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Identification of Quality of Care Deficiencies in Elderly Surgical Patients by Measuring Adherence to Process-Based Quality Indicators

2013· article· en· W1973821713 on OpenAlexaff
Simon Bergman, Vanessa Martelli, M. Monette, Nadia Sourial, Mélina Deban, Fadi Hamadani, Debby Teasdale, Christina Holcroft, Helena Zakrzewski, Shannon A. Fraser

Bibliographic record

VenueJournal of the American College of Surgeons · 2013
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsUniversité de MontréalMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineQuality ScoreCohortEmergency medicineQuality managementRetrospective cohort studyDeliriumComorbidityQuality (philosophy)Physical therapySurgeryIntensive care medicineInternal medicineOperations management

Abstract

fetched live from OpenAlex

In Brief BACKGROUND: The ability to measure surgical quality of care is important and can lead to improvements in patient safety. As such, processes should be carried out in an identical fashion for all patients, regardless of how vulnerable or complex they are. Our objectives were to assess quality of surgical care delivered to elderly patients and to determine the association between patient characteristics and quality of care. STUDY DESIGN: This is a retrospective pilot cohort study, conducted in a single university-affiliated hospital. Using the institution's National Surgical Quality Improvement Program (NSQIP) database (2009 to 2010), 143 consecutive patients 65 years or older, undergoing elective major abdominal surgery, were selected. Adherence to 15 process-based quality indicators (QIs) was measured, and a pass rate was calculated for each individual QI. The association between patient characteristics (age, sex, Charlson Comorbidity Index, functional status, wound class) and patient quality score was assessed using multiple linear regression. RESULTS: Quality indicators with the lowest pass rates included postoperative delirium screening (0%), level of care documentation (0.7%), cognition and functional assessment at discharge (4.9%), oral intake documentation (12.6%), and pressure ulcer risk assessment (35.0%). The mean patient quality score was 46.8% ± 10.7% (range 16.7% to 75.0%). No association was found between patient characteristics and patient quality score. CONCLUSIONS: Quality of care delivered to elderly patients undergoing major surgery at our institution was generally poor and independent of patient characteristics. Although quality appears to be uniform across different patients, these results provide targets for quality improvement initiatives. The quality of care delivered to elderly patients undergoing major surgery at our hospital was generally poor (mean quality score 46.8% ± 10.7%) and independent of patient characteristics. Low adherence to process-based quality indicators (QIs) revealed weaknesses in the documentation of QIs and provide targets for quality improvement initiatives.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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 designObservational
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

Citations27
Published2013
Admission routes1
Has abstractyes

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