals not yet ready to quit smoking could be engaged in a brief abstinence game. Six months later, the TAB group had nearly double the rate of smoking cessation vs the NRT sampling comparison group. Integrating a skills-building game experience with brief NRT sampling can enhance long-term cessation among those not yet ready to quit smoking. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02973425.

mHealth Messaging to Motivate Quitline Use and Quitting: Protocol for a Community-Based Randomized Controlled Trial in Rural Vietnam.

BACKGROUND: Tobacco kills more than 8 million people each year, mostly in low- and middle-income countries. In Vietnam, 1 in every 2 male adults smokes tobacco. Vietnam has set up telephone Quitline counseling that is available to all smokers, but it is underused. We previously developed an automated and effective motivational text messaging system to support smoking cessation among US smokers. OBJECTIVE: The aim of this study is to adapt the aforementioned system for rural Vietnamese smokers to promote cessation of tobacco use, both directly and by increasing the use of telephone Quitline counseling services and nicotine replacement therapy. Moreover, we seek to enhance research and health service capacity in Vietnam. METHODS: We are testing the effectiveness of our culturally adapted motivational text messaging system by using a community-based randomized controlled trial design (N=600). Participants were randomly allocated to the intervention (regular motivational and assessment text messages) or control condition (assessment text messages only) for a period of 6 months. Trial recruitment took place in four communes in the Hung Yen province in the Red River Delta region of Vietnam. Recruitment events were advertised to the local community, facilitated by community health workers, and occurred in the commune health center. We are assessing the impact of the texting system on 6-month self-reported and biochemically verified smoking cessation, as well as smoking self-efficacy, uptake of the Quitline, and use of nicotine replacement therapy. In addition to conducting the trial, the research team also provided ongoing training and consultation with the Quitline during the study period. RESULTS: Site preparation, staff training, intervention adaptation, participant recruitment, and baseline data collection were completed. The study was funded in August 2017; it was reviewed and approved by the University of Massachusetts Medical School Institutional Review Board in 2017. Recruitment began in November 2018. A total of 750 participants were recruited from four communes, and 700 (93.3%) participants completed follow-up by March 2021. An analysis of the trial results is in progress; results are expected to be published in late 2022. CONCLUSIONS: This study examines the effectiveness of mobile health interventions for smoking in rural areas in low- and middle-income countries, which can be implemented nationwide if proven effective. In addition, it also facilitates significant collaboration and capacity building among a variety of international partners, including researchers, policy makers, Quitline counselors, and community health workers. TRIAL REGISTRATION: ClinicalTrials.gov NCT03567993; https://clinicaltrials.gov/ct2/show/NCT03567993. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30947.

Facilitators and barriers to post-discharge pain assessment and triage: a qualitative study of nurses' and patients' perspectives.

BACKGROUND: After hospital discharge, patients can experience symptoms prompting them to seek acute medical attention. Early evaluation of patients' post-discharge symptoms by healthcare providers may improve appropriate healthcare utilization and patient safety. Post-discharge follow-up phone calls, which are used for routine transitional care in U.S. hospitals, serve as an important channel for provider-patient communication about symptoms. This study aimed to assess the facilitators and barriers to evaluating and triaging pain symptoms in cardiovascular patients through follow-up phone calls after their discharge from a large healthcare system in Central Massachusetts. We also discuss strategies that may help address the identified barriers. METHODS: Guided by the Practical, Robust, Implementation and Sustainability Model (PRISM), we completed semi-structured interviews with 7 nurses and 16 patients in 2020. Selected nurses conducted (or supervised) post-discharge follow-up calls on behalf of 5 clinical teams (2 primary care; 3 cardiology). We used thematic analysis to identify themes from interviews and mapped them to the domains of the PRISM model. RESULTS: Participants described common facilitators and barriers related to the four domains of PRISM: Intervention (I), Recipients (R), Implementation and Sustainability Infrastructure (ISI), and External Environment (EE). Facilitators include: (1) patients being willing to receive provider follow-up (R); (2) nurses experienced in symptom assessment (R); (3) good care coordination within individual clinical teams (R); (4) electronic health record system and call templates to support follow-up calls (ISI); and (5) national and institutional policies to support post-discharge follow-up (EE). Barriers include: (1) limitations of conducting symptom assessment by provider-initiated follow-up calls (I); (2) difficulty connecting patients and providers in a timely manner (R); (3) suboptimal coordination for transitional care among primary care and cardiology providers (R); and (4) lack of emphasis on post-discharge follow-up call reimbursement among cardiology clinics (EE). Specific barriers for pain assessment include: (1) concerns with pain medication misuse (R); and (2) no standardized pain assessment and triage protocol (ISI). CONCLUSIONS: Strategies to empower patients, facilitate timely patient-provider communication, and support care coordination regarding pain evaluation and treatment may reduce the barriers and improve processes and outcomes of pain assessment and triage.

Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: patterns and effects of user-system engagement.

BACKGROUND: Motivational messaging is a frequently used digital intervention to promote positive health behavior changes, including smoking cessation. Typically, motivational messaging systems have not actively sought feedback on each message, preventing a closer examination of the user-system engagement. This study assessed the granular user-system engagement around a recommender system (a new system that actively sought user feedback on each message to improve message selection) for promoting smoking cessation and the impact of engagement on cessation outcome. METHODS: We prospectively followed a cohort of current smokers enrolled to use the recommender system for 6 months. The system sent participants motivational messages to support smoking cessation every 3 days and used machine learning to incorporate user feedback (i.e., user's rating on the perceived influence of each message, collected on a 5-point Likert scale with 1 indicating strong disagreement and 5 indicating strong agreement on perceiving the influence on quitting smoking) to improve the selection of the following message. We assessed user-system engagement by various metrics, including user response rate (i.e., the percent of times a user rated the messages) and the perceived influence of messages. We compared retention rates across different levels of user-system engagement and assessed the association between engagement and the 7-day point prevalence abstinence (missing outcome = smoking) by using multiple logistic regression. RESULTS: We analyzed data from 731 participants (13% Black; 73% women). The user response rate was 0.24 (SD = 0.34) and user-perceived influence was 3.76 (SD = 0.84). The retention rate positively increased with the user response rate (trend test P < 0.001). Compared with non-response, six-month cessation increased with the levels of response rates: low response rate (odds ratio [OR] = 1.86, 95% confidence interval [CI]: 1.07-3.23), moderate response rate (OR = 2.30, 95% CI: 1.36-3.88), high response rate (OR = 2.69, 95% CI: 1.58-4.58). The association between perceived message influence and the outcome showed a similar pattern. CONCLUSIONS: High user-system engagement was positively associated with both high retention rate and smoking cessation, suggesting that investigation of methods to increase engagement may be crucial to increase the impact of the recommender system for smoking cessation. TRIAL REGISTRATION: Registration Identifier: NCT03224520 . Registration date: July 21, 2017.

Non-specific pain and 30-day readmission in acute coronary syndromes: findings from the TRACE-CORE prospective cohort.

BACKGROUND: Patients with acute coronary syndromes often experience non-specific (generic) pain after hospital discharge. However, evidence about the association between post-discharge non-specific pain and rehospitalization remains limited. METHODS: We analyzed data from the Transitions, Risks, and Actions in Coronary Events Center for Outcomes Research and Education (TRACE-CORE) prospective cohort. TRACE-CORE followed patients with acute coronary syndromes for 24 months post-discharge from the index hospitalization, collected patient-reported generic pain (using SF-36) and chest pain (using the Seattle Angina Questionnaire) and rehospitalization events. We assessed the association between generic pain and 30-day rehospitalization using multivariable logistic regression (N = 787). We also examined the associations among patient-reported pain, pain documentation identified by natural language processing (NLP) from electronic health record (EHR) notes, and the outcome. RESULTS: Patients were 62 years old (SD = 11.4), with 5.1% Black or Hispanic individuals and 29.9% women. Within 30 days post-discharge, 87 (11.1%) patients were re-hospitalized. Patient-reported mild-to-moderate pain, without EHR documentation, was associated with 30-day rehospitalization (odds ratio [OR]: 2.03, 95% confidence interval [CI]: 1.14-3.62, reference: no pain) after adjusting for baseline characteristics; while patient-reported mild-to-moderate pain with EHR documentation (presumably addressed) was not (OR: 1.23, 95% CI: 0.52-2.90). Severe pain was also associated with 30-day rehospitalization (OR: 3.16, 95% CI: 1.32-7.54), even after further adjusting for chest pain (OR: 2.59, 95% CI: 1.06-6.35). CONCLUSIONS: Patient-reported post-discharge generic pain was positively associated with 30-day rehospitalization. Future studies should further disentangle the impact of cardiac and non-cardiac pain on rehospitalization and develop strategies to support the timely management of post-discharge pain by healthcare providers.

Health-Related Goal Setting and Achievement Among Veterans with High Technology Adoption.

BACKGROUND: There is increasing recognition of the importance of supporting patients in their health-related goals. Patient-provider discussions and health-related mobile applications (apps) can support patients to pursue health goals; however, their impact on patient goal setting and achievement is not well understood. OBJECTIVE: To examine the relationships between the following: (1) patient demographics, patient-provider discussions, and health-related goal setting and achievement, and (2) patient mobile health app use and goal achievement. DESIGN: Cross-sectional survey. PARTICIPANTS: Veterans who receive Veterans Health Administration (VA) healthcare and are users of VA patient-facing technology. MAIN MEASURES: Veteran demographics, goal-related behaviors, and goal achievement. METHODS: Veterans were invited to participate in a telephone survey. VA administrative data were linked to survey data for additional health and demographic information. Logistic regression models were run to identify factors that predict health-related goal setting and achievement. KEY RESULTS: Among respondents (n=2552), 75% of patients indicated having set health goals in the preceding 6 months and approximately 42% reported achieving their goal. Men (vs. women) had lower odds of setting goals (OR: 0.71; CI95: 0.53-0.97), as did individuals with worse (vs. better) health (OR: 0.18; CI95: 0.04-0.88). Individuals with advanced education-some college/college degrees, and post-college degrees (vs. no college education)-demonstrated higher odds of setting goals (OR: 1.35; CI95: 1.01-1.79; OR: 1.71; CI95: 1.28-2.28, respectively). Those who reported having discussed their goals with their providers were more likely to set goals (OR: 3.60; CI95: 2.97-4.35). Patient mobile health app use was not statistically associated with goal achievement. CONCLUSIONS: Efforts to further promote patient-led goal setting should leverage the influence of patient-provider conversations. Use of patient-facing technologies, specifically mobile health apps, may facilitate goal-oriented care, but further work is needed to examine the potential benefits of apps to support patient goals, particularly if providers discuss and endorse use of those apps with patients.

Written Advice Given by African American Smokers to Their Peers: Qualitative Study of Motivational Messages.

BACKGROUND: Although African Americans have the lowest rates of smoking onset and progression to daily smoking, they are less likely to achieve long-term cessation. Interventions tailored to promote use of cessation resources in African American individuals who smoke are needed. In our past work, we demonstrated the effectiveness of a technology-assisted peer-written message intervention for increasing smoking cessation in non-Hispanic White smokers. In this formative study, we have adapted this intervention to be specific for African American smokers. OBJECTIVE: We aimed to report on the qualitative analysis of messages written by African American current and former smokers for their peers in response to hypothetical scenarios of smokers facing cessation challenges. METHODS: We recruited African American adult current and former smokers (n=41) via ResearchMatch between April 2017 and November 2017. We asked participants to write motivational messages for their peers in response to smoking-related hypothetical scenarios. We also collected data on sociodemographic factors and smoking characteristics. Thematic analysis was conducted to identify cessation strategies suggested by the study participants. RESULTS: Among the study participants, 60% (25/41) were female. Additionally, more than half (23/41, 56%) were thinking about quitting, 29% (12/41) had set a quit date, and 27% (11/41) had used electronic cigarettes in the past 30 days. Themes derived from the qualitative analysis of peer-written messages were (1) behavioral strategies, (2) seeking help, (3) improvements in quality of life, (4) attitudes and expectations, and (5) mindfulness/religious or spiritual practices. Under the behavioral strategies theme, distraction strategies were the most frequently suggested strategies (referenced 84 times in the 318 messages), followed by use of evidence-based treatments/cessation strategies. Within the seeking help theme, subthemes included seeking help or support from family/friends or close social networks (referenced 56 times) and health care professionals (referenced 22 times). The most frequent subthemes that emerged from improvements in the quality of life theme included improving one's health (referenced 22 times) and quality of life (referenced 21 times). Subthemes that emerged from the attitude and expectations theme included practicing positive self-talk (referenced 27 times), autonomy/independence from the smoking habit (referenced six times), and financial cost of smoking (referenced five times). The two subthemes that emerged from the mindfulness/religious or spiritual practices theme were use of self-awareness techniques (referenced 36 times) and religious or spiritual practices to cope (referenced 13 times). CONCLUSIONS: Our approach to adapt a prior peer-message intervention to African American smokers yielded a set of evidence-based messages that may be suitable for smokers at all phases of motivation to quit (ready to quit or not ready to quit). In future research, we plan to assess the impact of texting these messages to African American smokers in a smoking cessation trial.

Comparing recruitment strategies for a digital smoking cessation intervention: Technology-assisted peer recruitment, social media, ResearchMatch, and smokefree.gov.

BACKGROUND: Choosing the right recruitment strategy has implications for the successful conduct of a trial. Our objective was to compare a novel peer recruitment strategy to four other recruitment strategies for a large randomized trial testing a digital tobacco intervention. METHODS: We compared enrollment rates, demographic and baseline smoking characteristics, and odds of completing the 6-month study by recruitment strategy. Cost of recruitment strategies per retained participant was calculated using staff personnel time and advertisement costs. FINDINGS: We enrolled 1487 participants between August 2017 and March 2019 from: Peer recruitment n = 273 (18.4%), Facebook Ads n = 505 (34%), Google Ads = 200 (13.4%), ResearchMatch n = 356 (23.9%) and Smokefree.govn = 153 (10.3%). Mean enrollment rate per active recruitment month: 1) Peer recruitment, n = 13.9, 2) Facebook ads, n = 25.3, 3) Google ads, n = 10.51, 4) Research Match, n = 59.3, and 5) Smokefree.gov, n = 13.9. Peer recruitment recruited the greatest number of males (n = 110, 40.3%), young adults (n = 41, 14.7%), participants with a high school degree or less (n = 24, 12.5%) and smokers within one's social network. Compared to peer recruitment (retention rate = 57%), participants from Facebook were less likely (OR 0.46, p < 0.01, retention rate = 40%), and those from ResearchMatch were more likely to complete the study (OR 1.90, p < 0.01, retention rate = 70%). Peer recruitment was moderate in cost per retained participant ($47.18) and substantially less costly than Facebook ($173.60). CONCLUSIONS: Though peer recruitment had lower enrollment than other strategies, it may provide greater access to harder to reach populations and possibly others who smoke within one's social network while being moderately cost-effective. ClinicalTrials.gov: NCT03224520.

Merging Implementation Practice and Science to Scale Up Promising Practices: The Veterans Health Administration (VHA) Diffusion of Excellence (DoE) Program.

INTRODUCTION: The Veterans Health Administration (VHA) Diffusion of Excellence (DoE) program developed and manages a framework for identification, replication, and diffusion of promising practices throughout the nation's largest integrated health care system. DoE identifies promising practices through a "Shark Tank" competition with winning bidders receiving external implementation facilitation. DoE further supports diffusion of successful practices across the VHA. METHODS: This article presents results of a mixed methods implementation evaluation of DoE, focusing on program reach, program participation and decisions to adopt innovative practices, implementation processes, and practice sustainment. Data sources include practice adoption metrics, focus groups with bidders (two focus groups), observations of DoE events (seven events), surveys of stakeholders (five separate surveys), and semistructured interviews of facility directors, practice developers, implementation teams, and facilitators (133 participants). RESULTS: In the first four Shark Tank cohorts (2016-2018), 1,676 practices were submitted; 47 were designated Gold Status Practices (practices with facilitated implementation). Motivation for participation varied. Generally, staff led projects targeting problems they felt passionate about, facility directors focused on big-picture quality metrics and getting middle manager support, and frontline staff displayed variable motivation to implement new projects. Approximately half of facilitated implementation efforts were successful; barriers included insufficient infrastructure, staff, and resources. At the facility level, 73.3% of facilities originating or receiving facilitated implementation support have maintained the practice. VHA-wide, 834 decisions to adopt these practices were made. CONCLUSION: DoE has resulted in the identification of many candidate practices, promoted adoption of promising practices by facility directors, and supported practice implementation and diffusion across the VHA.

Impact of Patient Access to Online VA Notes on Healthcare Utilization and Clinician Documentation: a Retrospective Cohort Study.

BACKGROUND: In an effort to foster patient engagement, some healthcare systems provide their patients with open notes, enabling them to access their clinical notes online. In January 2013, the Veterans Health Administration (VA) implemented online access to clinical notes ("VA Notes") through the Blue Button feature of its patient portal. OBJECTIVE: To measure the association of online patient access to clinical notes with changes in healthcare utilization and clinician documentation behaviors. DESIGN: A retrospective cohort study. PATIENTS: Patients accessing My HealtheVet (MHV), the VA's online patient portal, between July 2011 and January 2015. MAIN MEASURES: Use of healthcare services (primary care clinic visits and online electronic secure messaging), and characteristics of physician clinical documentation (readability of notes). KEY RESULTS: Among 882,575 unique portal users, those who accessed clinical notes (16.2%; N = 122,972) were younger, more racially homogenous (white), and less likely to be financially vulnerable. Compared with non-users, Notes users more frequently used the secure messaging feature on the portal (mean of 2.6 messages (SD 7.0) v. 0.87 messages (SD 3.3) in January-July 2013), but their higher use of secure messaging began prior to VA Notes implementation, and thus was not temporally related to the implementation. When comparing clinic visit rates pre- and post-implementation, Notes users had a small but significant increase in rate of 0.36 primary care clinic visits (2012 v. 2013) compared to portal users who did not view their Notes (p = 0.01). At baseline, the mean reading ease of primary care clinical notes was 53.8 (SD 10.1) and did not improve after implementation of VA Notes. CONCLUSIONS: VA Notes users were different than patients with portal access who did not view their notes online, and they had higher rates of healthcare service use prior to and after VA Notes implementation. Opportunities exist to improve clinical note access and readability.

Herbal or Dietary Supplement Use and Hypertensive Medications: Does the Combination Relate to Medication Adherence and Blood Pressure Control?

Introduction: Medication nonadherence contributes to poor blood pressure control and increases cardiovascular disease risk in patients with hypertension. Identifying modifiable risk factors for low or nonadherence to antihypertensive medication is needed. Studies that have examined the relationship between herbal or dietary supplement (HDS) use and antihypertensive medication nonadherence provide inconsistent findings. Data from the National Health and Nutrition Examination Survey were used to examine the association between HDS use, current use of prescribed antihypertensive medication, and blood pressure status. Methods: The study sample included hypertensive adults (n = 5,478) who have been told by a health professional to take prescribed medication for their health condition. Respondents were classified as either HDS users or HDS nonusers. Depending on the kind of supplement used, HDS users were further divided into three mutually exclusive groups: hypertension HDS users, nonhypertension HDS users, and those who used both kinds of supplements. Supplements groupings were based on reports in the literature. Blood pressure status definition was based on Eighth Joint National Committee (JNC 8) recommendations. Adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs) were obtained from logistic models. Results: Overall HDS use prevalence was 62%. Current medication use did not significantly differ between patients reporting no HDS use and those reporting HDS use (aOR: 1.45; 95% CI: 0.78-2.69). No observable differences were found in current medication use between patients reporting no HDS use and those taking nonhypertension HDS or both kinds of supplements. Use of hypertension HDS was associated with improved blood pressure (aOR: 1.90; 95% CI: 1.07-3.36) compared with no HDS use. Conclusion: In a nationally representative U.S. sample, HDS use did not compromise current use of prescribed medication and was associated with improved blood pressure control. More research is needed to better understand why HDS use is associated with better blood pressure control.

Impact of Patient-Clinical Team Secure Messaging on Communication Patterns and Patient Experience: Randomized Encouragement Design Trial.

BACKGROUND: Although secure messaging (SM) between patients and clinical team members is a recommended component of continuous care, uptake by patients remains relatively low. We designed a multicomponent Supported Adoption Program (SAP) to increase SM adoption among patients using the Veterans Health Administration (VHA) for primary care. OBJECTIVE: Our goals were to (1) conduct a multisite, randomized, encouragement design trial to test the effectiveness of an SAP designed to increase patient engagement with SM through VHA's online patient portal (My HealtheVet [MHV]) and (2) evaluate the impact of the SAP and patient-level SM adoption on perceived provider autonomy support and communication. Patient-reported barriers to SM adoption were also assessed. METHODS: We randomized 1195 patients at 3 VHA facilities who had MHV portal accounts but had never used SM. Half were randomized to receive the SAP, and half served as controls receiving usual care. The SAP consisted of encouragement to adopt SM via mailed educational materials, proactive SM sent to patients, and telephone-based motivational interviews. We examined differences in SM adoption rates between SAP recipients and controls at 9 months and 21 months. Follow-up telephone surveys were conducted to assess perceived provider autonomy support and self-report of telephone communication with clinical teams. RESULTS: Patients randomized to the SAP had significantly higher rates of SM adoption than the control group (101/595, 17.0% vs 40/600, 6.7%; P<.001). Most adopters in the SAP sent their first message without a motivational interview (71/101, 70.3%). The 10-percentage point difference in adoption persisted a full year after the encouragement ended (23.7%, 142/600 in the SAP group vs 13.5%, 80/595 in the control group, P<.001). We obtained follow-up survey data from 49.54% (592/1195) of the participants. SAP participants reported higher perceived provider autonomy support (5.7 vs 5.4, P=.007) and less telephone use to communicate with their provider (68.8% vs 76.0%, P=.05), compared to patients in the control group. Patient-reported barriers to SM adoption included self-efficacy (eg, not comfortable using a computer, 24%), no perceived need for SM (22%), and difficulties with portal password or login (17%). CONCLUSIONS: The multicomponent SAP was successful in increasing use of SM 10 percentage points above standard care; new SM adopters reported improved perceptions of provider autonomy support and less use of the telephone to communicate with their providers. Still, despite the encouragement and technical assistance provided through the SAP, adoption rates were lower than anticipated, reaching only 24% at 21 months (10% above controls). Common barriers to adoption such as limited perceived need for SM may be more challenging to address and require different interventions than barriers related to patient self-efficacy or technical difficulties. TRIAL REGISTRATION: ClinicalTrials.gov NCT02665468; https://clinicaltrials.gov/ct2/show/NCT02665468.

Supporting the Implementation of Connected Care Technologies in the Veterans Health Administration: Cross-Sectional Survey Findings from the Veterans Engagement with Technology Collaborative (VET-C) Cohort.

BACKGROUND: Widespread adoption, use, and integration of patient-facing technologies into the workflow of health care systems has been slow, thus limiting the realization of their potential. A growing body of work has focused on how best to promote adoption and use of these technologies and measure their impacts on processes of care and outcomes. This body of work currently suffers from limitations (eg, cross-sectional analyses, limited patient-generated data linked with clinical records) and would benefit from institutional infrastructure to enhance available data and integrate the voice of the patient into implementation and evaluation efforts. OBJECTIVE: The Veterans Health Administration (VHA) has launched an initiative called the Veterans Engagement with Technology Collaborative cohort to directly address these challenges. This paper reports the process by which the cohort was developed and describes the baseline data being collected from cohort members. The overarching goal of the Veterans Engagement with Technology Collaborative cohort is to directly engage veterans in the evaluation of new VHA patient-facing technologies and in so doing, to create new infrastructure to support related quality improvement and evaluation activities. METHODS: Inclusion criteria for veterans to be eligible for membership in the cohort included being an active user of VHA health care services, having a mobile phone, and being an established user of existing VHA patient-facing technologies as represented by use of the secure messaging feature of VHA's patient portal. Between 2017 and 2018, we recruited veterans who met these criteria and administered a survey to them over the telephone. RESULTS: The majority of participants (N=2727) were male (2268/2727, 83.2%), White (2226/2727, 81.6%), living in their own apartment or house (2519/2696, 93.4%), and had completed some college (1176/2701, 43.5%) or an advanced degree (1178/2701, 43.6%). Cohort members were 59.9 years old, on average. The majority self-reported their health status as being good (1055/2725, 38.7%) or very good (524/2725, 19.2%). Most cohort members owned a personal computer (2609/2725, 95.7%), tablet computer (1616/2716, 59.5%), and/or smartphone (2438/2722, 89.6%). CONCLUSIONS: The Veterans Engagement with Technology Collaborative cohort is an example of a VHA learning health care system initiative designed to support the data-driven implementation of patient-facing technologies into practice and measurement of their impacts. With this initiative, VHA is building capacity for future, rapid, rigorous evaluation and quality improvement efforts to enhance understanding of the adoption, use, and impact of patient-facing technologies.