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A Multi-Tiered Perspective on Healthcare Interoperability

2015· book-chapter· en· W2482920623 on OpenAlexaff
Craig Kuziemsky

Bibliographic record

VenueIGI Global eBooks · 2015
Typebook-chapter
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInteroperabilityHealth careSemantic interoperabilityCross-domain interoperabilityHealthcare deliveryKnowledge managementProcess managementComputer scienceDomain (mathematical analysis)Process (computing)BusinessWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

The current healthcare delivery paradigm is defined by integrative care delivery across disparate providers and services. Therefore the ability to deliver efficient and effective healthcare services is dependent on designing and implementing interoperable systems. However, the notion of interoperability is multifaceted and complex. Although the exchange of data is often described as analogous with interoperability it must be remembered that healthcare is a process oriented domain and clinical, management, organizational and other processes must be considered as part of interoperability. This chapter discusses healthcare delivery and the role interoperability plays in supporting its delivery. First, the chapter provides a background on healthcare interoperability from multiple perspectives. Then it presents a case study of collaborative care delivery and uses it to outline specific interoperability requirements. The chapter then uses these requirements to develop a multi-tiered framework of healthcare interoperability, concluding with a discussion of the implications of the framework for interoperability research and for systems design to support integrated healthcare delivery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.011
Scholarly communication0.0090.012
Open science0.0020.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.151
GPT teacher head0.457
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2015
Admission routes1
Has abstractyes

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