Dataset Information

Big data in digital healthcare: lessons learnt and recommendations for general practice.

ABSTRACT: Big Data will be an integral part of the next generation of technological developments-allowing us to gain new insights from the vast quantities of data being produced by modern life. There is significant potential for the application of Big Data to healthcare, but there are still some impediments to overcome, such as fragmentation, high costs, and questions around data ownership. Envisioning a future role for Big Data within the digital healthcare context means balancing the benefits of improving patient outcomes with the potential pitfalls of increasing physician burnout due to poor implementation leading to added complexity. Oncology, the field where Big Data collection and utilization got a heard start with programs like TCGA and the Cancer Moon Shot, provides an instructive example as we see different perspectives provided by the United States (US), the United Kingdom (UK) and other nations in the implementation of Big Data in patient care with regards to their centralization and regulatory approach to data. By drawing upon global approaches, we propose recommendations for guidelines and regulations of data use in healthcare centering on the creation of a unique global patient ID that can integrate data from a variety of healthcare providers. In addition, we expand upon the topic by discussing potential pitfalls to Big Data such as the lack of diversity in Big Data research, and the security and transparency risks posed by machine learning algorithms.

SUBMITTER: Agrawal R

PROVIDER: S-EPMC7080757 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Big data in digital healthcare: lessons learnt and recommendations for general practice.

Agrawal Raag R Prabakaran Sudhakaran S

Heredity 20200305 4

Big Data will be an integral part of the next generation of technological developments-allowing us to gain new insights from the vast quantities of data being produced by modern life. There is significant potential for the application of Big Data to healthcare, but there are still some impediments to overcome, such as fragmentation, high costs, and questions around data ownership. Envisioning a future role for Big Data within the digital healthcare context means balancing the benefits of improvi ...[more]

PMID: 32139886

Similar Datasets

Project description:BACKGROUND:The treatment of multimorbid patients is one crucial task in general practice as multimorbidity is highly prevalent in this setting. However, there is little evidence how to treat these patients and consequently there are but a few guidelines that focus primarily on multimorbidity. Big data analytics are defined as a method that obtains results for high volume data with high variety generated at high velocity. Yet, the explanatory power of these results is not completely understood. Nevertheless, addressing multimorbidity as a complex condition might be a promising field for big data analytics. The aim of this scoping review was to evaluate whether applying big data analytics on patient data does already contribute to the treatment of multimorbid patients in general practice. METHODS:In January 2018, a review searching the databases PubMed, The Cochrane Library, and Web of Science, using defined search terms for "big data analytics" and "multimorbidity", supplemented by a search of grey literature with Google Scholar, was conducted. Studies were not filtered by type of study, publication year or language. Validity of studies was evaluated independently by two researchers. RESULTS:In total, 2392 records were identified for screening. After title and abstract screening, six articles were included in the full-text analysis. Of those articles, one reported on a model generated with big data techniques to help caring for one group of multimorbid patients. The other five articles dealt with the analysis of multimorbidity clusters. No article defined big data analytics explicitly. CONCLUSIONS:Although the usage of the phrase "Big Data" is growing rapidly, there is nearly no practical use case for big data analysis techniques in the treatment of multimorbidity in general practice yet. Furthermore, in publications addressing big data analytics, the term is rarely defined. However, possible models and algorithms to address multimorbidity in the future are already published.

Project description:BackgroundRecommendations to prevent morbidity and mortality in children was a high priority for the editorial group of a WHO pocket book for primary health care in the European region. However, the benefit of preventive interventions is not always clear and recommendations differ across countries and institutions. Here, we summarize the existing recommendations and the most recent evidence on ten selected preventive interventions applied to children under five years to inform this group. In addition, we reflect on the process and challenges of developing these summaries.MethodsFor each intervention, we systematically searched for current recommendations from the WHO, the United States Preventive Services Task Force, the workgroup PrevInfad from the Spanish Association of Primary Care Pediatrics, the Centers of Disease Control and Prevention, and the National Institute for Health and Care Excellence. Then, we systematically searched the sources above and the Cochrane library for relevant systematic reviews. For each topic, we reported the recommendations and the strength of the recommendation when and as reported by the authors. We summarized the main findings of systematic reviews with the certainty of the evidence as reported. Categorising the ten preventive interventions in three groups allowed narrative comparisons between similar types of interventions and between groups.Results and discussionFor the single interventions of providing vitamins D and K and topical fluoride there is overall a high degree of consensus between institutions for the evidence of their effectiveness. For the multiple interventions to prevent sudden infant death syndrome and unintentional injuries consensus was more variable as evidence of effectiveness is harder to ascertain. For the screening interventions the summaries of recommendations and evidence varied too. While institutions generally agreed in recommending for vision screening and against universal screening for language and speech delay and iron deficiency, they had some differences for pulse oximetry and autism. The transparent and independent process shed light upon how institutions use existing evidence in their settings - common and different positions were accounted for and became visible. We also identified gaps and duplications of research. Our approach was a crucial starting point for developing the respective sections in the pocket book.

Dataset Information

Big data in digital healthcare: lessons learnt and recommendations for general practice.

Publications

Big data in digital healthcare: lessons learnt and recommendations for general practice.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets