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Record W2116671625 · doi:10.12927/hcq.2012.23121

A Relentless Commitment to Improvement: The Guelph General Hospital Experience

2012· article· en· W2116671625 on OpenAlexaboutno aff
Esther Green, Richard L. Ernst

Bibliographic record

VenueHealthcare Quarterly · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsnot available
Fundersnot available
KeywordsNursingHealth administrationBest practiceMedicineOperations managementPublic relationsBusinessPsychologyMedical emergencyPolitical sciencePublic healthEngineering

Abstract

fetched live from OpenAlex

P atient experience is now accepted as a key element of quality care.In one of two interviews that touch on the patient experience with Esther Green (EG), Richard Ernst (RE) -the CEO of Guelph General Hospital -talks about the full range of efforts that his organization has used to achieve and sustain excellent patient experience ratings.The interview underlies the importance of an organization-wide approach to improvement that touches on processes, human resources, and culture as well as a relentless commitment to improvement that is manifested through regular meetings that track progress.EG: Guelph General Hospital has seen some positive results with respect to improving the patients' experience.Could you tell me, Richard, what you think are the key factors that have really contributed to the change?RE: I'll start by mentioning that we've been tracking patient satisfaction indicators on a dashboard since 2007.Prior to that, we were certainly reporting the information that came out of the hospital report on a regular basis.A key factor was not just tracking the outcomes but also focusing on opportunities for improvement that are routinely identified through these reports.The organization itself has made a commitment to improving our patient experience, and I think one of the best examples is what's transpired in our Emergency Department over the past couple of years.Emergency, as you know, is an entry point to the hospital.Ninety percent of medical patients admitted to our hospital come through our Emergency Department.That's 55,000 ED patients each year, and it's an area of significance to us relative to patient satisfaction.Starting in about February 2009, Guelph General Hospital became involved in a program of process improvement launched by the Ministry of Health and Long-Term Care.The ministry invested resources in providing consultants to help hospitals in the Waterloo-Wellington LHIN try to move the bar on some of the metrics in the Emergency Departments.In our hospital, we introduced a concept of Lean methodology -value-stream mapping.Using front-line staff, we were able to start to make some changes.For example, when we looked at value-stream mapping, one of the key elements is, you don't do things that don't add value to either care providers or care receivers.If you're not doing things that add no value to patients, you are, by default, improving the patient experience.Throughout that time, we had great physician leadership, and we had nurses from the Emergency Department shadowing nurses up in the Medical Unit and vice versa, so they could walk a mile in someone else's shoes.This led to an acknowledgement that patients who come to the hospital aren't Emergency Department patients and they're not Medical Unit patientsthey are our patients.And it wasn't just those two nursing areas either; it was the diagnostic areas, environmental services, and bed allocation.Everybody who's involved in the process that Geulph General Hospital Quality Comittee: (l-r) Eileen Bain, VP Patient Services and Chief Nursing Executive,

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)0.007
metaresearch head score (Gemma)0.013
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: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.010
Scholarly communication0.0050.004
Open science0.0020.008
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.076
GPT teacher head0.436
Teacher spread0.360 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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