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Record W135919753 · doi:10.3384/lic.diva-111635

Managing Variable Patient Flows at Hospitals

2014· book· en· W135919753 on OpenAlex
Olle Olsson

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.

Bibliographic record

VenueLinköping University Electronic Press eBooks · 2014
Typebook
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsEngineering Link (Canada)
Fundersnot available
KeywordsVariable (mathematics)InflowHealth careOperations managementOutflowPatient careBusinessMedical emergencyMedicineNursingGeographyEngineering

Abstract

fetched live from OpenAlex

Healthcare is a large industry faced with major challenges, such as decreasing inpatient bed numbers and increases in the share of elderly people, which require improved efficiency and effectiveness. The organisation of hospitals normally comprises highly specialised clinical departments, through which patient flows are managed. Since patient flows often involve several clinical departments, this requires much coordination both in space and time. With every individual patient having different diseases, severity levels and responses to therapy, the variability in patient flows has an impact on the inflow, internal flow and outflow at clinical departments and hospitals. Historically, healthcare resources have not been adapted to these variations. The purpose of this licentiate thesis is therefore to explore how variable patient flows are managed in hospitals. This comprises how variable patient flows affect hospitals as well as how variable patient flows are handled. It also includes the organisational configuration, and the influence it has on the actions used to handle variable patient flows in 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.021
GPT teacher head0.277
Teacher spread0.257 · 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