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Record W1975023834 · doi:10.1108/lhs-02-2013-0013

Applying systems engineering to create a population‐centered sleep disorders program

2013· article· en· W1975023834 on OpenAlexaffabout
Linda Hathout, Tina Tenbergen, Eleni Giannouli, Helen Clark, Daniel Roberts

Bibliographic record

VenueLeadership in health services · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsWinnipeg Regional Health AuthorityHealth Sciences CentreUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsOperations managementManaged careService (business)Health carePlan (archaeology)PopulationService systemMedicineMedical emergencyComputer scienceEngineeringBusinessMarketingEnvironmental health

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to present a case study of a healthcare service redesign. In 2005, sleep disorder diagnostic assessments for patients in the Province of Manitoba were conducted at two independent sites. Referrals had accumulated, creating a waiting list of over 3,400 patients while only 1,200 patients were studied annually. Wait times for diagnosis and treatment increased dramatically. No managed patient database existed, nor were there standards to measure the effectiveness of the services. Design/methodology/approach A systems analysis approach was used which including population demand analysis, value stream mapping and refining the clinical service objectives. The current and desired state of the system was defined and a gap analysis became the foundation of a change management plan. Findings A system redesign resulted in tripling the throughput with a 35 per cent increase in operating budget, evaluation metrics, elimination of diagnostic handling and treatment start delays, and an increase in treatment rates for positively diagnosed patients from 55 to 70 per cent. Originality/value This paper provides an example of how healthcare services can be envisioned using a systems analysis approach.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.126
GPT teacher head0.391
Teacher spread0.264 · 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 designSimulation or modeling
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

Citations5
Published2013
Admission routes2
Has abstractyes

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