ПЕРСПЕКТИВИ ТА ПРОБЛЕМИ ВИКОРИСТАННЯ ТЕХНОЛОГІЙ BIG DATA В МЕДИЦИНІ

Background. The future of medicine offers a personalized multimodal approach, focused on the patient, integrated care, intelligent decision support systems for doctors, telemedicine. The solution to these problems can be achieved by Big Data technologies, although their use is controversial. Materials and methods. The analysis of databases Scopus, Web of Science, Ulrich's Periodicals, eLIBRARY.RU, Google Scholar, PubMed, Medline, EMBASE, EconLit, Cochrane Library, UpToDate, ACP Journal Club, HINARI, http://www.meta. ua, http://www.nbuv.gov.ua, etc. for the period from 2007 to 2019 for the keywords "Big Data", "medicine" was made. Results. It is shown that the goals of using Big Data are to create the most complete registers of medical data exchanging information with each other, use the accumulated information to predict the possibility of the development of diseases and their prevention for each patient, prevent epidemics, create a pricing and payment system, new business models, the use of predictive modeling in the development of drugs, the introduction of electronic patient records that would be available to everyone his doctor, which allows the introduction of personalized medicine. The main Big Data processing technologies are NoSQL, MapReduce, Hadoop, R, hardware solutions. It is proved that the use of Big Data technologies in medicine can be achieved with the widespread use of digital presentation of biomedical information, the feasibility and necessity of ensuring its prompt transmission, including via mobile communications, are shown, unresolved issues in the application of Big Data are indicated (unstructured, syntactic and semantic data problems, redundancy and risk of information distortion, incomplete compliance with the requirements of evidence-based medicine, legal, moral and ethical, insurance aspects, the inadequacy of traditional security mechanisms such as firewalls and anti-virus software). Conclusions. The data presented indicate the promise of using these technologies to significantly improve the quality of medical care for the population.Представлен анализ внедрения технологий Big Data в клинической и экспериментальной медицине, системе менеджмента здравоохранения, фармации и клинических исследованиях. Приведенные данные свидетельствуют об эффективности использования данных технологий для существенного улучшения качества медицинского обслуживания населения. Основными целями применения Big Data в медицине является создание максимально полных реестров медицинских данных, обменивающихся между собой информацией, использование накопленной информации для прогнозирования возможности развития заболеваний и их профилактики у каждого конкретного пациента, предотвращение эпидемий, создания системы ценообразования и оплаты, новых бизнес-моделей, использование предиктивного моделирования при разработке лекарственных средств, внедрение электронных карт пациента, которые были бы доступны каждому врачу, что дает возможность внедрения персонализированной медицины. Доказано, что внедрение технологий Big Data в медицине может быть достигнуто при широком использовании представления медико-биологической информации в цифровом виде. Показана целесообразность и необходимость обеспечения ее оперативной передачи, в том числе по каналам мобильной связи, указаны нерешенные вопросы в применении Big Data (неструктурированность данных, избыточность и риск искажения информации, неполное соответствие требованиям доказательной медицины, правовые, морально-этические, страховые аспекты).Проведено аналіз наукометричних баз даних Scopus, Web of Science, Ulrich's Periodicals, eLIBRARY.RU, Google Scholar, PubMed, Medline, EMBASE, EconLit, Cochrane Library, UpToDate, ACP Journal Club, HINARI, українських баз даних (http:// www.meta.ua,http://www.nbuv.gov.ua), друкованих наукових статей, монографій і посібників, присвячених проблемі Віg Data в медицині за період з 2007 по 2019 роки за ключовими словами «Big Data», «medicine». Представлено результати впровадження технологій Big Data в клінічній та експериментальній медицини, системі менеджменту охорони здоров'я, фармації та клінічних дослідженнях. Big Data — соціально-економічний феномен, що пов'язаний із появою нових технологічних можливостей для аналізу величезної кількості даних. Показано, що цілями застосування Big Data в медицині є створення максимально повних реєстрів медичних даних, які обмінюються між собою інформацією, використання накопиченої інформації для прогнозування можливості розвитку захворювань та їх профілактики у кожного конкретного пацієнта, запобігання епідеміям, створення системи ціноутворення й оплати, нових бізнес-моделей, використання інтелектуального моделювання при розробці лікарських засобів, впровадження електронних карт пацієнта, що були б доступні кожному лікареві та дає можливість впровадження персоналізованої медицини. Основними технологіями оброблення Big Data є NoSQL, MapReduce, Hadoop, R, апаратні рішення. Доведено, що використання технологій Big Data в медицині може бути досягнуто при широкому представленні медико-біологічної інформації у цифровому вигляді, показано доцільність і необхідність забезпечення її оперативного передавання, в тому числі по каналах мобільного зв'язку, вказано на невирішені питання в застосуванні Big Data (неструктурованість, синтаксичні та семантичні проблеми даних, надмірність і ризик спотворення інформації, неповна відповідність вимогам доказової медицини, правові, морально-етичні, страхові аспекти, недостатність традиційних механізмів безпеки, таких як брандмауери та антивірусне програмне забезпечення). Наведені дані свідчать про перспективність використання даних технологій для істотного поліпшення якості медичного обслуговування населення

Artificial intelligence in orthopaedics:false hope or not? A narrative review along the line of Gartner's hype cycle

Artificial Intelligence (AI) in general, and Machine Learn-ing (ML)-based applications in particular, have the potential to change the scope of healthcare, including orthopaedic surgery.The greatest benefit of ML is in its ability to learn from real world clinical use and experience, and thereby its capability to improve its own performance.Many successful applications are known in orthopaedics, but have yet to be adopted and evaluated for accuracy and efficacy in patients' care and doctors' workflows.The recent hype around AI triggered hope for development of better risk stratification tools to personalize orthopaedics in all subsequent steps of care, from diagnosis to treatment.Computer vision applications for fracture recognition show promising results to support decision-making, overcome bias, process high-volume workloads without fatigue, and hold the promise of even outperforming doctors in certain tasks.In the near future, AI-derived applications are very likely to assist orthopaedic surgeons rather than replace us. 'If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine'.(76)</p

Use of Machine-Learning Software to Categorize Oncotype DX Data

This study looked into the utilization of machine-learning software known as the Clinical Annotation Research Kit or CLARK to abstract and structure Oncotype DX testing results, a genomic test result that is often reported in text-heavy unstructured clinic notes, into a format that can be merged with larger structured databases. Oncotype DX testing results were abstracted for all the patients and each were labeled both manually and through CLARK as being low, intermediate, high or no score based on their risk level. The labeling accuracy of CLARK was compared to manual abstraction.Doctor of Pharmac

A Review of Big Data Trends and Challenges in Healthcare

The healthcare sector produces an enormous amount of complicated data from several sources, such as health monitoring systems, medical devices, and electronic health records. Big data analytics may improve healthcare by enabling more effective decision-making, improving patient outcomes, and reducing costs. To improve the operational efficiency of healthcare organizations, scientific studies must search for the standardization and integration of data analysis equipment and methods. This systematic literature review aims to provide current insights on the topic by analyzing a total of 60 relevant articles published between 2017 and 2023. The review explores the challenges and opportunities in using big data in healthcare, including data security, privacy, data quality, interoperability, and ethical considerations. The article also explores big data analytics' potential uses in healthcare, such as personalized treatment, disease prediction and prevention, and population health management. It provides significant insights for healthcare providers, researchers, and practitioners to make evidence-based decisions, as well as underlines the need for more research in this area to fully realize the promise of big data in healthcare

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

EAST MIDLANDS INTEGRATED LIFESTYLE (ILS) DATABASE: FEASIBILITY STUDY - FINAL REPORT

EXECUTIVE SUMMARY Background: A regional integrated database could serve as a rich data source for in-depth analysis in research studies across key Public Health lifestyle areas in the East Midlands. This could inform Public Health policy, service delivery and commissioning decisions. Unfortunately, existing datasets are poorly aligned across the four key Public Health lifestyle areas examined in this study: physical activity, smoking cessation, reduction in alcohol consumption, and diet and weight management. This feasibility study was therefore commissioned by the East Midlands Directors of Public Health Group chaired by Professor Derek Ward, Director of Public Health in Lincolnshire, with funding from the NIHR East Midlands Clinical Research Network and the College of Social Science, University of Lincoln. Public Health researchers in the Mental Health, Health and Social Care Research Group (MH2aSC) at the University of Lincoln were invited to carry out the study to explore the feasibility of developing and implementing an integrated lifestyle database across the East Midlands Region. Methods: A scoping review for available evidence was conducted to inform decisions about feasibility of the proposed integrated lifestyle database. This was followed by a consultation exercise with 18 stakeholders, predominantly in the East Midlands, from September 2020 to February 2021. The consultation exercise sought to gather the views of stakeholders, purposively invited to take part due to their role in public health, about the potential feasibility of an integrated database. Stakeholders were identified and invited by email to participate in the consultation meetings which took place via Microsoft Teams. A topic guide, designed specifically for this study, was used to guide the discussion. The meetings were recorded, transcribed, and analysed thematically. Results: The scoping literature review revealed potential benefits but also barriers to the development of an integrated lifestyle dataset, and highlighted the need to consider local factors which need to be better understood prior to implementation. These findings from the literature were supported by rustults from the subsequent consultation exercise. Stakeholders for the most part, welcomed the idea of an integrated East Midlands lifestyle database because of its potential benefits for research and to produce evidence to inform service development and commissioning decisions. However, concerns were expressed by some providers including anxieties around revealing their business strategies to rival organisations also involved in the provision of lifestyles services, the cost of setting up and running the proposed integrated database, and the complexities involved in information sharing and governance arrangements which would need to be established. Conclusion: In view of the findings the following options should be explored while taking into consideration the barriers and facilitators expressed by stakeholders: 1. A fully integrated individual level lifestyle dataset across the whole East Midlands covering all four lifestyle areas, with governance and access controlled by one institution (possibly a Local Authority or a university) that will house and maintain the database. 2. A fully integrated individual level dataset for all four lifestyle areas, within just one geographical area to start with, which is owned by the service provider. There is a need to consider how to make this available more widely, as the providers only report collated data back to the commissioners. 3. A fully integrated individual level dataset initially starting with one health area (possibly smoking which already has a standardised Key Performance Indicators (KPI) across the whole region, (to be rolled out later subject to success), with governance and access controlled by the institution (either a Local Authority or a local university) that will house the database. 4. An integrated aggregated level dataset covering all four lifestyle areas (reporting similar KPIs as is done currently by service providers who report back to their commissioners), across the whole East Midlands, with governance and access controlled by one institution (possibly a Local Authority or a university) that will house and maintain the database. 5. A fully integrated aggregated level dataset for all four lifestyle areas, within just one geographical area to start with, as we have in Lincolnshire, which is owned by the service provider. There is a need to consider how to make this more widely available, as the providers only report collated data back to the commissioners. This is the model already used in Lincolnshire. 6. An integrated aggregated level dataset initially starting with one health area (possibly smoking which already has a standardised KPI) across the whole region, (to be rolled out later subject to success), with governance and access controlled by the institution (either a Local Authority or a local university) that will house the database

An analysis of the effects of certified electronic health records on organizations and patients.

The growing technological advancement of electronic health records can become an issue with quality and electronic patient information exchange if hospitals do not adhere to federal guidelines. It is recommended that hospitals utilize certified electronic health records (EHRs) to receive financial incentives. This certification is supposedly also associated with the quality of the EHR itself. The certification process is criticized for allowing EHR vendors to meet a set of limited functions known in advance. EHRs can affect healthcare quality and electronic health information exchange. This dissertation explored what is known about the effects of certified EHRs on length of stay (LOS) and patient generated health data (PGHD), the relationship between hospital utilization of certified EHRs and LOS, and the relationship between hospital utilization of certified EHRs with hospital capability of allowing the function of PGHD. The first analysis was a scoping review guided by the PRISMA protocol to explore what is known of the effects of certified EHRs on LOS and PGHD. The second analysis used datasets from the American Hospital Association Survey and Information Technology Supplement and Kentucky Cabinet for Health and Family Services, Office of Health Policy from 2015 to 2019 to understand the relationship between hospital utilization of certified EHRs and LOS through a fixed effects regression model. The final paper analysis used datasets from the American Hospital Association Survey and Information Technology Supplement from 2016 to 2020 to understand the relationship between hospital utilization of certified EHRs and the function of enabling PGHD through a binary logistic regression. There is support amongst researchers on EHRs improving quality, such as, LOS and the function of PGHD improving technology efficiency and others supporting EHRs with more customization and open architecture. There is less known about whether an EHR, certified or non-certified, are different from one another with providing advantages for hospitals. Hospitals with certified EHRs have a longer LOS compared to hospitals with non-certified EHRs. Most hospitals experienced barriers with receiving, sending, or other electronic information exchange. Most hospitals with certified EHRs were more likely to not enable the function for PGHD compared to hospitals with non-certified EHRs. EHRs can be problematic while hospitals are providing hospital care. Although most hospitals possess certified EHRs, most do not enable the function of PGHD. Secondary sources from the survey were completed by the Chief Technology Officer or Chief Information Officer. Further research could be continued with understanding different groups’ health effects with health information technology. Hospitals may be satisfied with their EHRs but not as abreast on how functional the EHR is and how the EHR can benefit patients

Data Privacy in the Time of Plague

Data privacy is a life-or-death matter for public health. Beginning in late fall 2019, two series of events unfolded, one everyone talked about and one hardly anyone noticed: The greatest world-health crisis in at least 100 years, the COVID-19 pandemic; and the development of the Personal Data Protection Act Committee by the Uniform Law Commissioners (ULC) in the United States. By July 2021, each of these stories had reached a turning point. In the developed, Western world, most people who wanted to receive the vaccine against COVID- 19 could do so. Meanwhile, the ULC adopted the Uniform Personal Data Protection Act (UPDPA) at its annual meeting, paving the way for state legislatures to adopt it beginning in 2022. It has so far been introduced in three jurisdictions. These stories intersect in public health. Public health researchers struggled with COVID-19 in the United States because they lacked information about individuals who were exposed, among other matters. Understanding other public health threats (e.g., obesity, opioid abuse, racism) also requires linking diverse data on contributing social, environmental, and economic factors. The UPDPA removes some barriers to public health practice and research resulting from the lack of comprehensive federal privacy laws. Its full potential, however, can be achieved only with involvement of public health researchers and professionals. This article analyzes the UPDPA and other comprehensive state privacy statutes, noting the ways that they could promote—and hinder—public health. It concludes with recommendations for public health researchers and professionals to get involved in upcoming legislative debates on data privacy. Lives will depend on the outcomes