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Research challenges in measuring data for population health to enable predictive modeling for improving healthcare

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Noshir ContractorAuthors Info & Claims

ACM SIGHIT Record, Volume 2, Issue 2

Pages 36 - 41

https://doi.org/10.1145/2384556.2384561

Published: 01 September 2012 Publication History

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Abstract

At the core of the healthcare crisis is a fundamental lack of actionable data, needed to stratify individuals within populations, to predict which persons have which outcomes. A new health system with better health management will require better health measurement, to improve cost and quality. It is now possible to use new technologies to provide the rich datasets necessary for adequate health measurement, which enables new information systems for new health systems.

This report is a summary of a workshop on Measuring Data for Population Health, sponsored by the NSF SmartHealth program with assistance from the NIH mHealth initiative, held on January 12--13, 2012 in Washington DC. There were 42 attendees, including invited researchers from academia, government and industry, plus program officers from NSF and NIH.

The workshop had background talks by leaders in health systems and information systems, followed by breakout discussions on future challenges and opportunities in measuring and managing population health. This report describes the observations on what problems of health systems should be addressed and what solutions of information systems should be developed. The recommendations cover how new information technologies can enable new health systems, with support from future initiatives of federal programs.

The workshop and its report identify research challenges that utilize new computing and information technologies to enable better measurement and management for practical healthcare. The measurement technologies focus on deeper monitoring of broader populations. The management technologies focus on utilizing new personal health records to provide personalized treatment guidelines, specialized for each population cohort. This would enable predictive modeling for health systems to support viable healthcare at acceptable cost and quality.

A workshop website contains background and discussion notes: https://wiki.engr.illinois.edu/display/hiworkshop/NSF+Workshop+Population+Health

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Sarwar Kamal MDey NAshour A(2017)Large Scale Medical Data Mining for Accurate Diagnosis: A BlueprintHandbook of Large-Scale Distributed Computing in Smart Healthcare10.1007/978-3-319-58280-1_7(157-176)Online publication date: 8-Aug-2017
https://doi.org/10.1007/978-3-319-58280-1_7
Liu WLiu JWu QSusilo WDeng HQin B(2016)SAKESecurity and Communication Networks10.1002/sec.11989:15(2754-2765)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1002/sec.1198
Schatz B(2015)National Surveys of Population Health: Big Data Analytics for Mobile Health MonitorsBig Data10.1089/big.2015.00213:4(219-229)Online publication date: Dec-2015
https://doi.org/10.1089/big.2015.0021

Index Terms

Research challenges in measuring data for population health to enable predictive modeling for improving healthcare
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation

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ACM SIGHIT Record Volume 2, Issue 2

September 2012

48 pages

EISSN:2158-8813

DOI:10.1145/2384556

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Association for Computing Machinery

New York, NY, United States

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Published: 01 September 2012

Published in SIGHIT Volume 2, Issue 2

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Sarwar Kamal MDey NAshour A(2017)Large Scale Medical Data Mining for Accurate Diagnosis: A BlueprintHandbook of Large-Scale Distributed Computing in Smart Healthcare10.1007/978-3-319-58280-1_7(157-176)Online publication date: 8-Aug-2017
https://doi.org/10.1007/978-3-319-58280-1_7
Liu WLiu JWu QSusilo WDeng HQin B(2016)SAKESecurity and Communication Networks10.1002/sec.11989:15(2754-2765)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1002/sec.1198
Schatz B(2015)National Surveys of Population Health: Big Data Analytics for Mobile Health MonitorsBig Data10.1089/big.2015.00213:4(219-229)Online publication date: Dec-2015
https://doi.org/10.1089/big.2015.0021

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New health systems using network information technologies

The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature

Personalized Health Monitoring System for Managing Well-Being in Rural Areas

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New health systems using network information technologies

The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature

Personalized Health Monitoring System for Managing Well-Being in Rural Areas

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