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Record W2035286012 · doi:10.4018/ijcac.2015010103

A Bounded Health Information Technology System Design Approach to Support Community-Based Care Delivery

2015· article· en· W2035286012 on OpenAlexaff
Liam Peyton, Jaspreet Bindra, Aladdin Baarah, Austin Chamney, Craig Kuziemsky

Bibliographic record

VenueInternational Journal of Cloud Applications and Computing · 2015
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCloud computingHealth careLeverage (statistics)Computer sciencemHealthInformation systemKnowledge managementInformation technologyProcess managementData scienceBusinessEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Health information technology (HIT) offers great potential for supporting healthcare delivery, particularly collaborative care delivery that is provided across multiple settings and providers. To date much of HIT design has focused on digitizing data or processes on a departmental or healthcare provider basis. However, this bounded approach has not scaled well for supporting community based care across disparate providers or settings because of the lack of boundaries (e.g. disprate data and processes) that exist in community based care. Cloud computing approaches that leverage mobile form applications for developing integrated HIT solutions have the potential to support collaborative healthcare delivery in the community. However, to date there is a shortage of methods that describe how to develop integrated cloud computing solutions to support community based care delivery. In particular there is a need for methods that identify how to incorporate boundaries into cloud computing systems design. This paper uses a three year case study of the design of the Palliative Care Information System (PAL-IS) to provide system design insight on cloud computing approaches that leverage mobile forms applications to support community care management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.411
Teacher spread0.326 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2015
Admission routes1
Has abstractyes

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