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Background A Risk Evaluation and Mitigation Strategy (REMS) is a drug safety program for certain medications with serious safety concerns required by the U.S. Food and Drug Administration (FDA) of manufacturers to implement to help ensure the benefits of the medication outweigh its risks. FDA is encouraging “the research community to develop novel methods for assessing REMS,” conveying the unmet need for a standardized evaluation method of these regulatory-mandated healthcare programs. The objective of this research is to evaluate FDA REMS assessment plans using established implementation science frameworks and identify opportunities for strengthening REMS evaluation. Methods A content analysis was conducted of publicly available assessment plans for all REMS programs ( N  = 23) approved 1/1/2014–12/31/2018 for new drug applications (NDAs) and biologics license applications (BLAs) requiring FDA-mandated Elements to Assure Safe Use (ETASU). Blinded reviewers critically appraised REMS assessment measures ( n  = 674) using three established implementation science frameworks: RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance); PRECEDE-PROCEED (Predisposing, Reinforcing, and Enabling Constructs in Educational/Environmental Diagnosis and Evaluation – Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development); and CFIR (Consolidated Framework for Implementation Research). Framework constructs were mapped to REMS Assessment categories as defined by FDA Guidance for Industry to evaluate congruence. Results REMS assessment measures demonstrated strong congruence (> 90% mapping rate) with the evaluative constructs of RE-AIM, PRECEDE-PROCEED, and CFIR. Application of the frameworks revealed that REMS assessment measures heavily emphasize implementation and operations, focus less on health outcomes, and do not evaluate program context and design assumptions. Conclusions Implementation science frameworks have utility for evaluating FDA-mandated drug safety programs including the selection of primary measures to determine whether REMS goals are being met and of secondary measures to evaluate contextual factors affecting REMS effectiveness in varying organizational settings.

OBJECTIVE:--Given the risk of obesity and diabetes in the U.S., and clear benefit of exercise in disease prevention and management, this study aimed to determine the prevalence of physical activity among adults with and at risk for diabetes. RESEARCH DESIGN AND METHODS--The Medical Expenditure Panel Survey is a nationally representative survey of the U.S. population. In the 2003 survey, 23,283 adults responded when asked about whether they were physically active (moderate or vigorous activity, >=30 min, three times per week). Information on sociodemographic characteristics and health conditions were self-reported. Additional type 2 diabetes risk factors examined were age >=45 years, non-Caucasian ethnicity, BMI >=25 kg/m², hypertension, and cardiovascular disease. RESULTS:--A total of 39% of adults with diabetes were physically active versus 58% of adults without diabetes. The proportion of active adults without diabetes declined as the number of risk factors increased until dropping to similar rates as people with diabetes. After adjustment for sociodemographic and clinical factors, the strongest correlates of being physically active were income level, limitations in physical function, depression, and severe obesity (BMI >=40 kg/m²). Several traditional predictors of activity (sex, education level, and having received past advice from a health professional to exercise more) were not evident among respondents with diabetes. CONCLUSIONS:--The majority of patients with diabetes or at highest risk for developing type 2 diabetes do not engage in regular physical activity, with a rate significantly below national norms. There is a great need for efforts to target interventions to increase physical activity in these individuals.

Clinical and Translational Science Award (CTSA) hubs must advance implementation science via innovative approaches to understand and develop strategies for overcoming barriers to the adoption, adaptation, integration, scale-up, and sustainability of evidence-based interventions, tools, policies, and guidelines. This special communication describes adaption of the I-Corps™@NCATS training program, a Lean Start-Up approach developed to advance commercialization of academic innovation, as a mechanism for building implementation science capacity at the Institute for Translational Medicine, a Chicago-based multi-institutional CTSA hub. Results from seven training cohorts (2021–2025) are presented (43 teams, 157 participants). In this five-week experiential program, teams conducted “customer discovery” interviews with stakeholders (mean = 23.8/team, SD = 5.6) to rapidly assess fit-to-context of their innovation and adoption requirements. Likelihood of recommending the program to a colleague was high (8.9, SD = 1.5; 1–10 scale, where “10” = “extremely likely”). Important adaptations were providing non-commercial use cases; defining “customers” in terms of stakeholders and partners; reframing commercial business model goals in terms of designing-for-dissemination-and-sustainability; and showing how the value proposition hypothesis is analogous to a research hypothesis being tested and validated with “customer discovery” data. Findings support that the modified I-Corps@NCATS training program provides flexible translational science skill-building to advance implementation science capacity among clinical and translational researchers.

OBJECTIVE: Several second-generation antipsychotic (SGA) drugs have been associated with weight gain, hyperglycemia, and dyslipidemia. We evaluated whether glucose and lipid testing increased after the American Diabetes Association (ADA) consensus statement recommending metabolic monitoring for SGA-treated patients. RESEARCH DESIGN AND METHODS: Laboratory claims for serum glucose and lipid testing were identified for an incident cohort of 18,876 adults initiating SGA drugs in a U.S. commercial health plan (2001-2006) and a control group of 56,522 adults with diabetes not receiving antipsychotics. Interrupted time-series models were used to estimate the effect of ADA recommendations on baseline and annual testing trends after adjusting for differences in age, sex, mental health diagnoses, and cardiovascular risk using propensity score matching. RESULTS: Mean baseline testing rates for SGA-treated patients during the study period were 23% (glucose) and 8% (lipids). Among persistent users of SGA medication, annual testing rates were 38% (glucose) and 23% (lipid). Before the ADA statement, screening rates for SGA-treated patients were increasing (glucose: baseline 3.6% per year, annual 7.2% per year; lipid: baseline 1.2% per year, annual 4.8% per year; P < 0.001 for each trend). Increases were similar to background testing trends in control subjects. The ADA statement was not associated with an increase in screening rates. CONCLUSIONS: In a commercially insured population, glucose and lipid testing for SGA-treated adults was infrequent. A gradual increase in screening rates occurred over the 6-year period, but the changes were not temporally associated with the ADA statement. More effort is needed to improve diabetes and dyslipidemia screening in these at-risk patients.

OBJECTIVE:--Obesity and physical inactivity are established risk factors for type 2 diabetes and cardiovascular comorbidities. Whether adiposity or fitness level is more important to health is controversial. The objective of this research is to determine the relative associations of physical activity and BMI with the prevalence of diabetes and diabetes-related cardiovascular comorbidities in the U.S. RESEARCH DESIGN AND METHODS--The Medical Expenditure Panel Survey (MEPS) is a nationally representative survey of the U.S. population. From 2000 to 2002, detailed information on sociodemographic characteristics and health conditions were collected for 68,500 adults. Normal weight was defined as BMI 18.5 to <25 kg/m², overweight 25 to /=]40 kg/m². Physical activity was defined as moderate/vigorous activity [>/=]30 min [>/=]3 days per week. RESULTS:--The likelihood of having diabetes and diabetes-related cardiovascular comorbidities increased with BMI regardless of physical activity and increased with physical inactivity regardless of BMI. Compared with normal-weight active adults, the multivariate-adjusted odds ratio (OR) for diabetes was 1.52 (95% CI 1.25-1.86) for normal-weight inactive adults and 1.65 (1.40-1.96) for overweight inactive adults; the OR for diabetes and comorbid hypertension was 1.71 (1.32-2.19) for normal-weight inactive adults and 1.84 (1.47-2.32) for overweight inactive adults. CONCLUSIONS:--Both physical inactivity and obesity seem to be strongly and independently associated with diabetes and diabetes-related comorbidities. These results support continued research investigating the independent causal nature of these factors.

A significant health challenge is evident in the United States, with 6 in 10 adults having a chronic disease and 4 in 10 adults having 2 or more. Chronic disease self-management aims to prevent or delay disease progression and disability and reduce mortality risk. The evidence to support the use of information technology tools, including mobile apps, web-based portals, and web-based educational interventions, that support disease self-management and improve clinical outcomes is growing. Customer discovery and value proposition design methodology is a form of stakeholder engagement and is based on marketing and lean start-up business methods. As applied in health care, customer discovery and value proposition methodology can be used to understand the clinical problem and articulate the product's hypothesized unique value proposition relative to alternative options that are available to end users. This study aims to describe the experience and findings of academic researchers applying the customer discovery and value proposition methodology to identify stakeholders, needs, adaptability, and sustainability of a chronic disease self-management mobile app (CDapp). The motivation of the work is to make mobile health app interventions accessible and acceptable for all segments of patients' chronic diseases. Data were obtained through key informant interviews and analyzed using rapid qualitative analysis techniques. The value proposition framework was used to build the interview guide. The aim was to identify the needs, challenges (pains), and potential benefits (gains) of the CDapp for our stakeholders. Our results showed that the primary consumers (end users) of a CDapp were the patients. The app adopters (decision makers) can be medical center leaders including population health department managers or insurance providers, while the consumer adoption influencers (influencers or saboteurs) are clinicians and patient caregivers. We developed an ecosystem map to visualize the clinical practice workflow and how an app for chronic disease management might integrate within an academic health care center or system. A value proposition for the identified customer segments was generated. Each stakeholder segment was working within a different framework to improve patient self-management. Patients needed help to adhere to self-care activities and they needed tailored health education. Health care leaders aim to improve the quality of care while reducing costs and workload. Clinicians wanted to improve patient education and care while reducing the time burden. Our results also showed that within academic medical centers, there were variations regarding patients' self-reported abilities to manage their diseases. Customer discovery is a useful form of stakeholder engagement when designing studies that seek to implement, adapt, and sustain an intervention. The customer discovery and value proposition methodology can be used as an alternative or complementary approach to formative research to generate valuable information in a brief period.

The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE). We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment; and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus group. Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources; (2) Team science is key for success; (3) Quality of data generates trust in the network; (4) Capacity building is defined differently by researcher career stage and CTSA existing resources; and (5) Researchers' unmet needs. Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for Data Science.

The ACT Network was funded by NIH to provide investigators from across the Clinical and Translational Science Award (CTSA) Consortium the ability to directly query national federated electronic health record (EHR) data for cohort discovery and feasibility assessment of multi-site studies. NIH refunded the program for expanded research application to become “Evolve to Next-Gen ACT” (ENACT). In parallel, the US Food and Drug Administration has been evaluating the use of real-world data (RWD), including EHR data, as sources of real-world evidence (RWE) for its regulatory decisions involving drug and biological products. Using insights from implementation science, six lessons learned from ACT for developing and sustaining RWD/RWE infrastructures and networks across the CTSA Consortium are presented in order to inform ENACT’s development from the outset. Lessons include intentional institutional relationship management, end-user engagement, beta-testing, and customer-driven adaptation. The ENACT team is also conducting customer discovery interviews with CTSA hub and investigators using Innovation-Corps@NCATS (I-Corps™) methodology for biomedical entrepreneurs to uncover unmet RWD needs. Possible ENACT value proposition hypotheses are presented by stage of research. Developing evidence about methods for sustaining academically derived data infrastructures and support can advance the science of translation and support our nation’s RWD/RWE research capacity.

A key barrier to translation of biomedical research discoveries is a lack of understanding among scientists regarding the complexity and process of implementation. To address this challenge, the National Science Foundation's Innovation Corps™ (I-Corps™) program trains researchers in entrepreneurship. We report results from the implementation of an I-Corps™ training program aimed at biomedical scientists from institutions funded by the National Center for Advancing Translational Sciences (NCATS). National/regional instructors delivered 5-week I-Corps@NCATS short courses to 62 teams (150 individuals) across six institutions. Content included customer discovery, value proposition, and validating needs. Teams interviewed real-life customers and presented the value of innovations for specific end-users weekly, culminating in a \"Finale\" featuring their refined business thesis and business model canvas. Methodology was developed to evaluate the newly adapted program. National mixed-methods evaluation assessed program implementation, reach, effectiveness using observations of training delivery and surveys at Finale ( = 55 teams), and 3-12 months post-training ( = 34 teams). Innovations related to medical devices (33%), drugs/biologics (20%), software applications (16%), and diagnostics (8%). An average of 24 interviews was conducted. Teams reported increased readiness for commercialization over time (83%, 9 months; 14%, 3 months). Thirty-nine percent met with institutional technology transfer to pursue licensing/patents and 24% pursued venture capital/investor funding following the short courses. I-Corps@NCATS training provided the NCATS teams a rigorous and repeatable process to aid development of a business model based on customer needs. Outcomes of this pilot program support the expansion of I-Corps™ training to biomedical scientists for accelerating research translation.

Background: Although growing concern has been expressed about premature medical mortality in persons with mental illness, limited data are available quantifying the extent and correlates of this problem using population-based, nationally representative samples. Methods: The study used data from the 1989 National Health Interview Survey mental health supplement, with mortality data through 2006 linked through the National Death Index (80,850 participants, 16,435 deaths). Multivariable models adjusting for demographic factors assessed the increased hazard of mortality adding socioeconomic status, healthcare variables, clinical factors first separately, and then together. Results: Persons with mental disorders died an average of 8.2 years younger than the rest of the population (P < 0.001). Adjusting for demographic factors, presence of a mental illness was associated with a significant risk of excess mortality, (hazard ratio = 2.06, 95% confidence interval = 1.71-2.40), with 95.4% of deaths owing to medical rather than unnatural causes. Adding socioeconomic variables to the model, the hazard ratio was 1.77 (P < 0.001); adding health system factors, it was 1.80 (P < 0.001)); adding baseline clinical characteristics, the hazard ratio was 1.32 (P < 0.001). After adding all the 3 groups of variables simultaneously, the association was reduced by 82% from baseline and became statistically nonsignificant (hazard ratio = 1.19, P = 0.053). Conclusions: The results of the study underscore the complex causes and high burden of medical mortality among persons with mental disorders in the United States. Efforts to address this public health problem will need to address the socioeconomic, healthcare, and clinical risk factors that underlie it.