ABC | Volume 112, Nº4, April 2019

Editorial Toward a Patient-Centered, Data-Driven Cardiology Antonio Luiz Ribeiro 1 and Gláucia Maria Moraes de Oliveira 2 Universidade Federal de Minas Gerais, 1 Belo Horizonte, MG – Brazil Universidade Federal do Rio de Janeiro, 2 Rio de Janeiro, RJ – Brazil Mailing Address: Gláucia Maria Moraes de Oliveira • Universidade Federal do Rio de Janeiro – R. Prof. Rodolpho P. Rocco, 255 – 8°. Andar – Sala 6, UFRJ. Postal Code 21941-913, Cidade Universitária, RJ – Brazil E-mail: glauciam@cardiol.br , glauciamoraesoliveira@gmail.com Keywords Cardiology; Clinical Decision - Making; Patient Centered Care; Evidence-Based Medicine/methods; Access Health Technologies; Artificial Intelligence; Diagnostic Equipment Digital; Machine Learning/trends; Health Manager; Physician Patient Relations. DOI: 10.5935/abc.20190069 Beginning in the 1970s and 1980s, the emergence of randomized clinical trials and studies with large cohorts, associated with the development of the methodology for systematic reviews and meta-analyses, triggered a revolution in the way of thinking and performing healthcare practice. Evidence-based medicine (EBM), defined as the integration of the best research evidence with clinical experience and patient values, 1 has become a new paradigm, orienting medical education and specialized publications. One of the principles of EBM was precisely the primacy of information obtained from randomized clinical trials and meta-analyses, which were placed at the top of an evidence hierarchy, valuing quantitative results more than clinical experience and expert opinion. Indeed, it has always been challenging for EBM to integrate empirical evidence with other types of medical knowledge, such as clinical expertise and pathophysiological rationale, or even with the preferences of individual patients. 2 The use of EBM in clinical practice also runs into the difficulty of finding robust evidence for all subgroups of clinical situations found in the real world, "gray areas" in which no reliable evidence can be obtained from the scientific literature to guide the physician in caring for his patient. Randomized clinical trials are expensive and generally require large study samples and long-term follow-up. There are several situations without evidence, or situations in which the evidence is inconsistent or of poor quality. 3 In the last two decades, the use of digital technology has invaded daily life worldwide and radically changed the way people live and relate, with a direct impact on healthcare practice. Public and private information systems and administrative record systems in healthcare practice have become ubiquitous and increasingly complex and complete, storing information ranging from diseases of compulsory notification to reasons for hospitalization and cause of death. Diagnostic equipment has become digital, and electronic medical records began to accumulate the patients’ clinical information, prescribed medications, and laboratory tests. Smartphones and digital devices began tracking physical activity or recording an individual's diet, in a myriad of applications and software, including information sharing on social networks. Computational advances also allowed the emergence of bioinformatics, with the attainment of a large volume of genetic information, as well as information about proteins, hormones, and other substances present in the body. The availability of this huge amount of data and new analytical techniques – big data analytics 4 – opens up new scientific possibilities promising to bring about a real revolution in healthcare practice. Artificial intelligence (AI) areas, such as machine learning and data mining , allow for interactive interpretation and apprehension of the unstructured information available in large databases, recognizing hidden patterns of combination of information that are not obtained with traditional statistical methods. 5 AI‑based methods are being increasingly applied to cardiology to diagnose combinations of multiple imaging modalities, biobanks, electronic cohorts remote and on‑site clinical sensors for monitoring of chronic pathologies, electronic health records, and genomes and other molecular techniques, among others 6 (Table 1). The complete sequencing of the genome and exome, already available in multiple centers, and the future sequencing of the proteome, transcriptome, and metabolome may lead to the knowledge of biological differences among individuals, contextualizing the observed phenotypes with their molecular characterization, leading to the modulation of treatment for specific targets, with greater safety and precision, in the so‑called precision medicine. 7 This perspective of transformation of how knowledge is generated and applied, from the use of new data sources and analysis methodologies, has the potential to bring a new paradigm to medical and healthcare practice (Table 1). 8-13 However, the use of this large volume of data by healthcare managers and professionals for planning of actions in healthcare and direct patient care is still a major challenge. Difficulties and risks cannot be underestimated. 14,15 Studies on AI are usually based on observational data obtained from administrative databases or medical records, with the potential for different types of biases and confounding factors. The associations obtained rarely meet the criteria of causality, and well-designed and long-running studies will continue to be necessary for proving hypotheses and defining causality. On the other hand, most algorithms used work with the "black box" principle, without allowing the information user to know the reasons why a diagnosis or recommendation was generated, which can be a problem, especially if the algorithms were designed for a different environment than the one that the user's patient is inserted. Issues regarding information ethics, privacy, and security are still far from being resolved. Matters regarding the cost and cost-effectiveness of healthcare AI projects should be considered early, given 371