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'''"[[Journal:Big data and public health systems: Issues and opportunities|Big data and public health systems: Issues and opportunities]]"'''
'''"[[Journal:Data to diagnosis in global health: A 3P approach|Data to diagnosis in global health: A 3P approach]]"'''


In recent years, the need for changing the current model of European public health systems has been repeatedly addressed, in order to ensure their sustainability. Following this line, information technology (IT) has always been referred to as one of the key instruments for enhancing the [[information management]] processes of healthcare organizations, thus contributing to the improvement and evolution of health systems. On the IT field, big data solutions are expected to play a main role, since they are designed for handling huge amounts of [[information]] in a fast and efficient way, allowing users to make important decisions quickly. This article reviews the main features of the European public health system model and the corresponding healthcare and management-related information systems, the challenges that these health systems are currently facing, and the possible contributions of big data solutions to this field. To that end, the authors share their professional experience on the Spanish public health system and review the existing literature related to this topic. ('''[[Journal:Big data and public health systems: Issues and opportunities|Full article...]]''')<br />
With connected medical devices fast becoming ubiquitous in healthcare monitoring, there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge. To address this challenge, we present a "3P" approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is "physician assist filters" (PAF) that 1. transform unwieldy multi-sensor time series data into summarized patient/disease-specific trends in steps of progressive precision as demanded by the doctor for a patient’s personalized condition, and 2. help in identifying and subsequently predictively alerting the onset of critical conditions. ('''[[Journal:Data to diagnosis in global health: A 3P approach|Full article...]]''')<br />
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Revision as of 15:25, 18 March 2019

Fig9 Pathinarupothi BMCMedInfoDecMak2018 18.png

"Data to diagnosis in global health: A 3P approach"

With connected medical devices fast becoming ubiquitous in healthcare monitoring, there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge. To address this challenge, we present a "3P" approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is "physician assist filters" (PAF) that 1. transform unwieldy multi-sensor time series data into summarized patient/disease-specific trends in steps of progressive precision as demanded by the doctor for a patient’s personalized condition, and 2. help in identifying and subsequently predictively alerting the onset of critical conditions. (Full article...)

Recently featured:

Building a newborn screening information management system from theory to practice
Adapting data management education to support clinical research projects in an academic medical center
Development of an electronic information system for the management of laboratory data of tuberculosis and atypical mycobacteria at the Pasteur Institute in Côte d’Ivoire