CrowdHEALTH: An e-Health Big Data Driven Platform towards Public Health Policies

Mavrogiorgou, Argyro; Kiourtis, Athanasios; Kyriazis, Dimosthenis; Nigro, Antonio De; Blanes-Selva, Vicent; García‐Gómez, Juan Miguel; Menychtas, Andreas; Sorić, Maroje; Jurak, Gregor; Luštrek, Mitja; Gradišek, Anton; Kosmidis, Thanos; Nifakos, Sokratis; Perakis, Κonstantinos; Miltiadou, Dimitrios; Gallos, Parisis

doi:10.5220/0009388802410249

Cited by 6 publications

(9 citation statements)

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“…The CrowdHEALTH's Health Policy Model, a new paradigm of Health Records ( [11] [12]), offers the ability to include all existing health data. To achieve that, CrowdHEALTH seamlessly integrates big data technologies across the complete data path [13], incorporating mechanisms for causal and risk analysis [12], allowing to create policy models and obtaining analytical results for evidence-based decisions and evaluations for policymakers toward a "health in all policies" approach. The hierarchical structure of this model offers versatility in the creation and handling of the policies, resulting in Health Analytics Tools that offer quantitative policy support and provide a basis for meta-analytic operations [11].…”

Section: Discussionmentioning

confidence: 99%

The Application of Artificial Intelligence in Health Policy: A Scoping Review

Ramezani

Takian

Bakhtiari

et al. 2023

Preprint

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Background Policymakers require precise, in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking. Method PubMed, Scopus, and the Web of Science databases were investigated to find relevant studies from 2000 to 2023 using the keywords of "artificial intelligence" and "policymaking." Walt and Gilson's policy triangle framework was used for charting the data. Results The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. Moreover, it can be used to develop creative agendas with fewer political limitations and higher rationality, leading to better policies. Furthermore, AI provides the opportunity to make evidence-informed decisions by developing new platforms and toolkits. Most of the suggested AI solutions for health policy are not meant to replace experts but to make decision-making smarter. Conclusion Numerous approaches exist for AI to influence the health policymaking process. Leading health systems can benefit from AI's potential to expand the use of evidence-based policymaking in health systems.

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Section: Discussionmentioning

confidence: 99%

The Application of Artificial Intelligence in Health Policy: A Scoping Review

Ramezani

Takian

Bakhtiari

et al. 2023

Preprint

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“…The 7 tools reviewed are summarized in Table 2. Three integrated platforms (EVOTION, MIDAS, CrowdHEALTH) were designed to support public health policy decisions for a range of conditions and include a data analytics component supporting both descriptive and predictive analytics [22][23][24][25][26]. Users can create policy models, define the way in which data should be analyzed in order to produce the evidence useful for public health policymaking and obtain analytical results of how this evidence may support or contradict various policy actions.…”

Section: Tools Overviewmentioning

confidence: 99%

“…Two platforms were piloted with each of the following conditions: respiratory, cardiovascular and diabetes. CrowdHEALTH was piloted with cancer [23]. Six of the 7 tools were designed for or used with NCDs only while PoPHR was applicable to infectious diseases too [21].…”

Section: Tools Overviewmentioning

confidence: 99%

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review (Preprint)

Dritsakis,

Gallos,

Psomiadi

et al. 2024

Preprint

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BACKGROUND There is an emerging need for evidence-based approaches harnessing large amounts of healthcare data and novel technologies (such as Artificial Intelligence, AI) to optimize public health policy-making. OBJECTIVE The aim of the present study was to explore the data analytics tools designed specifically for policy-making in Non-Communicable Diseases (NCDs) and their implementation. METHODS A scoping review was conducted after searching the PubMed database for articles published in the last 10 years. RESULTS Nine articles that presented 7 data analytics tools designed to inform policy-making for NCDs were reviewed. Tools incorporate descriptive and predictive analytics. Some tools were designed to include recommendations for decision support but no pilot studies have been published that apply prescriptive analytics. The tools were piloted with a range of conditions with cancer being the condition least studied. Implementation of tools included use cases, pilots or evaluation workshops were reported that involved policy-makers. However, our findings demonstrate very limited real-world use of analytics by policy-makers, in line with previous studies. CONCLUSIONS Despite the availability of tools designed for different purposes and conditions, data analytics are not widely used to support policy-making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy-making. Based on the findings we make a number of suggestions for researchers developing digital tools to support public health policy-making. The findings will also serve as input for the EU-funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform.

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“…location), should be combined and aggregated with historical data from the centralized electronic health records of healthcare organizations, taking also into consideration the limitations on the resources availability and energy consumption [58]. In this context, signal processing is often followed by analysis techniques which are taking place in multiple layers of the application stack, such as clustering, support vector machines, decision trees, neural and deep networks that are used for predictions, assessment of health parameters and risk estimation [59].…”

Section: Current and Future Challengesmentioning

confidence: 99%

Roadmap on signal processing for next generation measurement systems

Iakovidis

Ooi

Kuang

et al. 2021

Meas. Sci. Technol.

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Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

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CrowdHEALTH: An e-Health Big Data Driven Platform towards Public Health Policies

Cited by 6 publications

References 0 publications

The Application of Artificial Intelligence in Health Policy: A Scoping Review

The Application of Artificial Intelligence in Health Policy: A Scoping Review

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review (Preprint)

Roadmap on signal processing for next generation measurement systems

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