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Each decade, the American Heart Association (AHA) develops an Impact Goal to guide its overall strategic direction and investments in its research, quality improvement, advocacy, and public health programs. Guided by the AHA's new Mission Statement, to be a relentless force for a world of longer, healthier lives, the 2030 Impact Goal is anchored in an understanding that to achieve cardiovascular health for all, the AHA must include a broader vision of health and well-being and emphasize health equity. In the next decade, by 2030, the AHA will strive to equitably increase healthy life expectancy beyond current projections, with global and local collaborators, from 66 years of age to at least 68 years of age across the United States and from 64 years of age to at least 67 years of age worldwide. The AHA commits to developing additional targets for equity and well-being to accompany this overarching Impact Goal. To attain the 2030 Impact Goal, we recommend a thoughtful evaluation of interventions available to the public, patients, providers, healthcare delivery systems, communities, policy makers, and legislators. This presidential advisory summarizes the task force's main considerations in determining the 2030 Impact Goal and the metrics to monitor progress. It describes the aspiration that these goals will be achieved by working with a diverse community of volunteers, patients, scientists, healthcare professionals, and partner organizations needed to ensure success.
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American Heart Association , Enfermedades Cardiovasculares/epidemiología , Enfermedades Cardiovasculares/prevención & control , Salud Global , Formulación de Políticas , Vigilancia de la Población , Servicios Preventivos de Salud/normas , Anciano , Enfermedades Cardiovasculares/diagnóstico , Enfermedades Cardiovasculares/mortalidad , Estado de Salud , Humanos , Persona de Mediana Edad , Medición de Riesgo , Factores de Riesgo , Factores de Tiempo , Estados Unidos/epidemiologíaRESUMEN
BACKGROUND: Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added. METHODS: We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care. RESULTS: A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes. CONCLUSIONS: The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.).
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Pruebas Genéticas , Enfermedades Raras/genética , Análisis de Secuencia de ADN , Adulto , Animales , Niño , Diagnóstico Diferencial , Drosophila , Exoma , Femenino , Pruebas Genéticas/economía , Costos de la Atención en Salud/estadística & datos numéricos , Humanos , Masculino , Modelos Animales , National Institutes of Health (U.S.) , Enfermedades Raras/diagnóstico , Síndrome , Estados UnidosRESUMEN
PURPOSE: The NIH Undiagnosed Diseases Network (UDN) evaluates participants with disorders that have defied diagnosis, applying personalized clinical and genomic evaluations and innovative research. The clinical sites of the UDN are essential to advancing the UDN mission; this study assesses their contributions relative to standard clinical practices. METHODS: We analyzed retrospective data from four UDN clinical sites, from July 2015 to September 2019, for diagnoses, new disease gene discoveries and the underlying investigative methods. RESULTS: Of 791 evaluated individuals, 231 received 240 diagnoses and 17 new disease-gene associations were recognized. Straightforward diagnoses on UDN exome and genome sequencing occurred in 35% (84/240). We considered these tractable in standard clinical practice, although genome sequencing is not yet widely available clinically. The majority (156/240, 65%) required additional UDN-driven investigations, including 90 diagnoses that occurred after prior nondiagnostic exome sequencing and 45 diagnoses (19%) that were nongenetic. The UDN-driven investigations included complementary/supplementary phenotyping, innovative analyses of genomic variants, and collaborative science for functional assays and animal modeling. CONCLUSION: Investigations driven by the clinical sites identified diagnostic and research paradigms that surpass standard diagnostic processes. The new diagnoses, disease gene discoveries, and delineation of novel disorders represent a model for genomic medicine and science.
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Enfermedades no Diagnosticadas , Animales , Genómica , Humanos , Enfermedades Raras/diagnóstico , Enfermedades Raras/genética , Estudios Retrospectivos , Secuenciación del ExomaRESUMEN
There is great value in understanding the patient perspective in rare disease diagnosis and research, and in partnering actively with patients and their families throughout the process. Meaningful and respectful interaction between patients and researchers leads to learning on both sides, and ultimately, to better research outcomes. Researchers can help patients understand how research is conducted and what the latest advances and perceived gaps in research are, and patients, who have direct experience living with their health conditions, can impart to researchers what is most important to them. We describe our engagement with patients in the Undiagnosed Diseases Network (UDN) program, as well as the lessons we have learned to date. In the UDN, patients have been instrumental in bringing meaning to the work of clinicians and researchers, building patient communities, making the network aware of unmet patient needs, advocating for additional research funding, and disseminating UDN research findings. Although patient engagement in the UDN has already had a significant positive impact on our work, we continue to strive to involve patients earlier in the process, in the research design itself, and in addressing power dynamics that may arise between clinicians, researchers, and patients.
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Enfermedades Raras , Humanos , Enfermedades Raras/diagnósticoRESUMEN
Diagnosis at the edges of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in unprecedented ways. Exact disease recognition, an element of the concept of precision in medicine, requires new infrastructure that spans geography, institutional boundaries, and the divide between clinical care and research. The National Institutes of Health (NIH) Common Fund supports the Undiagnosed Diseases Network (UDN) as an exemplar of this model of precise diagnosis. Its goals are to forge a strategy to accelerate the diagnosis of rare or previously unrecognized diseases, to improve recommendations for clinical management, and to advance research, especially into disease mechanisms. The network will achieve these objectives by evaluating patients with undiagnosed diseases, fostering a breadth of expert collaborations, determining best practices for translating the strategy into medical centers nationwide, and sharing findings, data, specimens, and approaches with the scientific and medical communities. Building the UDN has already brought insights to human and medical geneticists. The initial focus has been on data sharing, establishing common protocols for institutional review boards and data sharing, creating protocols for referring and evaluating patients, and providing DNA sequencing, metabolomic analysis, and functional studies in model organisms. By extending this precision diagnostic model nationally, we strive to meld clinical and research objectives, improve patient outcomes, and contribute to medical science.
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Enfermedades Raras/diagnóstico , Enfermedades Raras/genética , Genotipo , Técnicas de Genotipaje , Humanos , Difusión de la Información/métodos , Metabolómica , National Institutes of Health (U.S.) , Fenotipo , Análisis de Secuencia de ADN , Estados UnidosRESUMEN
BACKGROUND: The majority of undiagnosed diseases manifest with objective findings that warrant further investigation. The Undiagnosed Diseases Network (UDN) receives applications from patients whose symptoms and signs have been intractable to diagnosis; however, many UDN applicants are affected primarily by subjective symptoms such as pain and fatigue. We sought to characterize presenting symptoms, referral sources, and demographic factors of applicants to the UDN to identify factors that may determine application outcome and potentially differentiate between those with undiagnosed diseases (with more objective findings) and those who are less likely to have an undiagnosed disease (more subjective symptoms). METHODS: We used a systematic retrospective review of 151 consecutive Not Accepted and 50 randomly selected Accepted UDN applications. The primary outcome was whether an applicant was Accepted, or Not Accepted, and, if accepted, whether or not a diagnosis was made. Objective and subjective symptoms and information on prior specialty consultations were collected from provider referral letters. Demographic data and decision data on network acceptance were gathered from the UDN online portal. RESULTS: Fewer objective findings and more subjective symptoms were found in the Not Accepted applications. Not Accepted referrals also were from older individuals, reported a shorter period of illness, and were referred to the UDN by their primary care physicians. All of these differences reached statistical significance in comparison with Accepted applications. The frequency of subspecialty consults for diagnostic purposes prior to UDN application was similar in both groups. CONCLUSIONS: The preponderance of subjective and lack of objective findings in the Not Accepted applications distinguish these from applicants that are accepted for evaluation and diagnostic efforts through the UDN. Not Accepted applicants are referred primarily by their primary care providers after multiple specialist consultations fail to yield answers. Distinguishing between patients with undiagnosed diseases with objective findings and those with primarily subjective findings can delineate patients who would benefit from further diagnostic processes from those who may have functional disorders and need alternative pathways for management of their symptoms. TRIAL REGISTRATION: clinicaltrials.gov NCT02450851 , posted May 21st 2015.
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Manejo de la Enfermedad , Enfermedades Raras/diagnóstico , Derivación y Consulta/organización & administración , Adolescente , Adulto , Toma de Decisiones , Femenino , Programas de Gobierno , Humanos , Masculino , Persona de Mediana Edad , National Institutes of Health (U.S.) , Estudios Retrospectivos , Estados Unidos , Adulto JovenRESUMEN
BACKGROUND: The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note. METHODS: We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets - clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms. We evaluated the performance of classifiers and their portability across the two datasets. RESULTS: The convolutional recurrent neural network with neural word embeddings trained-medical subdomain classifier yielded the best performance measurement on iDASH and MGH datasets with area under receiver operating characteristic curve (AUC) of 0.975 and 0.991, and F1 scores of 0.845 and 0.870, respectively. Considering better clinical interpretability, linear support vector machine-trained medical subdomain classifier using hybrid bag-of-words and clinically relevant UMLS concepts as the feature representation, with term frequency-inverse document frequency (tf-idf)-weighting, outperformed other shallow learning classifiers on iDASH and MGH datasets with AUC of 0.957 and 0.964, and F1 scores of 0.932 and 0.934 respectively. We trained classifiers on one dataset, applied to the other dataset and yielded the threshold of F1 score of 0.7 in classifiers for half of the medical subdomains we studied. CONCLUSION: Our study shows that a supervised learning-based NLP approach is useful to develop medical subdomain classifiers. The deep learning algorithm with distributed word representation yields better performance yet shallow learning algorithms with the word and concept representation achieves comparable performance with better clinical interpretability. Portable classifiers may also be used across datasets from different institutions.
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Toma de Decisiones Clínicas , Aprendizaje Automático , Registros Médicos , Procesamiento de Lenguaje Natural , Unified Medical Language System , HumanosRESUMEN
The American College of Medical Informatics (ACMI) periodically hosts a debate at the American Medical Informatics Association (AMIA) fall symposium on a timely topic in biomedical informatics. In 2014 a panel of ACMI fellows debated the following proposition: "The lack of interaction and collaboration between health IT vendors and academic clinical informatics units is stifling innovation and will continue to have a detrimental effect on the evolution of commercial products." Debaters disagreed on the level of interaction and collaboration between the health IT sector and academia and disagreed on whether and by whom innovation was actually taking place. While collaboration between industry and academia was seen as desirable by all of the debaters, there was an acknowledgment that these groups have notably different roles and responsibilities. There was consensus that a path forward should be found, and that AMIA itself has an important role to play in effecting this.
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Informática Médica/métodos , Informática Médica/organización & administración , Sociedades Médicas , Programas Informáticos/economía , Acceso a la Información , Comercio , Información de Salud al Consumidor , Conducta Cooperativa , Difusión de Innovaciones , Política de Salud , Humanos , Sistemas de Registros Médicos Computarizados , Estados Unidos , UniversidadesRESUMEN
This article presents the research process and methods used to evaluate and improve a web-based health information resource, called "Community Connect to Research," intended for the public. The research process was iterative and involved collaboration with many partners. Two formal evaluations were conducted in 2009 and 2010 using key informant interviews, usability interviews, focus groups, an online survey, and readability and suitability assessment tools. These methods provided users' perspectives on the overall design, content, and literacy demands of the website as well as valuable feedback on their interaction with the website. The authors subsequently redesigned Community Connect to Research, making significant improvements on the basis of what they learned from the evaluation. The second evaluation revealed that the redesign addressed many issues found in the first evaluation and identified additional areas of possible improvement. Overall, both evaluations suggested that participants believed that the website was useful and valuable, indicating that Community Connect to Research is a health information resource that provides patients and families with accessible, relevant, and high-quality information. Regular formal evaluation is an essential tool for effective ongoing enhancement of health information resources meant for the public.
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Participación de la Comunidad , Comportamiento del Consumidor/estadística & datos numéricos , Comunicación en Salud/métodos , Internet , Adulto , Femenino , Grupos Focales , Alfabetización en Salud , Humanos , Masculino , Persona de Mediana Edad , Investigación Cualitativa , Interfaz Usuario-ComputadorRESUMEN
INTRODUCTION: The Undiagnosed Diseases Network (UDN), a clinical research study funded by the National Institutes of Health, aims to provide answers for patients with undiagnosed conditions and generate knowledge about underlying disease mechanisms. UDN evaluations involve collaboration between clinicians and researchers and go beyond what is possible in clinical settings. While medical and research outcomes of UDN evaluations have been explored, this is the first formal assessment of the patient and caregiver experience. METHODS: We invited UDN participants and caregivers to participate in focus groups via email, newsletter, and a private participant Facebook group. We developed focus group questions based on research team expertise, literature focused on patients with rare and undiagnosed conditions, and UDN participant and family member feedback. In March 2021, we conducted, recorded, and transcribed four 60-min focus groups via Zoom. Transcripts were evaluated using a thematic analysis approach. RESULTS: The adult undiagnosed focus group described the UDN evaluation as validating and an avenue for access to medical providers. They also noted that the experience impacted professional choices and helped them rely on others for support. The adult diagnosed focus group described the healthcare system as not set up for rare disease. In the pediatric undiagnosed focus group, caregivers discussed a continued desire for information and gratitude for the UDN evaluation. They also described an ability to rule out information and coming to terms with not having answers. The pediatric diagnosed focus group discussed how the experience helped them focus on management and improved communication. Across focus groups, adults (undiagnosed/diagnosed) noted the comprehensiveness of the evaluation. Undiagnosed focus groups (adult/pediatric) discussed a desire for ongoing communication and care with the UDN. Diagnosed focus groups (adult/pediatric) highlighted the importance of the diagnosis they received in the UDN. The majority of the focus groups noted a positive future orientation after participation. CONCLUSION: Our findings are consistent with prior literature focused on the patient experience of rare and undiagnosed conditions and highlight benefits from comprehensive evaluations, regardless of whether a diagnosis is obtained. Focus group themes also suggest areas for improvement and future research related to the diagnostic odyssey.
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Atención a la Salud , Familia , Adulto , Humanos , Niño , Enfermedades Raras/diagnóstico , Grupos Focales , Evaluación del Resultado de la Atención al PacienteRESUMEN
BACKGROUND: Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. RESULTS: In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. CONCLUSION: A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.
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Bases de Datos Genéticas , Cadenas de Markov , Vocabulario Controlado , Animales , Perfilación de la Expresión Génica , Regulación de la Expresión Génica , Humanos , Anotación de Secuencia Molecular , Enfermedad de Parkinson/genética , ProbabilidadRESUMEN
OBJECTIVE: Many studies have documented significant associations between religion and spirituality (R/S) and health, but relatively few prospective analyses exist that can support causal inferences. To date, there has been no systematic analysis of R/S survey items collected in US cohort studies. We conducted a systematic content analysis of all surveys ever fielded in 20 diverse US cohort studies funded by the National Institutes of Health (NIH) to identify all R/S-related items collected from each cohort's baseline survey through 2014. DESIGN: An R|S Ontology was developed from our systematic content analysis to categorise all R/S survey items identified into key conceptual categories. A systematic literature review was completed for each R/S item to identify any cohort publications involving these items through 2018. RESULTS: Our content analysis identified 319 R/S survey items, reflecting 213 unique R/S constructs and 50 R|S Ontology categories. 193 of the 319 extant R/S survey items had been analysed in at least one published paper. Using these data, we created the R|S Atlas (https://atlas.mgh.harvard.edu/), a publicly available, online relational database that allows investigators to identify R/S survey items that have been collected by US cohorts, and to further refine searches by other key data available in cohorts that may be necessary for a given study (eg, race/ethnicity, availability of DNA or geocoded data). CONCLUSIONS: R|S Atlas not only allows researchers to identify available sources of R/S data in cohort studies but will also assist in identifying novel research questions that have yet to be explored within the context of US cohort studies.
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Investigadores , Espiritualidad , Estudios de Cohortes , Humanos , Estudios Prospectivos , Religión , Encuestas y CuestionariosRESUMEN
OBJECTIVES: This paper proposes a novel semantic method for auditing associative relations in biomedical terminologies. We tested our methodology on two Unified Medical Language System (UMLS) knowledge sources. METHODS: We use the UMLS semantic groups as high-level representations of the domain and range of relationships in the Metathesaurus and in the Semantic Network. A mapping created between Metathesaurus relationships and Semantic Network relationships forms the basis for comparing the signatures of a given Metathesaurus relationship to the signatures of the semantic relationship to which it is mapped. The consistency of Metathesaurus relations is studied for each relationship. RESULTS: Of the 177 associative relationships in the Metathesaurus, 84 (48%) exhibit a high-degree of consistency with the corresponding Semantic Network relationships. Overall, 63% of the 1.8 M associative relations in the Metathesaurus are consistent with relations in the Semantic Network. CONCLUSION: The semantics of associative relationships in biomedical terminologies should be defined explicitly by their developers. The Semantic Network would benefit from being extended with new relationships and with new relations for some existing relationships. The UMLS editing environment could take advantage of the correspondence established between relationships in the Metathesaurus and the Semantic Network. Finally, the auditing method also yielded useful information for refining the mapping of associative relationships between the two sources.
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Conocimiento , Auditoría Administrativa , Informática Médica , Unified Medical Language SystemRESUMEN
BACKGROUND: Medical informatics, or biomedical and health informatics (BMHI), has become an established scientific discipline. In all such disciplines there is a certain inertia to persist in focusing on well-established research areas and to hold on to well-known research methodologies rather than adopting new ones, which may be more appropriate. OBJECTIVES: To search for answers to the following questions: What are research fields in informatics, which are not being currently adequately addressed, and which methodological approaches might be insufficiently used? Do we know about reasons? What could be consequences of change for research and for education? METHODS: Outstanding informatics scientists were invited to three panel sessions on this topic in leading international conferences (MIE 2015, Medinfo 2015, HEC 2016) in order to get their answers to these questions. RESULTS: A variety of themes emerged in the set of answers provided by the panellists. Some panellists took the theoretical foundations of the field for granted, while several questioned whether the field was actually grounded in a strong theoretical foundation. Panellists proposed a range of suggestions for new or improved approaches, methodologies, and techniques to enhance the BMHI research agenda. CONCLUSIONS: The field of BMHI is on the one hand maturing as an academic community and intellectual endeavour. On the other hand vendor-supplied solutions may be too readily and uncritically accepted in health care practice. There is a high chance that BMHI will continue to flourish as an important discipline; its innovative interventions might then reach the original objectives of advancing science and improving health care outcomes.
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Investigación Biomédica/organización & administración , Informática Médica/organización & administración , Modelos Organizacionales , Objetivos Organizacionales , Proyectos de Investigación , Ciencia/organización & administraciónRESUMEN
Throughout history, philosophers, scientists, and other scholars have named and organized the salient elements of the world. These efforts have led to conceptualizations that differ widely in both content and form. This paper argues that all conceptualizations are biased, both because they depend on the purposes for which they have been created, and because they are closely tied to the world view of their designers. This bias needs to be recognized, and its consequences need to be addressed if the conceptualizations are to be used for purposes other than those for which they were designed. The paper begins with a brief overview of the disciplines that have been concerned with conceptualizing particular domains. This is followed by a non-exhaustive, illustrative, historical perspective, and the paper concludes with the interesting case study of biological taxonomy.
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Biología Computacional/métodos , Computadores , Historia , Humanos , Conocimiento , Metafisica , Filosofía , Ciencia , Terminología como AsuntoRESUMEN
This report reviews some of the extensive literature in health literacy, much of it focused on the intersection of low literacy and the understanding of basic health care information. Several articles describe methods for assessing health literacy as well as methods for assessing the readability of texts, although generally these latter have not been developed with health materials in mind. Other studies have looked more closely at the mismatch between patients' literacy levels and the readability of materials intended for use by those patients. A number of studies have investigated the phenomenon of literacy from the perspective of patients' interactions in the health care setting, the disenfranchisement of some patients because of their low literacy skills, the difficulty some patients have in navigating the health care system, the quality of the communication between doctors and their patients including the cultural overlay of such exchanges, and ultimately the effect of low literacy on health outcomes. Finally, the impact of new information technologies has been studied by a number of investigators. There remain many opportunities for conducting further research to gain a better understanding of the complex interactions between general literacy, health literacy, information technologies, and the existing health care infrastructure.
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Comprensión , Educación en Salud , Lectura , Adulto , Escolaridad , Educación en Salud/normas , Humanos , Estados UnidosRESUMEN
BACKGROUND: A 1.5-day interactive forum was convened to discuss critical issues in the acquisition, analysis, and sharing of data in the field of cardiovascular and stroke science. The discussion will serve as the foundation for the American Heart Association's (AHA's) near-term and future strategies in the Big Data area. The concepts evolving from this forum may also inform other fields of medicine and science. METHODS AND RESULTS: A total of 47 participants representing stakeholders from 7 domains (patients, basic scientists, clinical investigators, population researchers, clinicians and healthcare system administrators, industry, and regulatory authorities) participated in the conference. Presentation topics included updates on data as viewed from conventional medical and nonmedical sources, building and using Big Data repositories, articulation of the goals of data sharing, and principles of responsible data sharing. Facilitated breakout sessions were conducted to examine what each of the 7 stakeholder domains wants from Big Data under ideal circumstances and the possible roles that the AHA might play in meeting their needs. Important areas that are high priorities for further study regarding Big Data include a description of the methodology of how to acquire and analyze findings, validation of the veracity of discoveries from such research, and integration into investigative and clinical care aspects of future cardiovascular and stroke medicine. Potential roles that the AHA might consider include facilitating a standards discussion (eg, tools, methodology, and appropriate data use), providing education (eg, healthcare providers, patients, investigators), and helping build an interoperable digital ecosystem in cardiovascular and stroke science. CONCLUSION: There was a consensus across stakeholder domains that Big Data holds great promise for revolutionizing the way cardiovascular and stroke research is conducted and clinical care is delivered; however, there is a clear need for the creation of a vision of how to use it to achieve the desired goals. Potential roles for the AHA center around facilitating a discussion of standards, providing education, and helping establish a cardiovascular digital ecosystem. This ecosystem should be interoperable and needs to interface with the rapidly growing digital object environment of the modern-day healthcare system.
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Acceso a la Información , Investigación Biomédica/organización & administración , Cardiología/organización & administración , Enfermedades Cardiovasculares , Minería de Datos , Bases de Datos Factuales , Difusión de la Información , Accidente Cerebrovascular , American Heart Association , Investigación Biomédica/tendencias , Cardiología/tendencias , Enfermedades Cardiovasculares/diagnóstico , Enfermedades Cardiovasculares/etiología , Enfermedades Cardiovasculares/terapia , Consenso , Conducta Cooperativa , Minería de Datos/tendencias , Bases de Datos Factuales/tendencias , Difusión de Innovaciones , Predicción , Humanos , Comunicación Interdisciplinaria , Accidente Cerebrovascular/diagnóstico , Accidente Cerebrovascular/etiología , Accidente Cerebrovascular/terapia , Estados UnidosRESUMEN
The authors have developed the Genetics Home Reference, a consumer resource that addresses the health implications of the Human Genome Project. The research results made possible by the Human Genome Project are being made available increasingly in scientific databases on the Internet, but, because of the often highly technical nature of these databases, they are not readily accessible to the lay public. The authors' goal is to provide a bridge between the clinical questions of the public and the richness of the data emanating from the Human Genome Project. The Genetics Home Reference currently focuses on single gene or polygenic conditions that are also topics on MEDLINEplus, the National Library of Medicine's primary consumer health site. As knowledge of genetics expands, the interrelationships between genes and diseases will continue to unfold, and the site will reflect these developments.