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Record W2269339265 · doi:10.1161/str.45.suppl_1.wp279

Abstract W P279: A Multi-Campus Health System's Approach to Achieving the Highest Level of Quality Through Standardization of Practices

2014· article· en· W2269339265 on OpenAlexaff
Katherine Afshar, Renee Richetts, Lindsay Olson-Mack, Christopher Bajkiewicz, Lynn Berger

Bibliographic record

VenueStroke · 2014
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsBerger (Canada)
Fundersnot available
KeywordsMedicineStandardizationHealth careStroke (engine)LegislationQuality (philosophy)Medical emergencyDashboardQuality managementEmergency medicineOperations managementDatabaseManagement system

Abstract

fetched live from OpenAlex

Background: With healthcare reform, non-value added variation will be difficult to maintain while remaining a financially successful healthcare institution under new legislation. Our organization began looking at the highest levels of quality throughout our 5 campus system, and standardizing practice in order to achieve the same high quality standards across all sites. Methods: Acute ischemic stroke (AIS) accounts for approximately 85% of the all stroke patients; therefore, we prioritized establishing structure around AIS patients first, in order to achieve greatest impact. The first piece of the structure developed was the Preformatted Order (PFO). It is completed by the healthcare provider and a large driver in the consumption of healthcare. It was decided by setting the ‘structure’ or PFO at the point of admission could dramatically impact the care provided throughout the hospitalization. Each hospital had an individualized PFO for ED Stroke Code, IV tPA Administration, Admission Order for patients receiving IV tPA, and Admission Orders for patients with AIS/TIA without IV tPA. All were independently built with the AHA’s Clinical Practice Guidelines but site specific variation persisted. Best practices and data were analyzed from each campus to identify elements for a system PFO in each of the above. Quality and CPGs remained the driver for decision making while PFOs were built. Once agreed upon by medical staff, the system-wide PFOs were rolled out across all sites. Results: Significant improvements in dashboard metrics were identified when compared with the prior year’s data (same timeframe). The most significant, was a reduction of 0.9 days in the patient’s average length of stay (ALOS), when examined year-over-year. (FY12Q3 ALOS 4.73 days, FY13Q3 ALOS 3.80 days). The campus with the highest cost and longest ALOS, was able to demonstrate a reduction of 1.76 days in the patient’s ALOS, examined year-over-year. (FY12Q3 ALOS 5.04 days, FY13Q3 ALOS 3.28 days). Conclusions: Developing a structure aiming at the highest quality can result in an overall cost reduction and decreased ALOS. This structure will allow us to retain the same high level of care across all hospital sites, while allowing for customization of care, tailored to specific patient needs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.064
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.064
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0080.006
Scholarly communication0.0170.009
Open science0.0050.027
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0100.003

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 head0.536
GPT teacher head0.552
Teacher spread0.016 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

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