title: PopHR: a knowledge-based platform to support integration, analysis, and visualization of population health data.
abstract: Population health decision makers must consider complex relationships between multiple concepts measured with differential accuracy from heterogeneous data sources. Population health information systems are currently limited in their ability to integrate data and present a coherent portrait of population health. Consequentially, these systems can provide only basic support for decision makers. The Population Health Record (PopHR) is a semantic web application that automates the integration and extraction of massive amounts of heterogeneous data from multiple distributed sources (e.g., administrative data, clinical records, and survey responses) to support the measurement and monitoring of population health and health system performance for a defined population. The design of the PopHR draws on the theories of the determinants of health and evidence-based public health to harmonize and explicitly link information about a population with evidence about the epidemiology and control of chronic diseases. Organizing information in this manner and linking it explicitly to evidence is expected to improve decision making related to the planning, implementation, and evaluation of population health and health system interventions. In this paper, we describe the PopHR platform and discuss the architecture, design, key modules, and its implementation and use.
authors.affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada.|University of Tennessee Health Science Center-Oak Ridge National Laboratory (UTHSC-ORNL), Center for Biomedical Informatics, Department of Pediatrics, Memphis, Tennessee.
authors.forename: Arash
authors.initials: A
authors.lastname: Shaban-Nejad
authors.affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada.
authors.forename: Maxime
authors.initials: M
authors.lastname: Lavigne
authors.affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada.
authors.forename: Anya
authors.initials: A
authors.lastname: Okhmatovskaia
authors.affiliation: Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada.
authors.forename: David L
authors.initials: DL
authors.lastname: Buckeridge
mesh_terms: D064229:Biological Ontologies / Q000639:trends; D057225:Data Mining / Q000379:methods* / Q000639:trends; D003658:Decision Making, Computer-Assisted; D057286:Electronic Health Records; D019317:Evidence-Based Medicine / Q000379:methods* / Q000639:trends; D006305:Health Status Indicators; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D020407:Internet; D040442:Public Health Informatics / Q000379:methods* / Q000639:trends; D012984:Software; D012985:Software Design; D018511:Systems Integration
keywords: health data analytics; health informatics; population health record; population health surveillance; semantic data modeling