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ICES: History, Research, Science & Companies

ICES (formerly known as the Institute for Clinical Evaluative Sciences) is an independent, non-profit corporation that applies the study of health informatics for health services research and population-wide health outcomes research in Ontario, Canada, using data collected through the routine administration of Ontario's system of publicly funded health care.

Language: English [EN]
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ICES topic overview

The analysis highlights History, Research, Science and Companies as prominent areas in the source structure around ICES.

Related topics
42
Source areas
5
Connected nodes
47
Extracted relationships
107
Concept neighborhoods
29
Bridge connections
47

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Examples of research impact · 20 topics
Overview · 14 topics
History · 4 topics
Methodology and data holdings · 2 topics
Privacy and security · 2 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Founded
1992
Industry
Health services research · Health research
Headquarters
Toronto, Ontario, Canada
Founder
David Naylor · Jack Williams
Key people
Michael Schull, President and CEO
Number of employees
400+

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

History

Privacy and security

Methodology and data holdings

Examples of research impact

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How ICES connects Entity context

The extracted context around ICES shows recurring relationship patterns in the source. For example, ICES → Advisory Panel, Although, Amit Garg, Astrid Guttmann, Brain Disorders, Canada, Clostridioides, CMAJ, Concern, Costs, David Naylor, Donald Redelmeier, Dr, Due, Government, Health Administrative Data, Health Canada, Healthcare Innovation, ICES Western, Incidence Another extracted example is ICES → April, David Naylor, Dr, Government, In, Jack Williams, Naylor, OHIP, Ontario, Ontario Health Insurance Plan. Use these groups to spot repeated connection types before inspecting the individual relationships.

ICES

Top relations

has impact · 45
ICES → Advisory Panel, Although, Amit Garg, Astrid Guttmann, Brain Disorders, Canada, Clostridioides, CMAJ, Concern, Costs, David Naylor, Donald Redelmeier, Dr, Due, Government, Health Administrative Data, Health Canada, Healthcare Innovation, ICES Western, Incidence
related to history · 10
ICES → April, David Naylor, Dr, Government, In, Jack Williams, Naylor, OHIP, Ontario, Ontario Health Insurance Plan
related to Privacy and security · 10
ICES → Act, All ICES, Information, IPC, Ontario, Personal Health Information Protection, PHIPA, Privacy Commissioner, To, Under Section
related to Research programs · 7
ICES → AddictionsPopulations, CancerCardiovascularChronic Disease, Dialysis, Health Systems, PharmacotherapyKidney, Public HealthPrimary Care, TransplantationLife StageMental Health
related to Methodology and data holdings · 6
ICES → By, It, Ontario, Ontario's, The ICES, Through
related to External links · 5
ICES → Health, Linkage Network, Long-Term CareInternational Population Data, OntarioOntario Ministry, Privacy Commissioner
Products · 4
ICES → Health informatics, Health information, Population health Reports, Scientific journal Papers
Founder · 2
ICES → David Naylor, Jack Williams
Industry · 2
ICES → Health research, Health services research
Founded · 1
ICES → 1992

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

health ontario data research care dr led study using administrative ontario's canada journal services population new 2015 institute government links

ICES relationships Subject–Predicate–Object triples

TTTA extracted 107 structured relationships around ICES. Examples in this analysis include ICES → Founded → 1992 and ICES → Founder → David Naylor. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
ICESFounded19921.00infobox
ICESFounderDavid Naylor1.00infobox
ICESFounderJack Williams1.00infobox
ICESHeadquartersToronto, Ontario, Canada1.00infobox
ICESIndustryHealth services research1.00infobox
ICESIndustryHealth research1.00infobox
ICESKey peopleMichael Schull, President and CEO1.00infobox
ICESNumber of employees400+1.00infobox
ICESProductsHealth information1.00infobox
ICESProductsHealth informatics1.00infobox
ICESProductsPopulation health Reports1.00infobox
ICESProductsScientific journal Papers1.00infobox
ICESRevenue7,830,535 Canadian dollar (2003)1.00infobox
ICESTotal assets7,467,811 Canadian dollar (2003)1.00infobox
ICESTypeNot-for-profit corporation1.00infobox
ICESWebsitehttp://www.ices.on.ca1.00infobox

Related concept clusters Concept neighborhoods

The concept neighborhoods around ICES bring nearby vocabulary together. In this analysis, examples include Health, Data and Led. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • ICES
    • Health
    • Data
    • Led
    • Research
    • Care
    • Ontario
    • Study
    • Dr
    • Administrative
    • Using
    • Canadian
    • Number
  • ices
    • Health
    • Data
    • Led
    • Research
    • Care
    • Ontario
    • Study
    • Dr
    • Administrative
    • Using
    • Canadian
    • Number
  • health informatics
    • Services
    • System
    • Data
    • Ices
    • Ontario
    • Research
    • Reports
    • Sciences
    • Scientific
    • Administrative
    • Care
    • Canadian
  • health services research
    • Data
    • Ices
    • System
    • Ontario
    • Population
    • Research
    • Government
    • Administrative
    • Care
    • Peer-reviewed
    • Reports
    • Scientific
  • health outcomes
    • Data
    • Ices
    • Ontario
    • Research
    • Administrative
    • Care
    • Institute
    • Population
    • Canada
    • Ontario's
    • Canadian
    • Collected
  • ontario
    • Information
    • Research
    • Privacy
    • Services
    • Links
    • Population
    • New
    • Ontario's
    • Administrative
    • Sciences
    • Canadian
    • Number
  • publicly funded health care
    • Data
    • Ices
    • Population
    • Ontario
    • Research
    • Corporation
    • Informatics
    • Reports
    • Scientific
    • Administrative
    • Care
    • Health
  • population health
    • Data
    • Ices
    • Reports
    • Scientific
    • Canadian
    • Ontario
    • Privacy
    • Services
    • Research
    • Information
    • Administrative
    • Care

Connections between topic areas Semantic bridges

For ICES, one of the stronger structural bridges in this analysis connects ICES with Examples of research impact. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
ICES — Examples of research impact · splits 27 ⟂ 21
ICES — Overview · splits 33 ⟂ 15
ICES — History · splits 43 ⟂ 5
ICES — Privacy and security · splits 45 ⟂ 3
ICES — Methodology and data holdings · splits 45 ⟂ 3

Map overview Semantic statistics

ICES

Nodes48
Edges47
Triples107
Avg. degree1.96
Density0.041667
Components1

Source & methodology

TTTA analyzes the structure around ICES to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Research, Science & Companies, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — ICES · EN edition · Analysis: TopicsToTalkAbout

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