Difference between revisions of "Template:Article of the week"

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'''"[[Journal:Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs|Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs]]"'''
'''"[[Journal:Data to diagnosis in global health: A 3P approach|Data to diagnosis in global health: A 3P approach]]"'''


Childhood obesity prevalence is an issue of international public health concern, and governments have a significant role to play in its reduction. The Healthy Children Initiative (HCI) has been delivered in New South Wales (NSW), Australia, since 2011 to support implementation of childhood obesity prevention programs at scale. Consequently, a system to support local implementation and data collection, analysis, and reporting at local and state levels was necessary. The Population Health Information Management System (PHIMS) was developed to meet this need.
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 />
 
A collaborative and iterative process was applied to the design and development of the system. The process comprised identifying technical requirements, building system infrastructure, delivering training, deploying the system, and implementing quality measures. ('''[[Journal:Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs|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
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