Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies.

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 CrowdHEALTH, 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.

Ethical challenges and responses of big data in healthcare / 中华医学科研管理杂志

Objective To analyze ethical challenges raised from big data in healthcare,provide suggestions for improving its application and development.Methods The ethical challenges were discussed according to the study of important literatures,typical case analysis,analyzing the current situation and its development trend of big data in healthcare.Results The advancement of technology and policies favorable have provided new opportunities for development of big data in healthcare.However,there are still some ethical challenges for its application.For instance,the difficulty of perform traditional informed consent process,the high risk of EHR information release during data storage and transmission,and inadequate privacy protection.To solve these problems,the corresponding regulation and some guidelines should be refined and/or updated;the management system that include informed consent should be adjusted;the corresponding technical supporting platform and specialized interdisciplinary team are also needed.Conclusions The establishment and application is a systematic project.The solution of the ethical challenges is also based on a comprehensive safeguard system of laws,regulations,management and technology.

Big data in emergency medcine and Datathon event / 中华危重病急救医学

A detailed, high-scale clinical data can be generated in the process of diagnosis and treatment of emergency critically ill patients. The integration and analysis and utilization of these data are of great value for improving the treatment level and efficiency and developing the data-driven clinical assistant decision support. China has large volume of health information resources, however, the construction of healthcare databases and subsequent secondary analysis has just started. With the effort of the Chinese PLA General Hospital in building an emergency database and promoting data sharing, the first emergency database was published in China and a health Datathon was organized utilizing this database, providing experience for clinical data integration, database construction, cross-disciplinary collaboration and data sharing. Referring to the development at home and abroad, this review discussed work in this area and further proposed establishing a big data cooperation for emergency medicine and building a learning healthcare system to integrate more clinical resources and form a closed loop of "clinical database construction-analysis-applications", and enhance the effectiveness of medical big data in reducing medical costs and improving healthcare delivery.

A cross-disciplinary collaborative "Datathon" model to promote the application of medical big data / 中华危重病急救医学

Medical practice generates and stores immense amounts of clinical process data, while integrating and utilization of these data requires interdisciplinary cooperation together with novel models and methods to further promote applications of medical big data and research of artificial intelligence. A "Datathon" model is a novel event of data analysis and is typically organized as intense, short-duration, competitions in which participants with various knowledge and skills cooperate to address clinical questions based on "real world" data. This article introduces the origin of Datathon, organization of the events and relevant practice. The Datathon approach provides innovative solutions to promote cross-disciplinary collaboration and new methods for conducting research of big data in healthcare. It also offers insight into teaming up multi-expertise experts to investigate relevant clinical questions and further accelerate the application of medical big data.

Unstructured medical image query using big data - An epilepsy case study.

Big data technologies are critical to the medical field which requires new frameworks to leverage them. Such frameworks would benefit medical experts to test hypotheses by querying huge volumes of unstructured medical data to provide better patient care. The objective of this work is to implement and examine the feasibility of having such a framework to provide efficient querying of unstructured data in unlimited ways. The feasibility study was conducted specifically in the epilepsy field. The proposed framework evaluates a query in two phases. In phase 1, structured data is used to filter the clinical data warehouse. In phase 2, feature extraction modules are executed on the unstructured data in a distributed manner via Hadoop to complete the query. Three modules have been created, volume comparer, surface to volume conversion and average intensity. The framework allows for user-defined modules to be imported to provide unlimited ways to process the unstructured data hence potentially extending the application of this framework beyond epilepsy field. Two types of criteria were used to validate the feasibility of the proposed framework - the ability/accuracy of fulfilling an advanced medical query and the efficiency that Hadoop provides. For the first criterion, the framework executed an advanced medical query that spanned both structured and unstructured data with accurate results. For the second criterion, different architectures were explored to evaluate the performance of various Hadoop configurations and were compared to a traditional Single Server Architecture (SSA). The surface to volume conversion module performed up to 40 times faster than the SSA (using a 20 node Hadoop cluster) and the average intensity module performed up to 85 times faster than the SSA (using a 40 node Hadoop cluster). Furthermore, the 40 node Hadoop cluster executed the average intensity module on 10,000 models in 3h which was not even practical for the SSA. The current study is limited to epilepsy field and further research and more feature extraction modules are required to show its applicability in other medical domains. The proposed framework advances data-driven medicine by unleashing the content of unstructured medical data in an efficient and unlimited way to be harnessed by medical experts.