The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies (original) (raw)
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Studies in health technology and informatics, 2017
Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.
Health in All Policy Making Utilizing Big Data
Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : časopis Društva za medicinsku informatiku BiH, 2020
Introduction: Health in all Policies (HiAP) is a valuable method for effective Healthcare policy development. Big data analysis can be useful to both individuals and clinicians so that the full potential of big data is employed. Aim: The present paper deals with Health in All Policies, and how the use of Big Data can lead and support the development of new policies. Methods: To this end, in the context of the CrowdHEALTH project, data from heterogeneous sources will be exploited and the Policy Development Toolkit (PDT) model will be used. In order to facilitate new insights to healthcare by exploiting all available data sources. Results: In the case study that is being proposed, the NOHS Story Board (inpatient and outpatient health care) utilizing data from reimbursement of disease-related groups (DRGs), as well as medical costs for outpatient data, will be analyzed by the PDT. Conclusion: PDT seems promising as an efficient decision support system for policymakers to align with HiAP as it offers Causal Analysis by calculating the total cost (expenses) per ICD-10, Forecasting Information by measuring the clinical effectiveness of reimbursement cost per medical condition, per gender and per age for outpatient healthcare, and Risk Stratification by investigating Screening Parameters, Indexes (Indicators) and other factors related to healthcare management. Thus, PDT could also support HiAP by helping policymakers to tailor various policies according to their needs, such as reduction of healthcare cost, improvement of clinical effectiveness and restriction of fraud.
Big data analytics in the health sector: challenges and potentials
Management:Journal of Sustainable Business and Management Solutions in Emerging Economies, 2019
The introduction of the Big Data concept in the healthcare sector points to a major challenge and potential. Motivation: Our goal is to indicate the importance of analyzing and processing large amounts of data that go beyond the typical ways of storing and processing information. Тhе data have their own characteristics: volume, velocity and variety. There are different structures. Analysis of these data is possible with the Big Data concept. Its importance is most evident in the health sector, because the preservation of the health status of the population depends on adequate data analysis. Idea: The idea of the paper is that big health data analytics contributes to a better quality provision of health services. The process is more efficient and effective. Data: Health analytics suggests that more and more resources are being utilized globally. In order to achieve improvements, health analytics and Big data concepts play a vital role in overcoming the obstacles, working more efficiently and aiming at providing adequate medical care. Tools: The Big data concept will help identify patients with developed chronic diseases. Big data can identify outbreaks of flu or other epidemics in real time. In this way, they are managed by the healthcare system, reducing overall healthcare costs over time, and increasing revenues. Findings: A key policy challenge is to improve the outcomes of the healthcare system, data collection and analysis, security, storage and transfers. Big data are the potential to improve quality of care, improve predictions of diseases, improve the treatment methods, reduce costs. Contribution: This paper points to the challenges and potentials of Big Health Data analytics and formulates good reasons to apply the Big Data concept in healthcare.
Transforming Health through Big Data: Challenges and Considerations
2018
Modern healthcare is increasingly dependent on good data, and effective information systems, for care delivery, and to develop and evaluate health policy. The context of big data differs in significant ways from traditional types of health data, while the use of big data for epidemiology and public health is becoming more common, the use of these tools for health service planning and health policy making lags behind. A large EU funded project (titled MIDAS) that focuses on merging, analysing and visualising data from heterogeneous sources to support health policy makers work in using and accessing health data across EU countries is underway. This paper briefly describes the key challenges that must be met to access, use and make sense of this big data in healthcare, focusing on legal, governance and ethical issues. Unless these issues are dealt with, the promise of Big Data for health, will never be fulfilled.
Big Data: towards smart and sustainable health
Management and evolution of the European Union member states in the Big Data era
In recent years, in all crucial processes in the health field, from clinical applications to citizen relations, an exponential diffusion of digital technologies has been noted, assuming an increasingly strategic importance in the context of clinical operations and the management of health establishments. The inevitable contact between technology and healthcare, through the analysis of Big Data up to artificial intelligence, represents a great opportunity either within the healthcare system as a whole, or in the individual management of the welfare. Although confidentiality issues may arise for the management of data on digital platforms, the information represents considerable value for research in the medical field. The document analyzes, through the study of various experiences and publications, the potential and use of Big Data in the health sector.
Medical Archives
Introduction. According to WHO, "health policy refers to decisions, plans, and actions that are undertaken to achieve specific health care goals within a society". Although policymaking is important to be based on scientific evidence, in many countries, evidence-informed decision-making remains the exception rather than the rule. Aim: This work presents a cloud-based Decision Support System for public health decision-making. Methods: In Crowd-HEALTH, the concept of a Public Health Policy (PHP) is directly connected with one or more Key Performance Indexes (KPIs). The design and technical details of the system implementations are reported, along with use case scenarios. Results: The Policy Development Toolkit presents a unique interface and point of reference for policymakers, allowing them to create policy models and obtain analytical results for evidence-based decisions and evaluations. Conclusions: The hierarchical structure of the Public Health Policy Model offers versatility in the creation and handling of the policies, resulting in Health Analytics Tools Results Objects which offer quantitative policy support and provide the basis for meta-analytic operations.
Big Data Techniques for Public Health: A Case Study
2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017
Public health researchers increasingly recognize that to advance their field they must grapple with the availability of increasingly large (i.e., thousands of variables) traditional population-level datasets (e.g., electronic medical records), while at the same time integrating additional large datasets (e.g., data on genomics, the microbiome, environmental exposures, socioeconomic factors, and health behaviors). Leveraging these multiple forms of data might well provide unique and unexpected discoveries about the determinants of health and wellbeing. However, we are in the very early stages of advancing the techniques required to understand and analyze big population-level data for public health research. To address this problem, this paper describes how we propose that big data can be efficiently used for public health discoveries. We show that data analytics techniques traditionally employed in public health studies are not up to the task of the data we now have in hand. Instead ...
Health Big Data Analytics: A Technology Survey
IEEE Access
Because of the vast availability of data, there has been an additional focus on the health industry and an increasing number of studies that aim to leverage the data to improve healthcare have been conducted. The health data are growing increasingly large, more complex, and its sources have increased tremendously to include computerized physician order entry, electronic medical records, clinical notes, medical images, cyber-physical systems, medical Internet of Things, genomic data, and clinical decision support systems. New types of data from sources like social network services and genomic data are used to build personalized healthcare systems, hence health data are obtained in various forms, from varied sources, contexts, technologies, and their nature can impede a proper analysis. Any analytical research must overcome these obstacles to mine data and produce meaningful insights to save lives. In this paper, we investigate the key challenges, data sources, techniques, technologies, as well as future directions in the field of big data analytics in healthcare. We provide a do-it-yourself review that delivers a holistic, simplified, and easily understandable view of various technologies that are used to develop an integrated health analytic application. INDEX TERMS Big data, cyber-physical systems, health analytics, machine learning, social networks analysis.
Practitioners and researchers in health services and public health routinely estimate population health indicators from a range of data sources. These indicators are used in many settings to describe health status, monitor quality of care, and evaluate the effect of interventions. The data and knowledge necessary to calculate indicators, however, are scattered across different health set-tings, resulting in inconsistent and fragmented indicators and an inefficient use of population health information in research and practice. The Population Health Record (PopHR) described in this paper is an informatics platform for semi-automated integration of disparate data to enable measurement and monitoring of population health status and determinants. The research and development to build the PopHR uses AI methods to perform many tasks, including calculation of indicators and interaction with users.