OBJECTIVES:To identify barriers and facilitators to implementing an electronic shared decision-making tool for managing anticoagulant-related drug-drug interactions that affect bleeding risk in routine clinical care. DESIGN:Preimplementation qualitative study using semistructured interviews. SETTING:Three academic medical centres in the southeastern and western USA. Interviews were conducted between 27 March and 25 September 2024. PARTICIPANTS:36 participants, including 19 clinicians involved in prescribing or managing anticoagulants and seventeen patients prescribed anticoagulants, were recruited using purposive and convenience sampling. RESULTS:Participants identified multiple barriers and facilitators to tool implementation. Common barriers included limited visit time, challenges integrating the tool into existing workflows, role and scope-of-practice constraints, and variation in patient digital literacy. Facilitators included clear visualisation of bleeding risk, access to supporting evidence, familiar interface design and perceived potential to support patient engagement and shared decision-making. Several determinants functioned as both barriers and facilitators, depending on clinical context and user role. CONCLUSIONS:This preimplementation qualitative study identified context-specific determinants that influence the adoption of an electronic shared decision-making tool for anticoagulant-related drug-drug interactions. Findings highlight the importance of early attention to workflow integration, role alignment and usability to support uptake in routine care. Addressing these factors during design and implementation may inform strategies to support adoption and future evaluation in real-world clinical settings.

BACKGROUND:Use of an implementation science (IS) theory, model, or framework (TMF) is one of the hallmarks of a well-executed IS study. Although TMFs are frequently used in IS studies, the TMFs themselves are seldom evaluated. Understanding the relationships between the constructs within an IS TMF and their effect on the implementation and effectiveness outcomes can help to refine the TMF, advance IS and assist implementers. METHODS:We evaluated several hypotheses pertinent to the context domains and Reach, Effectiveness Adoption, Implementation, and Maintenance (RE-AIM) outcomes from the Practical, Robust Implementation and Sustainability Framework (PRISM). Data for these evaluations emerged from the use of the Iterative PRISM (iPRISM) webtool, which includes 21 assessment questions that operationalize PRISM context and outcomes constructs. We tested 11 'a priori' hypotheses including relationships within and between different framework constructs and considered whether some of the webtool's assessment questions could be refined or changed to make the assessment more pragmatic and helpful. RESULTS:A total of 348 clinical, community, and public health respondents completed the iPRISM survey using the publicly available webtool. They reported on projects from a wide variety of clinical, community, and public health settings; in English and Spanish; and in different project phases. Seven of the 11 hypotheses were fully or partially supported (e.g. that ratings for Maintenance would be lower than other RE-AIM outcomes). One exception was that the hypothesis that the correlation between Reach and Effectiveness would be the lowest among RE-AIM outcomes was not supported. As hypothesized, scores on the equity items on the various RE-AIM dimensions (e.g. Reach) were consistently lower than general ratings for that dimension. Fewer of the hypotheses about the PRISM context items were supported, possibly due to there being only one item per contextual domain. CONCLUSIONS:The webtool questions provide a standardized way to operationalize PRISM constructs and initial norms for different items. Future research, including qualitative evaluation, is needed to replicate, explore, and understand the complex relationships found within RE-AIM outcomes, within PRISM context domains, and between PRISM context ratings and RE-AIM outcomes.

BackgroundTranslating evidence-based therapies from “bench to bedside” remains challenging, and implementation science (IS) experts are crucial for this process. Qualitative analyses are essential, but require extensive time and cost for manual coding. Now, many turn to artificial intelligence (AI) to accelerate the pace of qualitative analysis, but significant questions remain about the quality, validity, and ethics of applying large language models like ChatGPT (OpenAI) to qualitative data. To this end, we have developed a method for AI-assisted rapid qualitative analysis that addresses these concerns. ObjectiveThis study aimed to develop AI-assisted rapid qualitative analysis for implementation science as an open-source encoder-based small language model (SLM) to aid IS experts. We focus on 2 efficient and high-performing SLMs: distilled bidirectional encoder representations from transformers (DistilBERT) and efficiently learning an encoder that classifies token replacements accurately (ELECTRA). The objective is to assess these models’ accuracy in reproducing expert coding, their generalizability to new coding scenarios, and enhancing their accessibility for nontechnical experts through user-friendly tools. MethodsTwo previously coded IS datasets were used to train DistilBERT and ELECTRA models. These datasets were coded by IS experts using a mixed deductive and inductive approach, with initial categories derived from the domains of an IS framework: Practical, Robust Implementation, and Sustainability Model. We fine-tuned and evaluated DistilBERT and ELECTRA on these datasets, measuring performance by area under the precision-recall curve and Cohen κ. To facilitate use by nonprogrammers, we then developed an open-source Python package (pytranscripts) to streamline transcript processing, model classification, and evaluation. Additionally, a companion Streamlit web application allows users to upload interview transcripts and obtain automated coding and analytics without any coding expertise. ResultsOur findings demonstrate the success of leveraging SMLs to significantly accelerate qualitative analysis while maintaining high levels of accuracy and agreement with human annotators, although results are not universal and depend on how researchers approach qualitative coding. On the original dataset, DistilBERT achieved near-perfect agreement with human coders (Cohen κ=0.95), while ELECTRA showed substantial agreement (Cohen κ=0.71). However, both models’ performance declined on the second, more ambiguous dataset, with DistilBERT’s Cohen κ dropping to 0.48 and ELECTRA’s to 0.39. Two primary drivers of performance drop appear to be related to the number of codes applied to the dataset, and whether coders apply multiple codes to each piece of data or constrain themselves to applying one. ConclusionsThis work demonstrates that SLMs can meaningfully assist qualitative researchers with coding tasks as long as attention is paid to how experts code data that will train the SLM. This can be especially valuable in settings where deploying large language models is impractical or undesirable.

Artificial intelligence (AI) is quickly changing health care and expanding research opportunities, including in pharmacy. AI can offer effective solutions for numerous challenges in pharmacy research, such as persistent gaps in evidence-based prescribing, slow drug discovery-to-adoption timelines, complex data environments, and the need for more efficient methods, including qualitative methods. AI tools-ranging from machine learning models to large language models (LLMs)-offer new capabilities for data analysis, prediction, and content generation. Pharmacy researchers can harness these technologies to address persistent gaps in medication management, optimize patient safety and outcomes, and streamline many research workflows. However, successful integration requires understanding the spectrum of AI capabilities, appropriate use cases, and potential limitations. AI must be tailored to the research question, data, and institutional resources. This paper will guide pharmacy researchers in identifying use cases for AI in their research, selecting and applying AI tools, mitigating risks, providing standards for reporting the use of AI in research, and maximizing benefits for scientific discovery and patient outcomes.

BackgroundEmbedded pragmatic clinical trials (ePCTs) are conducted as part of routine care, which provides researchers and health systems multiple opportunities to study implementation processes and outcomes.MethodsWe conducted a cross-sectional survey of 32 ePCTs associated with the NIH Pragmatic Trials Collaboratory to assess the implementation-related outcomes that were measured or were planned to be measured, including reach (number and percent of eligible patients who participate in an intervention and the representativeness of those patients), patient engagement in or adherence to the intervention, adoption (number and percent of eligible organizations or clinicians that decide to take up or use an intervention), fidelity (clinician's delivery of an intervention as intended), adaptations (changes or modifications to an intervention), sustainability (potential for an intervention to be maintained or institutionalized after a trial concludes), sustainment (actual maintenance or institutionalization of an intervention after a trial concludes), and costs. The trials represented different phases of progress (planned, ongoing, or completed).Results91% of study teams completed the survey, and most (86%) reported measuring reach. The total number of teams measuring other outcomes was 76% for adherence, 45% for clinician adoption, 93% for fidelity, 69% for adaptations, 24% for sustainability, 38% for sustainment, and 31% for costs.ConclusionThere is an opportunity for growth in measuring clinician adoption of the intervention, sustainability, sustainment, and associated costs. Measurement of these constructs in future ePCTs could result in development of improved implementation strategies to increase the likelihood of effective implementation leading to equitable, sustainable, and scalable improvement in practice.

Clinical decision support (CDS) tools within electronic health records have the potential to improve evidence-based care but often fall short. One potential approach to improve CDS effectiveness is to extend CDS tools to include clinician-specific personalization by leveraging data on clinician behavior in the EHR. The objective of this study is to determine clinician attitudes towards CDS tools in general, review common concerns, and anticipate in what ways a clinician-personalized CDS approach can solve issues clinicians have with CDS tools. Using the case example of guideline directed medical treatment for heart failure, we conducted a qualitative study using semi-structured interviews with outpatient clinicians in primary care and cardiology settings. Participants were purposively sampled to capture diverse perspectives across specialties, practice settings, and clinician types. Interviews were analyzed using a pragmatic thematic approach. Nineteen clinicians participated. Across specialties, clinicians viewed CDS as a valuable cognitive support tool, particularly for managing complex guidelines and reducing mental workload. However, traditional CDS were consistently described as burdensome due to high alert volume, generic content, poor timing, and workflow disruption, contributing to alert fatigue and reduced trust. Clinician-personalized CDS was endorsed as a potential solution, with participants highlighting its ability to tailor information based on clinician knowledge and practice patterns, reduce redundant content, and enhance relevance. However, clinicians also raised concerns regarding the ability of algorithms to capture clinical nuance, the validity and transparency of clinician-level data, and the risk of perceived loss of autonomy or punitive feedback. Clinician-personalized CDS represents a promising extension of current CDS approaches by incorporating clinician context to improve relevance and usability. While early perceptions are favorable, important challenges related to trust, feasibility, and implementation complexity remain. Further evaluation is needed to determine whether clinician-personalization improves clinical outcomes and clinician experience, and to identify when and how this approach provides meaningful value within health systems.

Theories, models, and frameworks (TMFs) are frequently used to facilitate rigorous qualitative data collection and analysis of context in dissemination and implementation (D I) science. The Practical, Robust Implementation and Sustainability Model (PRISM), which includes contextual determinants of Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) dimensions, is one of the most widely used TMFs. Yet, missing from the literature is an assessment of how PRISM can inform the collection of qualitative data across implementation phases (i.e., pre-implementation, implementation, and post-implementation). The purpose of this study was to curate a collection of PRISM-informed qualitative data collection guides and assess how questions relate to PRISM domains and RE-AIM dimensions. In this retrospective observational study, exemplar interview and focus group guides were collected from a group of D I science experts to assess how PRISM constructs were operationalized. All guides were compiled into a database, and each question and sub-question were labelled with one or more relevant PRISM domains and RE-AIM dimensions. We calculated descriptive characteristics of all interview guides included in the database. The frequency of each PRISM domain was calculated across all interview guides, by implementation phase and by participant role. Guides (n=31) were collected from 13 research studies and were primarily used in individual interviews (n=30) and across pre-implementation (n=8), implementation (n=8), and post-implementation (n=15) phases. Research settings included the Veterans Health Administration (n=16), community health settings (n=10), schools (n=4), and an academic health center (n=1). Questions and sub-questions were more commonly labelled with PRISM domains (n=667) than RE-AIM dimensions (n=303). The Perspectives of Implementers (n=201), Perspectives of Recipients (n=152) and Implementation and Sustainability Infrastructure (n=103) were the most frequently used PRISM domains and Implementation (n=113) was the most frequent RE-AIM dimension. Our findings demonstrate that PRISM has valuable applications in qualitative data collection with recipients and implementers across all implementation phases and highlights how PRISM can be adapted for different topics and settings. The database of qualitative guides is publicly available and can be used as a resource for D I investigators using PRISM to guide their qualitative contextual assessment.

Primary care clinics need effective, low-cost tools to screen for and address multiple health risks — and technology can provide important efficiencies. My Own Health Report (MOHR) is a web-based intervention with 3 core functions: (1) risk flagging; (2) goal-setting for patient-prioritized risks; and (3) providing resources. A prior randomized trial in primary care settings showed that MOHR was feasible and effective. However, we identified two key gaps: a need for a flexible menu of delivery options to adopt and sustain MOHR and a pragmatic way to provide structured follow-up. We developed and pilot-tested two distinct primary implementation strategies to provide structured follow-up: the first primary strategy, termed “Reminder-Resource message (R2 message),” involves automated motivational text message nudges to remind patients of their MOHR goal and point to resources. The second, “R2 navigation,” involves personal support - community health worker phone calls to deliver reminders and resources. Each primary strategy is paired with additional secondary strategies that together address the contextual determinants of successful implementation and sustainment identified using our Pragmatic Robust Implementation and Sustainability Model (PRISM). We engaged clinic staff, patients, and community partners to finalize the R2 message and R2 navigation strategies and secondary strategies. This individually randomized, type 2 hybrid effectiveness-implementation trial will enroll 1,000 adult patients with at least two cancer risks (including both insufficient physical activity and fruit/vegetable intake). We will evaluate the outcomes and cost-effectiveness of R2 message and R2 navigation alone or in combination in 12 primary care clinics including both (a) large metropolitan health system and (b) rural-serving clinics. Expected outcomes include: (1) improvement in behavioral risk factors; (2) implementation outcomes – including representative engagement with R2 message and R2 navigation, and cost; and (3) improvement in practice value outcomes, including patient experience ratings. Based on the trial findings, we will co-develop an implementation, adaptation and sustainment guide for the most cost-effective implementation strategy. We expect the implementation strategies compared will produce different levels of representative engagement, behavior change, cost, and practice value. These findings will inform pragmatic ways to improve outcomes important to patients, primary care, and society. NCT07569224 (Registration date: 04/29/2026).

Guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) improves survival and health-related quality of life, yet prescribing remains suboptimal in both cardiology and primary care. This study aimed to examine clinicians’ perceptions of safety thresholds and the thresholds at which clinicians hesitate to prescribe GDMT. We conducted a concurrent mixed-methods study using a 24-item survey of cardiologists and primary care providers (PCPs) to assess perceived safe cutoffs (knowledge) and hesitancy thresholds (comfort) for prescribing GDMT across four biometric factors: systolic blood pressure, heart rate, estimated glomerular filtration rate (eGFR), and potassium. Open-ended responses were analyzed thematically, and generalized linear models compared responses by specialty. Among 101 respondents (23 cardiologists, 78 PCPs, from 3 unique health systems), clinicians reported more conservative hesitancy thresholds than safety thresholds, with greater differences among PCPs. PCPs consistently reported more conservative safety and hesitancy thresholds across all biometric categories compared to cardiologists, who pushed thresholds further (lower systolic blood pressure, heart rate, and eGFR; higher potassium). These differences were statistically significant except for potassium and eGFR in two GDMT categories. PCPs cited unfamiliarity with certain medications and concerns about adverse effects as drivers of conservative thresholds, whereas cardiologists emphasized strategies to manage adverse effects. When both potassium and eGFR were relevant, potassium more strongly influenced prescribing decisions. Thresholds at which clinicians hesitate to intensify GDMT for HFrEF are more conservative than their perceived safety thresholds, and variability exists in what is considered an evidence-based safe threshold. Cardiologists push safety and hesitancy thresholds further than PCPs and have smaller differences between these thresholds, which may reflect greater familiarity and less concern about adverse effects. Clinicians reported context is an important determinant of threshold. These findings highlight opportunities for targeted interventions to address knowledge gaps and reduce hesitancy, particularly in primary care.

BackgroundCardiovascular (CV) disease and its risk factors such as hypertension, diabetes, and hyperlipidemia account for most chronic diseases experienced by adults in the US. The use of text-based "behavioral nudges" supports behavior change and self-management of chronic disease. Building upon our previous trial utilizing an artificially intelligent (AI) chatbot for medication adherence, the Chat for Heart Health randomized controlled trial aims to test the comparative effectiveness of 3 text-based methods of delivering "nudges" to change health behaviors to reduce cardiovascular disease risk in patients across 3 safety-net healthcare systems.MethodsAdult patients ages 18-89 with CV risk factors will be recruited from 3 health systems. A target of 2097 participants will be randomized to 3 study arms: generic text messaging, AI interactive chatbot messaging, and AI interactive chatbot messaging plus pharmacist support. Evaluation of the intervention and the program will be carried out using the RE-AIM and PRISM frameworks. The primary effectiveness outcome is the change in CV risk reduction behaviors as defined by the American Heart Association's Life's Essential 8 score. Secondary outcomes including patient self-efficacy scores, clinical events, healthcare utilization, and facilitators and barriers to implementation and adoption will also be assessed.DiscussionOur large-scale pragmatic trial engages with patients and health systems who have traditionally not engaged in research, which presented substantial challenges yet will make our results more generalizable to diverse populations. Additionally, we engaged a diverse advisory panel made up of patients, community members, providers, and health systems leaders throughout the study to ensure sociocultural, linguistic, and community relevance. The results of this trial (if the intervention is effective) could lead to broader dissemination of a low-cost intervention to support behavior change to reduce CV risk.Trial registrationNCT06324981 (3/14/2024), https://clinicaltrials.gov/study/NCT06324981

Background Digital health (patient portals, remote monitoring devices, video visits) is a routine part of health care, though the digital divide may affect access. Objectives To test and validate an electronic health record (EHR) screening tool to identify patients at risk of the digital divide. Materials and Methods We conducted a retrospective EHR data extraction and cross-sectional survey of participants within 1 health care system. We identified 4 potential digital divide markers from the EHR: (1) mobile phone number, (2) email address, (3) active patient portal, and (4) >2 patient portal logins in the last year. We mailed surveys to patients at higher risk (missing all 4 markers), intermediate risk (missing 1-3 markers), or lower risk (missing no markers). Combining EHR and survey data, we summarized the markers into risk scores and evaluated its association with patients' report of lack of Internet access. Then, we assessed the association of EHR markers and eHealth Literacy Scale survey outcomes. Results A total of 249 patients (39.4%) completed the survey (53%>65 years, 51% female, 50% minority race, 55% rural/small town residents, 46% private insurance, 45% Medicare). Individually, the 4 EHR markers had high sensitivity (range 81%-95%) and specificity (range 65%-79%) compared with survey responses. The EHR marker-based score (high risk, intermediate risk, low risk) predicted absence of Internet access (receiver operator characteristics c-statistic=0.77). Mean digital health literacy scores significantly decreased as her marker digital divide risk increased (P <.001). Discussion Each of the four EHR markers (Cell phone, email address, patient portal active, and patient portal actively used) compared with self-report yielded high levels of sensitivity, specificity, and overall accuracy. Conclusion Using these markers, health care systems could target interventions and implementation strategies to support equitable patient access to digital health.

Co-prescribing naloxone alongside opioid prescriptions reduces fatal opioid overdose risk in patients discharged from inpatient care, yet its adoption remains limited. Clinical decision support (CDS) tools are effective in increasing naloxone co-prescribing in emergency and primary care settings, but data from the inpatient setting is sparse. To evaluate the effectiveness of an electronic health record (EHR)–integrated CDS tool on rates of naloxone co-prescribing for patients discharged from inpatient care with high-risk opioid prescriptions. This observational, pre-post study evaluated an EHR-embedded CDS tool implemented within an integrated health system between July 10, 2011, and July 15, 2023. Adult patients discharged from inpatient care with opioid prescriptions that met the Centers for Disease Control and Prevention high-risk criteria for opioid prescribing. A multidisciplinary team designed an interruptive CDS best practice alert to identify high-risk opioid prescriptions. The CDS offered prescribers a one-click option to add a naloxone co-prescription. Outcomes are organized under the RE-AIM implementation science framework, with the primary outcome, Effectiveness, measured by the proportion of patients receiving a naloxone prescription. Secondary outcomes include patient Reach, clinician Adoption, and fidelity to Implementation. Bayesian structural time-series models were used to evaluate differences in outcomes. During the study period, there were 355,465 inpatient discharges. In the post-intervention period, the CDS was triggered in 2.2

Background:Clinical decision support (CDS) is one strategy to increase evidence-based practices by clinicians. Despite its potential, CDS tools produce mixed results and are often disliked by clinicians. Principles from behavioral economics such as "nudges" may improve the effectiveness and clinician satisfaction of CDS tools. This paper outlines a pragmatic approach grounded in implementation science to identify and prioritize how to incorporate different types of nudges into CDS tools. Objective:The purpose of this paper is to describe a systematic and pragmatic approach grounded in implementation science to identify and prioritize how best to incorporate different types of nudges into CDS tools. We provide a case example of how this systematic approach was applied to design a CDS tool to improve guideline-concordant prescribing of mineralocorticoid receptor antagonists for patients with heart failure and reduced ejection fraction. Methods:We applied the Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitments, and Ego nudge framework and the Practical, Robust Implementation and Sustainability Model implementation science framework to systematically and pragmatically identify and prioritize different types of nudges for CDS tools. To illustrate how these frameworks can be applied in a real-life scenario, we use a case example of a CDS tool to improve guideline-concordant prescribing for patients with heart failure. We describe a process of how these frameworks can be used pragmatically by clinicians and informaticists or more technical CDS builders to apply nudge theory to CDS tools. Results:We defined four iterative steps guided by the Practical, Robust Implementation and Sustainability Model: (1) engage partners for user-centered design, (2) develop a shared understanding of the nudge types, (3) determine the overarching CDS format, and (4) brainstorm and prioritize nudge types to address each modifiable contextual issue. These steps are iterative and intended to be adapted to align with the local resources and needs of various clinical scenarios and settings. We provide illustrative examples of how this approach was applied to the case example, including who we engaged, details of nudge design decisions, and lessons learned. Conclusions:We present a pragmatic approach to guide the selection and prioritization of nudges, informed by implementation science. This approach can be used to comprehensively and systematically consider key issues when designing CDS to optimize clinician satisfaction, effectiveness, equity, and sustainability while minimizing the potential for unintended consequences. This approach can be adapted and generalized to other health settings and clinical situations, advancing the goals of learning health systems to expedite the translation of evidence into practice.

Background:Balancing safe opioid prescribing with effective pain management is essential to addressing the opioid crisis. Increasing clinician adoption of evidence-based safety practices is a public health priority. This project presents a practical, adaptable approach to developing electronic health record (EHR)-embedded clinical decision support (CDS) strategies that promote uptake of opioid safety measures recommended by the Centers for Disease Control and Prevention (CDC). The goal was to design implementation strategies that enhance guideline-concordant opioid prescribing, including a suite of EHR-integrated CDS tools. Methods:Guided by the Practical, Robust Implementation and Sustainability Model (PRISM) and informed by implementation science and user-centered design (UCD), we used an iterative, multi-level engagement process. PRISM served as the theoretical framework for integrating collaborator input and user testing throughout the development cycle. Activities included discovery, design, prototyping, and usability testing. Collaborators included executive decision-makers, informatics leaders, frontline clinicians across diverse settings, representatives from the CDC and National Institute on Drug Abuse, and patients. These methods informed the selection of opioid safety measures, design themes, and workflow considerations, resulting in implementation-ready CDS prototypes. Results:The discovery phase identified a naloxone prescribing alert as a strong candidate for redesign, aligning with CDC guidelines and relevant across all four PRISM domains. The final redesign received mean ratings of 4.0 and 4.2 out of 5 on the Acceptability of Intervention Measure (AIM) from inpatient and outpatient clinicians, respectively. Background:Despite substantial public and governmental attention and investment, the opioid crisis in the United States continues to pose a serious threat to public health.1 In 2023, 8.6 million people across the U.S. misused opioids.2 In 2022, there were 81,806 opioid-related deaths in the U.S.1 The overall rate of unintentional overdoses was 4.3% for prescription opioids and 28.7% for any opioids.3 These figures underscore the urgent need for strategies that enhance safety at the point of care, specifically when clinicians prescribe opioids, while minimizing disruption to clinical workflows and ensuring applicability across diverse healthcare settings. Opioid therapy is prescribed in a wide range of clinical contexts by various types of clinicians, from primary care to specialty and emergency care. This paper outlines the process and results of co-designing clinical decision support (CDS) tools to aid clinicians in safe opioid prescribing, drawing on implementation science and user-centered design (UCD) frameworks.

ImportancePoor medication adherence is common. Text messaging is increasingly used to change patient behavior but often not rigorously tested. ObjectiveTo compare different types of text messaging strategies with usual care to improve medication refill adherence among patients nonadherent to cardiovascular medications. Design, Setting, and ParticipantsPatient-level randomized pragmatic trial between October 2019 to April 2022 at 3 US health care systems, with last follow-up date of April 11, 2023. Adult (18 to <90 years) patients were eligible based on diagnosis of 1 or more cardiovascular condition(s) and prescribed medication to treat the condition. Patients who did not opt out and had a 7-day refill gap were randomized to 1 of 4 study groups. Intervention(s)Generic text message refill reminders (generic reminder); behavioral nudge text refill reminders (behavioral nudge); behavioral nudge text refill reminders plus a fixed-message chatbot (behavioral nudge + chatbot); usual care. Main Outcomes and MeasuresPrimary outcome was refill adherence based on pharmacy data using proportion of days covered at 12 months. Secondary outcomes were clinical events of emergency department visits, hospitalizations, and mortality. ResultsAmong 9501 enrolled patients, baseline characteristics across the 4 groups were comparable (mean age, 60 years; 47% female [n = 4351]; 16% Black [n = 1517]; 49% Hispanic [n = 4564]). At 12 months, the mean proportion of days covered was 62.0% for generic reminder, 62.3% for behavioral nudge, 63.0% for behavioral nudge + chatbot, and 60.6% for usual care (P = .06). In adjusted analysis, when compared with usual care, mean proportion of days covered was 2.2 percentage points (95% CI, 0.3-4.2; P = .02) higher for generic reminder, 2.0 percentage points (95% CI, 0.1-3.9; P = .04) higher for behavioral nudge, and 2.3 percentage points (95%, 0.4-4.2; P = .02) higher for behavioral nudge + chatbot, none of which were statistically significant after multiple comparisons correction. There were no differences in clinical events between study groups. Conclusions and RelevanceText message reminders targeting patients who delay refilling their cardiovascular medications did not improve medication adherence based on pharmacy refill data or reduce clinical events at 12 months. Poor medication adherence may be due to multiple factors. Future interventions may need to be designed to address the multiple factors influencing adherence. Trial RegistrationClinicalTrials.gov Identifier: NCT03973931

The Practical, Robust Implementation and SustainabilityModel (PRISM) is an implementation science framework that incorporates multilevel contextual considerations and key implementation outcomes that can be used to support program planning, implementation, and sustainment. The PRISM has been applied to diverse populations, settings, and implementation strategies. Tools to rapidly assess the PRISM's contextual determinants of implementation success are needed to support implementation efforts. The objectives of this study were to describe the development and preliminary psychometric and pragmatic properties of the PRISM Contextual Survey Instrument (PCSI) and to demonstrate its use to inform implementation and sustainment in health care settings. The 29-item survey was developed based on refinement of existing questions, expert feedback, and pilot testing. Three to six items were included for each of the six PRISM context domains, each rated on a 5-point Likert scale. Implementors completed the PRISM survey and quantitative measures of implementation outcomes (acceptability, feasibility, and appropriateness; Weiner et al., 2017) to establish concurrent validity. Survey results were used to tailor subsequent implementation efforts. The PCSI took 14 min on average to complete. The mean overall score across participants and sites was 3.95 (SD = 0.42). The PCSI exhibited good psychometric and pragmatic properties. Internal consistency for the subscales ranged from 0.53 to 0.82, and concurrent validity with the other implementation outcomes varied from r = 0.70 (p < .001) for feasibility to r = 0.80 (p < .001) for appropriateness. Pragmatic ratings ranged from the "minimal/emerging" to "excellent" category (Lewis et al., 2021), and provided examples illustrate the practical application of the survey results for implementation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

We conducted this secondary analysis to assess whether greater engagement with a text messaging intervention was associated with improved cardiovascular medication adherence at 12 months.

BackgroundPrevention of drug-induced QT prolongation (diLQTS) has been the focus of many system-wide clinical decision support (CDS) tools, which can be directly embedded within the framework of the electronic health record system and triggered to alert in high-risk patients when a known QT-prolonging medication is ordered. Justification for these CDS systems typically lies in the ability to accurately predict which patients are at high risk; however, it is not always evident that identification of risk alone is sufficient for appropriate CDS implementation. ObjectiveIn this investigation, we examined the impact of a system-wide, alert-based, inpatient CDS tool designed to prevent diLQTS across 10 known QT-prolonging medications. MethodsWe compared the risk of diLQTS, duration of hospitalization, and in- and out-of-hospital mortality before and after implementation of the CDS system in 178,097 hospitalizations among 102,847 patients. We also compared outcomes between those in whom an alert fired and those in whom it did not, and within the various responses to the alert by providers. Analyses were adjusted for age, sex, race and ethnicity, inpatient location, electrolyte values, and comorbidities, with the latter processed using an unsupervised clustering analysis applied to the top 500 most common medications and diagnosis codes, respectively. ResultsWe found that the simple, rule-based logic of the CDS (any prior electrocardiograph with heart rate–corrected QT interval (QTc)≥500 ms) successfully identified patients at high risk of diLQTS with an odds ratio of 2.28 (95% CI 2.10-2.47, P<.001) among those in whom it fired. However, we did not identify any impact on the risk of diLQTS based on provider responses or on the risk of inpatient, 3-month, 6-month, or 1-year mortality. When compared with rates prior to implementation, the risk of diLQTS was not significantly different after the CDS tools were deployed across the system, although mortality was significantly higher after the tools were implemented. ConclusionsWe found that despite successful identification of high-risk patients for diLQTS, deployment of an alert-based CDS did not impact the risk of diLQTS. These findings suggest that quantification of high risk may be insufficient rationale for implementation of a CDS system and that hospital systems should consider evaluation of the system in its entirety prior to adoption to improve clinical outcomes.