Background: One-size-fits-all counseling often underperforms in long-term cardiovascular prevention where sustained activation is required. We developed Signatures, a practical framework that maps individuals to four communication archetypes—Listener, Motivator, Director, with an Expert overlay—to guide message tone, structure, and shared decision-making at the point of care. The framework operationalizes psychographic segmentation into an implementable taxonomy. Methods: Classification uses three complementary pathways: (1) a 20-item self-assessment that summarizes activation and support-need domains; (2) a clinician 10-domain binary grid scored 0–10; and (3) a supervised NLP classifier that ingests de-identified narrative to estimate archetype probabilities. Discordance is resolved by conservative tie-breaking and barrier-domain overrides (health literacy, trust, access, food security). Intervention (Chatbot): We prototyped a rules-plus-NLP chatbot to (a) administer the self-assessment, (b) collect short narratives for NLP pre-labeling, and (c) deliver Signature-specific counseling (e.g., plain-language, one-step plans for Listeners; option sets and SMART weekly goals for Motivators; concise, data-driven progressions for Directors; synthesis and trade-offs for Experts). Example phrase templates were derived from our library of Signature-aligned responses for common health questions. Results: Formative testing established face validity of the three-pathway workflow and usability of chatbot dialogues. The system consistently generated actionable outputs: an assigned archetype, domain-level flags, and a message kit (tone, structure, and SDM cues) that clinicians can use or edit in real time. The chatbot supported weekly goal-setting, reminders, and teach-back prompts aligned to the assigned Signature. Conclusions: A triaged, multi-method classification combined with chatbot delivery is a feasible approach to precision communication in cardiovascular prevention, offering a practical bridge from psychographic theory to routine encounters and remote interactions. Prospective validation will assess concordance among pathways, equity, and effects on engagement, lifestyle habits, condition management and clinical proxies.

(1) Background: Artificial intelligence (AI) has flourished in recent years. More specifically, generative AI has had broad applications in many disciplines. While mental illness is on the rise, AI has proven valuable in aiding the diagnosis and treatment of mental disorders. However, there is little to no research about precisely how much interest there is in AI technology. (2) Methods: We performed a Google Trends search for “AI and mental health” and compared relative search volume (RSV) indices of “AI”, “AI and Depression”, and “AI and anxiety”. This time series study employed Box–Jenkins time series modeling to forecast long-term interest through the end of 2024. (3) Results: Within the United States, AI interest steadily increased throughout 2023, with some anomalies due to media reporting. Through predictive models, we found that this trend is predicted to increase 114% through the end of the year 2024, with public interest in AI applications being on the rise. (4) Conclusions: According to our study, we found that the awareness of AI has drastically increased throughout 2023, especially in mental health. This demonstrates increasing public awareness of mental health and AI, making advocacy and education about AI technology of paramount importance.

Large Language Modeling (LLM) is ubiquitous in the healthcare industry guiding clinical decisions. With the increase in demand, we must proceed with caution in the AI industry. In this study, we evaluated the accuracy of the Random Forest model in comparison to other similar models. From the 2005 to 2010 National Health and Nutrition Examination Survey (NHANES) dataset, we assessed if there was a relationship between depression and hypertension and if depression predicted hypertension. Depression was determined using the Patient Health Questionnaire (PHQ)−9 ≥ 10. Hypertension was determined by taking the average of three systolic pressure readings that were elevated. Current smoking was determined by self-reported data. We tested several Random Forest models, compared with logistic regression, naïve Bayes, decision tree model and assessed these for accuracy. The percentage of the population with diabetes was 7.7%. We found that in comparison to logistic regression (87.8%), naïve Bayes (84.6%), and decision tree model (89.3%), the Random Forest model (98.4%) was considered most accurate. We also found that out of all the variables, according to the Gini impurity index, employment (150) received the highest score in relative importance. The next highest score was depression (140). This system demonstrates the importance of using traditional AI systems such as Random Forest modeling in conjunction with LLM. ChatGPT and LLM’s must be further understood to integrate with classical machine learning techniques to make further advances in healthcare. LLM’s have been mobilized to write history and physical assessment, extracting drug names from medical notes, and condensing radiology reports. Abstraction of medical records and other applications in healthcare can further be enhanced by using the full potential for AI systems such LLM.

During the COVID-19 pandemic, a broad range of workplaces and institutions have strengthened infection control policies and protocols to minimize spread of the COVID-19 disease. This includes measures such as screening questionnaires to provide risk assessment, risk stratification for employees and visitors, temperature screening processes, triage of business essential operations, physical distancing where possible, universal mask and face cover usage, and regular disinfection. However, these measures only address possible exposures within the workplace. In reality, individuals could have multiple high-risk exposures outside of the workplace which significantly contribute to the prevalence of COVID-19 in a given worker population. To this point, most current screening and assessment tools do not incorporate the social determinants of health (SDOH). Social determinants of health are defined as conditions in the environments in which people are born, live, learn, work, play, worship, and age that account for 60% of health outcomes. They affect a wide range of health, functioning, and quality-of-life outcomes and risks including social, economic and physical conditions in various environments and settings such as home, school, church, workplace, and neighborhoods.1 These determinants have a profound influence on health that cannot be ignored, especially in vulnerable populations such meat packing plant workers. In this population, the virus spreads readily because workplace practices cannot control the community-based risk an individual may have. During the last months, the authors of this paper worked with meat packing plants, a critical infrastructure and highly impacted industry, to implement screening and assessment for this population. We identified multiple SDOH challenges contributing to the transmission of COVID-19 within this population that were not due to the workplace. These challenges included: 1. Data privacy concerns. Respondents were reluctant to answer questions due to fear of how the information may be used. Fear of deportation caused many workers to hesitate in providing home addresses, phone numbers, and other demographic information for themselves or close contacts. This added another layer of challenge for contact tracing. 2. Language barrier. Many workers did not speak English proficiently, or did not speak English at all. 3. Education level. Some workers did not know how to read or write. 4. Health literacy, including: a. Disease transmission understanding. Some may have been able to read but did not understand how illness is spread or were not able to understand common terms such as shortness of breath. b. Health system literacy. Most did not know where to go and how to use the US healthcare system including medical and social services when a problem occurs. c. Health benefit literacy. Most did now know how to use health benefits and the cost of services if they pursued care. d. Cultural beliefs about managing illness that may not align with medical recommendations. 5. Multi-family and multi-generational cohabitation. Homes with shared restroom and kitchen. This made it especially challenging to recommend a person to self-isolate, as most resources are communal. 6. Social customs. Participation in religious ceremonies, sports, dancing, and other social activities together outside of the work environment. 7. Shared transportation. Carpooling or use of public transportation was common, making physical distancing difficult. 8. Trust of their employer. Biases based on their previous experiences and the role of authority's trustworthiness. In order to understand the complexity of exposure risk, we used a modified SDOH risk assessment tool for meat packing plant workers which was adopted from the Accountable Health Communities Health-Related Social Needs Screening Tool published by the Centers for Medicare & Medicaid Services.2 While interviewing plant workers, the social factors playing the largest role in the COVID-19 clusters were housing, transportation, and community interactions. This is consistent with the literature, where the secondary attack rates among family members can be as high as 49.56%3 and individuals travelling with a positive case could have a 7× higher risk of infection.3 Additionally, regular social contact with coworkers in community settings in locations with ongoing community transmission increases the risk of infection.4 These three factors had a high impact on the spread of the virus because (1) the factor directly contributed to close contact with COVID-19 positive individuals and (2) the impact that the employer could have in mitigating exposure. These SDOH factors along with the Occupational Safety and Health Administration (OSHA) scheme for classifying the workplace risk of COVID-19 infection as high, medium, or low based on potential contact with infected individuals5 were combined to create a framework to assess and individual's occupational and non-occupational COVID-19 exposure risk (see Fig. 1), and provide guidance to workplaces on mitigation measures (Fig. 2).FIGURE 1: Social determinants of health questionnaire.FIGURE 2: Social determinants of health are needed in COVID-19 risk assessments for the workforce.Employers can help mitigate exposure in multi-family multi-generational households and community gatherings by providing their workforce with practical guidance to exercise safety recommendations and self-isolate sick individuals to the extent possible. This could include wearing cloth face coverings, using separate linens and kitchen utensils, and hand hygiene to minimize the transmission. Exposure risk due to transportation can be minimized if an employer provides company-sponsored transportation that follows physical distancing recommendations. However, these interventions can only be possible if employers examine living arrangements, community interactions and transportation as part of the COVID-19 risk assessment, as these factors are otherwise neglected. In this framework, social determinants such as food security and access to care were categorized as medium risk of exposures because these factors can be directly influenced by the employer. Food insecurity disproportionately affects low-income households that already struggled to meet basic needs prior to the pandemic.6 One employer best practice strategy for addressing food insecurity during COVID-19 is to sponsor boxed lunches for the workforce, which they can consume in their personal vehicles to allow for physical distancing. Additionally, employers can connect with local food bank to create co-ops for food distribution. Access to health care is another important SDOH that can be impacted by employers with language-appropriate telemedicine and mental health services. Employers can contract a health care liaison service to aid the workforce in navigating clinical appointments, medications and prescription costs. A liaison who can speak the language and understands the customs can build trust within the workforce, as they maintain data privacy at the individual level. Employer sponsored onsite and near-site clinics can also help to significantly decrease the barriers to care. Lastly, the education level and language preference of the meat packing workforce was categorized as low risk for contributing to the overall risk of exposure to COVID-19. This is because language-appropriate educational materials were made available to employees, covering the following topics: COVID-19 transmission, symptoms, refrain from coming to work if sick, the importance of health and nutrition, and information for local community resources. The pandemic has exposed the impact of SDOH for COVID-19 risk among the meat packing workforce. While the framework to assess the occupational and non-occupational risks of exposure to COVID-19 was developed for meat packing plant workers, the methodology can be used to calibrate risk for COVID-19 for a number of other vulnerable populations in industries with similar employee attributes, such as the agricultural or manufacturing workforce. This framework could also provide important sociological insights about the average SDOH for a workforce and can achieve a more holistic assessment of COVID-19 risk. It is our recommendation that future COVID-19 risk assessment tools, especially those for workforces with higher-risk populations with complex needs, incorporate SDOH.

Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in practice and is the leading cause of debilitating strokes with significant economic burden. It is currently not known whether asymptomatic undiagnosed AF should be treated if detected by various screening methods. Currently, United States guidelines have no recommendations for identifying patients with asymptomatic undiagnosed AF due to lack of evidence. The American Heart Association Center for Health Technology & Innovation undertook a plan to identify tools in 3 phases that may be useful in improving outcomes in patients with undiagnosed AF. In phase I we sought to identify AF risk factors that can be used to develop a risk score to identify high-risk patients using a large commercial insurance dataset. The principal findings of this study show that individuals at high risk for AF are those with advanced age, the presence of heart failure, coronary artery disease, hypertension, metabolic disorders, and hyperlipidemia. Our analysis also found that chronic respiratory failure was a significant risk factor for those over 65 years of age and chronic kidney disease for those less than 65 years of age.

The connection between health literacy and health outcomes includes access and utilization of healthcare services, patient/provider interaction and self-care. Digital approaches can be designed to simplify or expand on a concept, test for understanding, and do not have a time constraint. New technologies, such as artificial intelligence and machine learning, virtual and augmented reality, and blockchain can move the role of technology beyond data collection to a more integrated system. Rather than being a passive participant, digital solutions provide the opportunity for the individual to be an active participant in their health. These solutions can be delivered in a way that builds or enhances the individual's belief that the plan will be successful and more confidence that they can stick with it. Digital solutions allow for the delivery of multi-media education, such as videos, voice, and print, at different reading levels, in multiple languages, using formal and informal teaching methods. By giving the patient a greater voice and empowering them to be active participants in their care, they can develop their decision making and shared decision making skills. The first step in our health literacy instructional model is to address the emotional state of the person. Once the emotional state has been addressed, and an engagement strategy has been deployed the final phase is the delivery of an educational solution. While a clear definition of health literacy and an instructional model are important, further research must be done to continually determine more effective ways to incorporate health technology in the process of improving health outcomes.

The authors do a nice job of highlighting the opportunities and challenges of using mobile health applications in cardiovascular research [ [1] Sarwar C.M.S. Vaduganathan M. Anker S.D. Coiro S. Papadimitriou L. Saltz J. Schoenfeld E.R. Clark R.L. Dinh W. Kramer F. Gheorghiade M. Fonarow G.C. Butler J. Mobile health applications in cardiovascular research.. Int. J. Cardiol. 2018; 269: 197-203 Abstract Full Text Full Text PDF Scopus (18) Google Scholar ]. We believe the question is not can or should mobile health applications be used in cardiovascular research, but rather, how can they be used more effectively, so that they can achieve the basic promise that technology can be used to achieve better health outcomes. The takeaway is that while mobile health applications hold a lot of promise, there is very little evidence that this promise can be fulfilled. While many of the benefits described by the authors focus on monitoring of biometric and self-reported data, we believe that mobile health applications can be an effective intervention with patients to help them manage their health condition, navigate the healthcare system, communicate more effectively with their healthcare team, and ultimately make good, well informed decisions about their health. Mobile health applications in cardiovascular researchInternational Journal of CardiologyVol. 269PreviewCardiovascular disease is the leading cause of mortality and morbidity globally. With widespread and growing use of smart phones and mobile devices, the use of mobile health (mHealth) in transmission of physiologic parameters and patient-referred symptoms to healthcare providers and researchers, as well as reminders and care plan applications from providers to patients, has potential to revolutionize both clinical care and the conduct of clinical trials with improved designs, data capture, and potentially lower costs. Full-Text PDF

The Chronic Care Model (CCM) was developed in the 1990s as a way of improving care for chronic diseases such as heart disease, hypertension, diabetes, and pulmonary disease by identifying critical components and strategies [ [1] Wagner E.H. Austin B.T. Davis et al. Improving chronic illness care: translating evidence into action. Health Aff. 2001; 20: 64-78 Crossref PubMed Scopus (2157) Google Scholar ]. Yeoh, and colleagues conducted a systematic review of the benefits and limitations of the Chronic Care Model in a primary care setting [ [2] Yeoh E.K. Wong M. Wong E. et al. Benefits and limitations of implementing chronic care model (MMC) in primary care programs: a systematic review. Int. J. Cardiol. 2018; (XXXXXXXXXXX) Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar ]. We applaud the authors for their work on this important topic. The global burden of chronic health conditions is high and continues to grow, requiring stronger systems of care [ [3] World Health Organization Monitoring health for the SDGs (Sustainable Development Goals). in: World Health Statistics. World Health Organization, 2017 Google Scholar ]. Their findings are consistent with other reviews that indicate a benefit of using this model [ [4] Stellefson M. Dipnarine K. Stopka C. The chronic care model and diabetes management in US primary care settings: a systematic review. Prev. Chronic Dis. 2013; 10120180 Crossref Scopus (267) Google Scholar , [5] Coleman K. Austin B.T. Brach C. Wagner E.H. Evidence on the chronic care model in the new millennium. Health Aff. 2009; 28 Crossref Scopus (978) Google Scholar ]. These findings also suggest the need for higher quality research and strategies for feasibly implementing the CCM in a primary care setting. The management of chronic disease is vital for the health of the world. The paucity of studies and clear pathways of practice transformation into an effective, proven model demands immediate and significant action. In fact, the care of chronic health conditions might be more effective outside of the traditional healthcare setting. Benefits and limitations of implementing Chronic Care Model (CCM) in primary care programs: A systematic reviewInternational Journal of CardiologyVol. 258PreviewChronic Care Model (CCM) has been developed to improve patients' health care by restructuring health systems in a multidimensional manner. This systematic review aims to summarize and analyse programs specifically designed and conducted for the fulfilment of multiple CCM components. We have focused on programs targeting diabetes mellitus, hypertension and cardiovascular disease. Full-Text PDF

Knowledge and education is foundational to an individual receiving care, and health literacy is the ability of patients to understand and act on health information. Cardiovascular disease and diabetes are complex conditions that require active participation on the part of the patient. Lack of understanding on the condition and participation in self-care behaviors limits the effectiveness of treatment. An effective model is needed to better understand how patients with cardiovascular disease and diabetes acquire the knowledge and skills necessary to manage their health. In working together, the authors have created multiple programs that have been delivered at medical offices and corporations, resulting in a model for building functional and critical health literacy skills. This model is a progression that begins with health literacy, including the knowledge and understanding of the condition. Functional literacy includes numeracy, which is the ability to understand and manipulate numbers, and navigation, which is an understanding of what to do with the information. Finally, critical health literacy includes communication skills, including knowing what questions to ask and what information to share, and decision making, which can include shared decision making. These five levels of health literacy form a progression in the ability of the patient to become an active participant in their care, and inform the healthcare provider on effective educational methods.

While there have been multiple efforts to improve the health of the population, and simultaneously reducing the cost of healthcare and improving the quality of care, there is no single model for improving population health. Worksite health is a microcosm of the health of the nation and the results of been mixed. This paper considers the best practices associated with key worksite health. These best practices include the type of worksite intervention, attention to health literacy, engagement, onsite clinics, coaching and care plans, a digital platform, social support, population health, performance excellence, performance improvement, and an evaluation strategy. While each best practice is considered important, very few programs provide all of the components, and most emphasize one component over the other. A gap in the literature, therefore, is how these best practices can be combined into a single program. Following the review of literature of best practices, a case study, of the program design, of a comprehensive worksite program was conducted to demonstrate how these best practices are operationalized. The Game of Health, is a cognitive based program that focuses on stress management and behavior change in a program that provides onsite programs, a medical clinic, and a digital platform.

Background Primary care providers with limited time and resources bear a heavy responsibility for chronic disease prevention or progression. Reliable clinical tools are needed to risk stratify patients for more targeted care. This exploratory study examined the care of patients who had been risk stratified regarding their likelihood of clinically progressing to type 2 diabetes. Methods This was a retrospective chart review pilot study conducted to assess a primary care provider’s use of a risk screening test. In this quality improvement project, the result of the risk screening was examined in relation to its influence on medical management and clinical impact on patients at risk for diabetes. All providers were board certified in family medicine and had more than 10 years clinical experience in managing diabetes and prediabetes. No specific clinical practice guidelines were mandated for patient care in this pilot study. Physicians in the practice group received an orientation to the diabetes risk measure and its availability for use in a pilot study to be conducted over a 6-month period. We identified the 696 nondiabetic adults in family practices who received a risk screening test (PreDx®, a multi-marker blood test that estimates the 5-year likelihood of conversion to type 2 diabetes) between June and November 2011 for a 6-month sample. A comparison group of 2,002 patients from a total database of 3.2 million patients who did not receive the risk test was randomly selected from the same clinical database after matching for age, sex, selected diagnoses, and metabolic risk factors. Patient groups were compared for intensity of care provided and clinical impact. Results Compared to patients with a similar demographic and diagnostic profile, patients who had the risk test received more intensive primary care and had better clinical outcome than comparison patients. Risk-tested patients were more likely to return for follow-up visits, be monitored for relevant cardio-metabolic risk factors, and receive prescription medications with P<0.001. Further, intensity of care was associated with the level of risk test result: patients with moderate or high scores were more likely to return for follow-up visits and receive prescription medications than patients with low scores. All P-values for comparison patients between the low and moderate groups, low and high groups, and moderate and high groups resulted in P<0.001. Risk-tested patients were more likely than their comparison group counterparts to achieve weight reduction, lowered blood pressure, and improved blood glucose and cholesterol as demonstrated by P-values of <0.001. Conclusion Use of a risk stratification test in primary care may help providers to more effectively identify high risk patients, manage diabetes risk, increase patient involvement in diabetes risk management, and improve clinical outcomes. A randomized controlled study is the next step to investigate the impact of diabetes risk stratification in primary care.

Primary care medicine in the United States is undergoing a revolutionary shift. Primary care providers and their staff have an extraordinary chance to create and participate in exciting new approaches to care. New strategies will require courage, flexibility, and openness to change by every member of the practice team, especially the lead clinician who is most often the physician, but can also be the nurse practitioner or physician's assistant. Providers must first recognize their need to alter their fundamental identity to incorporate a new kind of leadership role-that of the MDCEO (TM) (i.e., the individual clinician who leads the practice to ensure that quality, service, and financial systems are developed and effectively managed). This paper provides a practical vision and rationale for the required transition in primary care, pointing the way for how to achieve new practice effectiveness through new leadership roles. It also provides a model to evaluate the status of a primary care practice. The authors have extensive experience in working with primary care providers to radically evolve their clinical practices to become MDCEOs (TM). The MDCEO (TM) will articulate the vision and strategy for the practice, define and foster the practice culture, and create and facilitate team development and overall high level functioning. Each member of the team can then begin to lead their part of the practice: a 21st century population-oriented, purpose-based practice resulting in increased quality of care, improved patient outcomes, greater financial success, and enhanced peace of mind

Purpose: The purpose of this clinical pilot project was to evaluate the effectiveness of a 12 week lifestyle change program targeted to patients with chronic disease.Data sources: Data were collected weekly from participants using individual and group feedback and body composition analysis.Conclusions: The Game of Health was well received by patients and was effective in modifying behaviors to achieve a healthier lifestyle and to improve body composition. Primary care providers need to consider how to make lifestyle change programs available to their patients to complement clinical interventions.

BACKGROUND:Age, gender, and race are factors that influence atherosclerotic coronary heart disease (CHD) risk and may conceivably affect the efficacy of lipid-altering drugs.METHODS:Post hoc analysis of two multicenter, 6-week, double-blind, randomized, parallel-group trials assessed age (<65 and ≥ 65 years), gender, and race (white, black, and other) effects on atorvastatin plus ezetimibe versus up-titration of atorvastatin in hypercholesterolemic patients with CHD risk. High CHD risk subjects with low-density lipoprotein (LDL) cholesterol levels ≥ 70 mg/dL (~1.81 mmol/L) during stable atorvastatin 40 mg therapy were randomized to atorvastatin 40 mg plus ezetimibe 10mg, or up-titrated to atorvastatin 80 mg. Moderately high CHD risk subjects with LDL cholesterol levels ≥ 100 mg/dL (~2.59 mmol/L) with atorvastatin 20mg were randomized to atorvastatin 20mg plus ezetimibe 10mg, or atorvastatin 40 mg.RESULTS:Although some variability existed, age, gender, and race subgroups did not substantially differ from the entire patient population with regard to lipid-altering findings. Ezetimibe plus atorvastatin produced greater percent reductions in LDL cholesterol, total cholesterol, triglycerides, non-high-density lipoprotein (HDL) cholesterol, and apolipoprotein B than up-titration of atorvastatin for all subgroups. HDL cholesterol and apolipoprotein AI changes were small and variable.CONCLUSION:Treatment efficacy in age, gender, and race subgroups did not substantially differ from the entire study population. Ezetimibe combined with atorvastatin generally produced greater incremental reductions in LDL cholesterol and several other key lipid parameters compared with doubling the atorvastatin dose in hypercholesterolemic patients with high or moderately high CHD risk. These results suggest that co-administration of ezetimibe with statins is a useful therapeutic option for treatment of dyslipidemia in differing patient populations.

Purpose: The purpose of this clinical pilot project was to evaluate the effectiveness of a 12 week lifestyle change program targeted to patients with chronic disease. Data sources: Data were collected weekly from participants using individual and group feedback and body composition analysis. Conclusions: The Game of Health was well received by patients and was effective in modifying behaviors to achieve a healthier lifestyle and to improve body composition. Primary care providers need to consider how to make lifestyle change programs available to their patients to complement clinical interventions.