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Record W2021733578 · doi:10.3414/me09-01-0027

Exploring Health Information Technology Innovativeness and its Antecedents in Canadian Hospitals

2009· article· en· W2021733578 on OpenAlexafffundabout
Mirou Jaana, C. Sicotte, Guy Paré

Bibliographic record

VenueMethods of Information in Medicine · 2009
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversité de MontréalUniversity of OttawaHEC Montréal
FundersCanada Research Chairs
KeywordsInformation technologyHealth information technologyBusinessMedicineKnowledge managementMarketingHealth careEconomicsPolitical scienceComputer scienceEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVES: The primary aim of this study was to assess the antecedents of health information technology (HIT) innovativeness in public hospitals. To do so, we built upon our own previous work to relate the level of HIT innovativeness to organizational capacity characteristics. METHODS: We conducted a survey of chief information officers (CIOs) in public hospitals in the two largest Canadian provinces to identify the level of HIT innovativeness in these settings and test nine research hypotheses derived from the proposed research model. RESULTS: A total of 106 completed questionnaires were received, which represents a response rate of 52%. Our findings indicate strong support for the research model. Seven out of nine hypotheses were supported indicating a significant relationship between HIT innovativeness and structural, financial, leadership, and knowledge sharing capacity characteristics. Results also reveal a moderate level of HIT innovativeness in the surveyed hospitals, with more emphasis on administrative systems and their integration than on clinical systems and emerging technologies. CONCLUSIONS: This study demonstrates that organizational characteristics are related to HIT innovativeness; this relationship holds irrespective of the public or private nature of hospitals.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.143
GPT teacher head0.518
Teacher spread0.375 · 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 designObservational
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

Citations26
Published2009
Admission routes3
Has abstractyes

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