Shared and unique transcriptomic signatures of antidepressant and probiotics action in the mammalian brain.

Understanding the shared and divergent mechanisms across antidepressant (AD) classes and probiotics is critical for improving treatment for mood disorders. Here we examine the transcriptomic effects of bupropion (NDRI), desipramine (SNRI), fluoxetine (SSRI) and a probiotic formulation (Lacidofil®) on 10 regions across the mammalian brain. These treatments massively alter gene expression (on average, 2211 differentially expressed genes (DEGs) per region-treatment combination), highlighting the biological complexity of AD and probiotic action. Intersection of DEG sets against neuropsychiatric GWAS loci, sex-specific transcriptomic portraits of major depressive disorder (MDD), and mouse models of stress and depression reveals significant similarities and differences across treatments. Interestingly, molecular responses in the infralimbic cortex, basolateral amygdala and locus coeruleus are region-specific and highly similar across treatments, whilst responses in the Raphe, medial preoptic area, cingulate cortex, prelimbic cortex and ventral dentate gyrus are predominantly treatment-specific. Mechanistically, ADs concordantly downregulate immune pathways in the amygdala and ventral dentate gyrus. In contrast, protein synthesis, metabolism and synaptic signaling pathways are axes of variability among treatments. We use spatial transcriptomics to further delineate layer-specific molecular pathways and DEGs within the prefrontal cortex. Our study reveals complex AD and probiotics action on the mammalian brain and identifies treatment-specific cellular processes and gene targets associated with mood disorders.

Developing Signs of Safety: A Deaf-accessible counselling toolkit for trauma and addiction.

The U.S. Deaf community-more than half a million Americans who communicate using American Sign Language (ASL)-experiences higher rates of trauma exposure and substance use disorder (SUD) than the general population. Yet there are no evidence-based treatments for any behavioural health condition that have been evaluated for use with Deaf people. The driving aim of our work, therefore, has been to develop and formally evaluate a Deaf-accessible trauma/SUD counselling approach. Here we describe our initial intervention development work and a single-arm pilot that evaluated the feasibility, acceptability, and preliminary clinical efficacy of Signs of Safety-a Deaf-accessible toolkit to be used with an existing, widely adopted protocol for trauma and addiction (Seeking Safety). Preliminary efficacy results indicated clinically significant reductions in PTSD symptoms and frequency of alcohol use for the Seeking Safety/Signs of Safety model. Frequency of drug use did not change significantly-likely attributable to the mid-study legalization of recreational marijuana in our state. Next steps include the redesign and refilming of Signs of Safety based on pilot participant feedback, again using a Deaf-engaged development and production process. This new toolkit will be tested via a pilot randomized controlled trial designed based on present methodological lessons learned.

U.S. hospital performance methodologies: a scoping review to identify opportunities for crossing the quality chasm.

BACKGROUND: Hospital performance quality assessments inform patients, providers, payers, and purchasers in making healthcare decisions. These assessments have been developed by government, private and non-profit organizations, and academic institutions. Given the number and variability in available assessments, a knowledge gap exists regarding what assessments are available and how each assessment measures quality to identify top performing hospitals. This study aims to: (a) comprehensively identify current hospital performance assessments, (b) compare quality measures from each methodology in the context of the Institute of Medicine's (IOM) six domains of STEEEP (safety, timeliness, effectiveness, efficiency, equitable, and patient-centeredness), and (c) formulate policy recommendations that improve value-based, patient-centered care to address identified gaps. METHODS: A scoping review was conducted using a systematic search of MEDLINE and the grey literature along with handsearching to identify studies that provide assessments of US-based hospital performance whereby the study cohort examined a minimum of 250 hospitals in the last two years (2017-2019). RESULTS: From 3058 unique records screened, 19 hospital performance assessments met inclusion criteria. Methodologies were analyzed across each assessment and measures were mapped to STEEEP. While safety and effectiveness were commonly identified measures across assessments, efficiency, and patient-centeredness were less frequently represented. Equity measures were also limited to risk- and severity-adjustment methods to balance patient characteristics across populations, rather than stand-alone indicators to evaluate health disparities that may contribute to community-level inequities. CONCLUSIONS: To further improve health and healthcare value-based decision-making, there remains a need for methodological transparency across assessments and the standardization of consensus-based measures that reflect the IOM's quality framework. Additionally, a large opportunity exists to improve the assessment of health equity in the communities that hospitals serve.

Deaf ACCESS: Adapting Consent Through Community Engagement and State-of-the-Art Simulation.

One of the most understudied health disparity populations in the United States is the Deaf community, a sociolinguistic minority group of more than 500,000 individuals who communicate via American Sign Language. Research on Deaf health disparities is lacking due to inaccessible recruitment, sampling, and data collection procedures, as well as the fundamental disconnect between medical and cultural views of Deaf people. A potential starting place for addressing inaccessible research methods and mistrust of the biomedical research community is the careful reconsideration of the traditional informed consent process, often a Deaf individual's first point of contact with the research world. Yet, most Deaf individuals experience obstacles to engaging in informed consent due to differences in language and development compared to hearing individuals. In response to these issues, our team led a three-phase, formative, community-engaged approach to adapt the informed consent process and train research staff in the updated method so that all required components are properly communicated and understood. The goals of our work were to promote Deaf engagement in research about the Deaf community, increase the number of Deaf individuals who participate in general population biomedical research, and generalize our findings to improve research accessibility for the general population.

Diagnosis and Treatment Patterns in Celiac Disease.

Celiac disease (CD) is an immune-mediated gastrointestinal (GI) disorder driven by innate and adaptive immune responses to gluten. Presentation of CD has changed over time, with non-GI symptoms, such as anemia and osteoporosis, presenting more commonly. With improved screening and diagnostic methods, the reported prevalence of CD has increased globally, and there is considerable global variation in diagnostic and treatment practices. The objective of this study was to describe the current state of CD diagnosis and treatment patterns. A targeted review of literature from MEDLINE, Embase, the Cochrane Library, and screening of relevant conference abstracts was performed. The generally recommended diagnostic approach is GI endoscopy with small bowel biopsy; however, in selected patients, biopsy may be avoided and diagnosis based on positive serology and clinical symptoms. Diagnosis often is delayed; the average diagnostic delay after symptom onset is highly variable and can last up to 12 years. Barriers to accurate and timely diagnosis include atypical presentation, lack of physician awareness about current diagnostic criteria, misdiagnosis, and limited access to specialists. Currently, strict adherence to a gluten-free diet (GFD) is the only recommended treatment, which is not successful in all patients. Only one-third of patients are monitored regularly following diagnosis. Unmet needs for CD include improvements in the accuracy and timeliness of diagnosis, and the development of treatments for both refractory CD and GFD nonresponsive CD. Further research should investigate the impact of education about gluten-free eating and the availability of gluten-free foods support adherence and improve outcomes in patients with CD.

Deaf Qualitative Health Research: Leveraging Technology to Conduct Linguistically and Sociopolitically Appropriate Methods of Inquiry.

One of the most understudied health disparity populations in the United States is the Deaf community-a sociolinguistic minority group of at least 500,000 individuals who communicate using American Sign Language. Research within this population is lacking, in part, due to researchers' use of methodologies that are inaccessible to Deaf sign language users. Traditional qualitative methods were developed to collect and analyze participants' spoken language. There is, therefore, a paradigm shift that must occur to move from an auditory data schema to one that prioritizes the collection and analysis of visual data. To effectively navigate this shift when working with Deaf sign language users, there are unique linguistic and sociopolitical considerations that should be taken into account. The current article explores these considerations and outlines an emerging method of conducting qualitative analysis that, we argue, has the potential to enhance qualitative researchers' work regardless of the population of focus.

Barriers and Facilitators to Deaf Trauma Survivors' Help-Seeking Behavior: Lessons for Behavioral Clinical Trials Research.

Deaf individuals experience significant obstacles to participating in behavioral health research when careful consideration is not given to accessibility during the design of study methodology. To inform such considerations, we conducted an exploratory secondary analysis of a mixed-methods study that originally explored 16 Deaf trauma survivors' help-seeking experiences. Our objective was to identify key findings and qualitative themes from consumers' own words that could be applied to the design of behavioral clinical trials methodology. In many ways, the themes that emerged were not wholly dissimilar from the general preferences of members of other sociolinguistic minority groups-a need for communication access, empathy, respect, strict confidentiality procedures, trust, and transparency of the research process. Yet, how these themes are applied to the inclusion of Deaf research participants is distinct from any other sociolinguistic minority population, given Deaf people's unique sensory and linguistic characteristics. We summarize our findings in a preliminary "Checklist for Designing Deaf Behavioral Clinical Trials" to operationalize the steps researchers can take to apply Deaf-friendly approaches in their empirical work.

Early efforts to target and enroll high-risk diabetic patients into urban community-based programs.

Health care disparities in minority populations can be attributed to a number of factors, including lack of access to coordinated primary care and chronic disease management programming. Interventions using a data-centric, coordinated, multidisciplinary, team-based approach to address patients with complex chronic comorbidities have demonstrated improvements in patient outcomes. The use of hospital admission and billing data coupled with care management teams to care for high-risk patients with chronic conditions may be an effective model for improving quality of care while reducing health care costs. This article describes how Camden city, the poorest city in the nation, has made headway toward developing an integrated approach to improving care while reducing costs for the city's most vulnerable.

Comprehensiveness, Accuracy, and Readability of Exercise Recommendations Provided by an AI-Based Chatbot: Mixed Methods Study.

BACKGROUND: Regular physical activity is critical for health and disease prevention. Yet, health care providers and patients face barriers to implement evidence-based lifestyle recommendations. The potential to augment care with the increased availability of artificial intelligence (AI) technologies is limitless; however, the suitability of AI-generated exercise recommendations has yet to be explored. OBJECTIVE: The purpose of this study was to assess the comprehensiveness, accuracy, and readability of individualized exercise recommendations generated by a novel AI chatbot. METHODS: A coding scheme was developed to score AI-generated exercise recommendations across ten categories informed by gold-standard exercise recommendations, including (1) health condition-specific benefits of exercise, (2) exercise preparticipation health screening, (3) frequency, (4) intensity, (5) time, (6) type, (7) volume, (8) progression, (9) special considerations, and (10) references to the primary literature. The AI chatbot was prompted to provide individualized exercise recommendations for 26 clinical populations using an open-source application programming interface. Two independent reviewers coded AI-generated content for each category and calculated comprehensiveness (%) and factual accuracy (%) on a scale of 0%-100%. Readability was assessed using the Flesch-Kincaid formula. Qualitative analysis identified and categorized themes from AI-generated output. RESULTS: AI-generated exercise recommendations were 41.2% (107/260) comprehensive and 90.7% (146/161) accurate, with the majority (8/15, 53%) of inaccuracy related to the need for exercise preparticipation medical clearance. Average readability level of AI-generated exercise recommendations was at the college level (mean 13.7, SD 1.7), with an average Flesch reading ease score of 31.1 (SD 7.7). Several recurring themes and observations of AI-generated output included concern for liability and safety, preference for aerobic exercise, and potential bias and direct discrimination against certain age-based populations and individuals with disabilities. CONCLUSIONS: There were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations. Exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise.

Pharmacoinformatics-enabled Interventions Improved Care Coordination and Identified Pharmacy-Related Safety Issues in a Multicultural Medicare Population.

BACKGROUND: Compared to White populations, multicultural older adults experience more gaps in preventive care (e.g., vaccinations, screenings, chronic condition monitoring), social determinants of health barriers (e.g., access to care, language, transportation), and disparities and inequities (e.g., comorbidities, disease burden, and health care costs). OBJECTIVES: This study aims to describe an informatics-based approach used to execute and evaluate results of a member-centric, pharmacoinformatics-informed engagement program to deliver culturally tailored microinterventions to close medication-related gaps in care utilizing multidisciplinary care coordination that leverages the expanded role of the pharmacist. The operational framework will be described, and the influence of the medication use processes will be reported in a multicultural Medicare Advantage cohort. METHODS: A pharmacoinformatics framework was leveraged to conduct a retrospective, observational cohort analysis of the program. Claims data were used to evaluate the influence of medication use process microinterventions from a large Medicare Advantage cohort of members who self-identify as Black and/or Hispanic, and have type 2 diabetes mellitus and/or hypertension, and meet eligibility criteria for multidisciplinary (e.g., nursing and pharmacy) care management (CM) and received pharmacy referral from January 1, 2022, through September 30, 2023. RESULTS: A total of 3,265 Medicare Advantage members (78.3% Black and 21.7% Hispanic) received CM and pharmacy referral. Pharmacovigilance reviews conducted during this timeframe identified 258 acute events that escalated member CM. Provider outreach (n = 185) informed of safety issues (drug duplication, n = 48; drug interactions, n = 21; drug-disease interactions, n = 5; noncompliance and/or dosing issues, n = 27). Outreach to members (n = 160) and providers (n = 164) informed of open quality-related measure gaps for medication adherence. CONCLUSION: The application of pharmacoinformatics by a payor-led multicultural clinical program demonstrated quality improvements in Medicare Advantage member identification including risk stratification, timely outreach for pharmacy-related safety issues, and improved efficiency of multidisciplinary care coordination involving medication use process workflows.

Comprehensive Cardiovascular Risk Factor Control With a Mobile Health Cardiovascular Risk Self-Management Program.

BACKGROUND: Mobile health technology's impact on cardiovascular risk factor control is not fully understood. This study evaluates the association between interaction with a mobile health application and change in cardiovascular risk factors. METHODS AND RESULTS: Participants with hypertension with or without dyslipidemia enrolled in a workplace-deployed mobile health application-based cardiovascular risk self-management program between January 2018 and December 2022. Retrospective evaluation explored the influence of application engagement on change in blood pressure (BP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and weight. Multiple regression analyses examined the influence of guideline-based, nonpharmacological lifestyle-based digital coaching on outcomes adjusting for confounders. Of 102 475 participants, 49.1% were women. Median age was 53 (interquartile range, 43-61) years, BP was 134 (interquartile range, 124-144)/84 (interquartile range, 78-91) mm Hg, TC was 183 (interquartile range, 155-212) mg/dL, LDL-C was 106 (82-131) mg/dL, and body mass index was 30 (26-35) kg/m2. At 2 years, participants with baseline systolic BP ≥140 mm Hg reduced systolic BP by 18.6 (SEM, 0.3) mm Hg. At follow up, participants with baseline TC ≥240 mg/dL reduced TC by 65.7 (SEM, 4.6) mg/dL, participants with baseline LDL-C≥160 mg/dL reduced LDL-C by 66.6 (SEM, 6.2) mg/dL, and participants with baseline body mass index ≥30 kg/m2 lost 12.0 (SEM, 0.3) pounds, or 5.1% of body weight. Interaction with digital coaching was associated with greater reduction in all outcomes. CONCLUSIONS: A mobile health application-based cardiovascular risk self-management program was associated with favorable reductions in BP, TC, LDL-C, and weight, highlighting the potential use of this technology in comprehensive cardiovascular risk factor control.

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals.

PURPOSE: The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals. METHOD: In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team. RESULTS: Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care. CONCLUSIONS: The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.

Clinical use of artificial intelligence requires AI-capable organizations.

Artificial intelligence-based algorithms are being widely implemented in health care, even as evidence is emerging of bias in their design, problems with implementation, and potential harm to patients. To achieve the promise of using of AI-based tools to improve health, healthcare organizations will need to be AI-capable, with internal and external systems functioning in tandem to ensure the safe, ethical, and effective use of AI-based tools. Ideas are starting to emerge about the organizational routines, competencies, resources, and infrastructures that will be required for safe and effective deployment of AI in health care, but there has been little empirical research. Infrastructures that provide legal and regulatory guidance for managers, clinician competencies for the safe and effective use of AI-based tools, and learner-centric resources such as clear AI documentation and local health ecosystem impact reviews can help drive continuous improvement.

Diverse environmental bacteria displaying activity against Phakopsora pachyrhizi, the cause of soybean rust.

The management of soybean rust (SBR) caused by the obligate fungus Phakopsora pachyrhizi mostly relies on the use of synthetic fungicides, especially in areas where the disease inflicts serious yield losses. The reliance on synthetic fungicides to manage this disease has resulted in resistance of P. pachyrhizi populations to most fungicides. In this study, bacteria isolated from diverse environments were evaluated for their biocontrol potential against P. pachyrhizi using soybean detached-leaf method and on-plant in the growth chamber, greenhouse, and field. Among 998 bacterial isolates evaluated using the detached-leaf method; 58% were isolated from plant-related materials, 27% from soil, 10% from insects, and 5% from other environments. Of the isolates screened, 73 were active (they had ⪖ 75% rust reduction) with an active rate of 7.3%. From the active isolates, 65 isolates were re-tested on-plant in the growth chamber for activity confirmation. In the confirmation test, 49 bacteria isolated from plant-related materials maintained their activity with a confirmation rate of 75%. The majority of bacteria with confirmed activity belonged to the taxonomic classes Bacilli and Gammaproteobacteria (70%). Active isolates were prioritized for greenhouse and field testing based on activity in the initial screen and confirmation test. Six bacterial isolates AFS000009 (Pseudomonas_E chlororaphis), AFS032321 (Bacillus subtilis), AFS042929 (Bacillus_C megaterium), AFS065981 (Bacillus_X simplex_A), AFS090698 (Bacillus_A thuringiensis_S), and AFS097295 (Bacillus_A toyonensis) were selected from those bacteria that maintained activity in the confirmation test and were evaluated in the greenhouse, and five among them were evaluated in the field. From the Alabama field evaluation, all bacterial isolates reduced rust infection as well as azoxystrobin (Quadris® at 0.3 L/ha) used as the fungicide control (P > 0.05). Moreover, the scanning electron micrographs demonstrated evidence of antagonistic activity of AFS000009 and AFS032321 against P. pachyrhizi urediniospores. Bacterial isolates that consistently showed activity comparable to that of azoxystrobin can be improved through fermentation and formulation optimization, developed, and deployed. These bacteria strains would provide a valuable alternative to the synthetic fungicides and could play a useful role in integrated disease management programs for this disease.

Leveraging Clinical Informatics to Address the Quintuple Aim for End-of-Life Care.

As the population of older adults grows at an unprecedented rate, there is a large gap to provide culturally tailored end-of-life care. This study describes a payor-led, informatics-based approach to identify Medicare members who may benefit from a Compassionate CareSM Program (CCP), which was designed to provide specialized care management services and support to members who have end-stage and/or life-limiting illnesses by addressing the quintuple aim. Potential participants are identified through machine learning models whereby nurse care managers then provide tailored outreach via telephone. A retrospective, observational cohort analysis of propensity-weighted Medicare members was performed to compare decedents who did or did not participate in the CCP. This program enhanced the end-of-life care experience while providing equitable outcomes regardless of age, gender, and geography and decreased inpatient (-37%) admissions with concomitant reduced (-59%) medical spend when compared to decedents that did not utilize the end-of-life care management program.

Real-world Application of Racial and Ethnic Imputation and Cohort Balancing Techniques to Deliver Equitable Clinical Trial Recruitment.

Enhancing diversity and inclusion in clinical trial recruitment, especially for historically marginalized populations including Black, Indigenous, and People of Color individuals, is essential. This practice ensures that generalizable trial results are achieved to deliver safe, effective, and equitable health and healthcare. However, recruitment is limited by two inextricably linked barriers - the inability to recruit and retain enough trial participants, and the lack of diversity amongst trial populations whereby racial and ethnic groups are underrepresented when compared to national composition. To overcome these barriers, this study describes and evaluates a framework that combines 1) probabilistic and machine learning models to accurately impute missing race and ethnicity fields in real-world data including medical and pharmacy claims for the identification of eligible trial participants, 2) randomized controlled trial experimentation to deliver an optimal patient outreach strategy, and 3) stratified sampling techniques to effectively balance cohorts to continuously improve engagement and recruitment metrics.

Sex and cell-specific gene expression in corticolimbic brain regions associated with psychiatric disorders revealed by bulk and single-nuclei RNA sequencing.

BACKGROUND: There are sex-specific differences in the prevalence, symptomology and course of psychiatric disorders. However, preclinical models have primarily used males, such that the molecular mechanisms underlying sex-specific differences in psychiatric disorders are not well established. METHODS: In this study, we compared transcriptome-wide gene expression profiles in male and female rats within the corticolimbic system, including the cingulate cortex, nucleus accumbens medial shell (NAcS), ventral dentate gyrus and the basolateral amygdala (n = 22-24 per group/region). FINDINGS: We found over 3000 differentially expressed genes (DEGs) in the NAcS between males and females. Of these DEGs in the NAcS, 303 showed sex-dependent conservation DEGs in humans and were significantly enriched for gene ontology terms related to blood vessel morphogenesis and regulation of cell migration. Single nuclei RNA sequencing in the NAcS of male and female rats identified widespread sex-dependent expression, with genes upregulated in females showing a notable enrichment for synaptic function. Female upregulated genes in astrocytes, Drd3+MSNs and oligodendrocyte were also enriched in several psychiatric genome-wide association studies (GWAS). INTERPRETATION: Our data provide comprehensive evidence of sex- and cell-specific molecular profiles in the NAcS. Importantly these differences associate with anxiety, bipolar disorder, schizophrenia, and cross-disorder, suggesting an intrinsic molecular basis for sex-based differences in psychiatric disorders that strongly implicates the NAcS. FUNDING: This work was supported by funding from the Hope for Depression Research Foundation (MJM).

The development and implementation of a new hospital performance measure to assess hospital contributions to community health and equity.

OBJECTIVE: To develop and implement a measure of how US hospitals contribute to community health with a focus on equity. DATA SOURCES: Primary data from public comments and hospital surveys and secondary data from the IBM Watson Top 100 Hospitals program collected in the United States in 2020 and 2021. STUDY DESIGN: A thematic analysis of public comments on the proposed measure was conducted using an iterative grounded approach for theme identification. A cross-sectional survey of 207 hospitals was conducted to assess self-attestation to 28 community health best practice standards in the revised measure. An analysis of hospital rankings before and after inclusion of the new measure was performed. DATA COLLECTION/EXTRACTION METHODS: Public comment on the proposed measure was collected via an online survey, email, and virtual meetings in 2020. The survey of hospitals was conducted online by IBM in 2021. The analysis of hospital ranking compared the 2020 and 2021 IBM Watson Top 100 Hospitals program results. PRINCIPAL FINDINGS: More than 650 discrete comments from 83 stakeholders were received and analyzed during measure development. Key themes identified in thematic analysis included equity, fairness, and community priorities. Hospitals that responded to a cross-sectional survey reported meeting on average 76% of applicable best practice standards. Least met standards included providing emergent buprenorphine treatment for opioid use disorder (53%), supporting an evidence-based home visiting program (53%), and establishing a returning citizens employment program (27%). Thirty-seven hospitals shifted position in the 100 Top Hospital rankings after the inclusion of the new measure. CONCLUSIONS: There is broad interest in measuring hospital contributions to community health with a focus on equity. Many highly ranked hospitals report meeting best practice standards, but significant gaps remain. Improving measurement to incentivize greater hospital contributions to community health and equity is an important priority.

Digital Health Interventions to Enhance Prevention in Primary Care: Scoping Review.

BACKGROUND: Disease prevention is a central aspect of primary care practice and is comprised of primary (eg, vaccinations), secondary (eg, screenings), tertiary (eg, chronic condition monitoring), and quaternary (eg, prevention of overmedicalization) levels. Despite rapid digital transformation of primary care practices, digital health interventions (DHIs) in preventive care have yet to be systematically evaluated. OBJECTIVE: This review aimed to identify and describe the scope and use of current DHIs for preventive care in primary care settings. METHODS: A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and Medical Subject Headings terms covering primary care professionals, prevention and care management, and digital health. A subgroup analysis identified relevant studies conducted in US primary care settings, excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (eg, health care performance and implementation science), and study quality as per Oxford levels of evidence were abstracted. RESULTS: The search yielded 5274 citations, of which 1060 full-text articles were identified. Following a subgroup analysis, 241 articles met the inclusion criteria. Studies primarily examined DHIs among health information technologies, including EHRs (166/241, 68.9%), clinical decision support (88/241, 36.5%), telehealth (88/241, 36.5%), and multiple technologies (154/241, 63.9%). DHIs were predominantly used for tertiary prevention (131/241, 54.4%). Of the core primary care functions, comprehensiveness was addressed most frequently (213/241, 88.4%). DHI users were providers (205/241, 85.1%), patients (111/241, 46.1%), or multiple types (89/241, 36.9%). Reported outcomes were primarily clinical (179/241, 70.1%), and statistically significant improvements were common (192/241, 79.7%). Results were summarized across the following 5 topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. The quality of the included studies was moderate to low. CONCLUSIONS: Preventive DHIs in primary care settings demonstrated meaningful improvements in both clinical and nonclinical outcomes, and across user types; however, adoption and implementation in the US were limited primarily to EHR platforms, and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluations of negative results, effects on health disparities, and many other gaps remain to be explored.

Considering Clinician Competencies for the Implementation of Artificial Intelligence-Based Tools in Health Care: Findings From a Scoping Review.

BACKGROUND: The use of artificial intelligence (AI)-based tools in the care of individual patients and patient populations is rapidly expanding. OBJECTIVE: The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings. METHODS: A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States. RESULTS: The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians. CONCLUSIONS: While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap.