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Record W2099846354 · doi:10.1136/jamia.2010.004838

A review on systematic reviews of health information system studies

2010· review· en· W2099846354 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the American Medical Informatics Association · 2010
Typereview
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of British ColumbiaUniversity of OttawaUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsGuidelineHealth careSystematic reviewIncentiveLegislationQuality (philosophy)MEDLINEMedicineNursingKnowledge managementPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The purpose of this review is to consolidate existing evidence from published systematic reviews on health information system (HIS) evaluation studies to inform HIS practice and research. Fifty reviews published during 1994-2008 were selected for meta-level synthesis. These reviews covered five areas: medication management, preventive care, health conditions, data quality, and care process/outcome. After reconciliation for duplicates, 1276 HIS studies were arrived at as the non-overlapping corpus. On the basis of a subset of 287 controlled HIS studies, there is some evidence for improved quality of care, but in varying degrees across topic areas. For instance, 31/43 (72%) controlled HIS studies had positive results using preventive care reminders, mostly through guideline adherence such as immunization and health screening. Key factors that influence HIS success included having in-house systems, developers as users, integrated decision support and benchmark practices, and addressing such contextual issues as provider knowledge and perception, incentives, and legislation/policy.

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.

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)0.063
metaresearch head score (Gemma)0.061
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0630.061
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
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 head0.147
GPT teacher head0.527
Teacher spread0.381 · 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