Connecting data to value: an operating model for healthcare advanced analytics
Abstract
Artificial intelligence offers the promise to revolutionize the way healthcare is delivered in the future. To capitalize on the value of advanced analytics and artificial intelligence, organizations must focus on building organizational capabilities. This report shares an operating model for insight and change in healthcare comprising six key components: analytics technology and operations, data governance, change and automation, advanced analytics and insights, analytics literacy and strategy and relationship management. The adoption of the proposed model will build core capabilities that will enable organizations to connect data to decision making and realize value from its investment in advanced analytics.
Integrity signals
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Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Algorithms trained on biased data often replicate and magnify inequalities present in their source material, disproportionately disadvantaging marginalized groups.
“Algorithms trained on biased data often replicate and magnify the inequalities present in their source material, disproportionately disadvantaging marginalized groups [[6], [7], [8]].”
From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [8]
Cited by
Sorted by citations
Amaya-Santos, S., Vargas, C., & Bermúdez-Tamayo, C. (2026) · Public Health in Practice · doi:10.1016/j.puhip.2025.100713 · Cited by 4 · see citing works
“Algorithms trained on biased data often replicate and magnify the inequalities present in their source material, disproportionately disadvantaging marginalized groups [6 – 8].”
- Authors
- Caesar, M., Hakim, Z., & Ierasts, T.
- Affiliations
- University Health Network (CA)
- University of Toronto (CA)
- ORCID
- Hakim, Zaki
- Venue
- Healthcare Quarterly
- Publisher
- Longwoods Publishing
- Published
- 2020-04-30
- Volume
- 23
- Issue
- 1
- Pages
- 20-27
- ISSN
- 1710-2774 (ISSN-L)
- OpenAlex
- W3014966784
- Cited by
- 5
- References
- 0
- Citations by year
- 2025: 1 · 2024: 2 · 2020: 2
- FWCI
- 0.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Healthcare Quarterly (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- operating model, artificial intelligence in healthcare, organizational capabilities, data governance, analytics strategy, change management, automation, data-driven decision making, value realization
- MeSH terms
- Data Science, Data Management, Artificial Intelligence, Automation, Decision Support Systems, Management, Delivery of Health Care, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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