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S1961 Quality Improvement in Decompensated Cirrhosis: Standardizing the Admission Process

2024· article· en· W4403721459 on OpenAlexaboutno aff
Laura E. Lavette, Hannah Laird, Mira Sridharan, Jessica J. Dreicer, Andrew Barros, Zachary Henry

Bibliographic record

VenueThe American Journal of Gastroenterology · 2024
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCirrhosisProcess (computing)Quality managementIntensive care medicineQuality (philosophy)Internal medicineOperations management

Abstract

fetched live from OpenAlex

Introduction: Patients with decompensated cirrhosis have severe underlying disease, leading to increased hospitalizations. Previous Quality Improvement (QI) projects have shown that standardized order sets are associated with decreased re-admissions, medical errors, and mortality.1 We performed a review of patients with decompensated cirrhosis and identified key areas for improvement at time of admission. Based on our findings, we generated an admission order set with the aim to 1) streamline provider’s practice, 2) encourage evidence-based medicine, and 3) improve patient outcomes. Methods: We performed a retrospective review of patients from 1/1/22 to 9/1/23 at a single tertiary care center. All patients had a diagnosis of decompensated cirrhosis prior to admission. We used findings from our pre-intervention data along with evidence-based practice guidelines to construct a cirrhosis admission order set for providers to use at time of admission or at any point during a patient’s hospitalization. Results: There were 229 patients who met inclusion criteria, and 156 (68%) were admitted to General Medicine and 73 (32%) to the Intensive Care Unit. One hundred twenty-three (54%) received a diagnostic paracentesis during current hospitalization; the average time from admission to paracentesis was 7.5 hours. The average time to collection of MELD labs was 8 hours. One hundred thirty-four (59%) were started on an appropriate cirrhosis diet inpatient. Forty-one (18%) died or were converted to hospice. Our order set, “Cirrhosis Management Panel,” was created as a stand-alone order set and was embedded in the “General Admission” order set. It consisted of 8 categories, many being common complications of portal hypertension (Figure 1). Pre-selected categories included diet, paracentesis labs, and cirrhosis education. The remainder of the categories were not automatically selected and left to user discretion. Conclusion: QI efforts have been shown to standardize care between providers and across medical systems. We created an admission order set tailored towards patients with decompensated cirrhosis with the goal of optimizing evidence-based, quality care. Future work will include collecting post-intervention data to evaluate the order set’s impact on patient outcomes. We believe our order set is generalizable to other institutions and is important in encouraging conversations about how to promote QI within the cirrhosis population. Reference 1. Wells C, Loshak H. Standardized hospital Order sets in acute care: A review of clinical evidence, cost-effectiveness, and guidelines [Internet]. Ottawa (ON): Canadian Agency for Drugs and Technologies in Health; 2019.Figure 1.: Cirrhosis admission order set categories including (A) Ascites Management, (B) Paracentesis Management, (C) Acute Kidney Injury (AKI) Management, (D) Gastrointestinal (GI) Bleed Management, (E) Hepatic Encephalopathy Management, (F) Labs, Consults, and Education.

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)0.046
metaresearch head score (Gemma)0.114
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 score0.046
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.114
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.009
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.334
Teacher spread0.320 · 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
Published2024
Admission routes1
Has abstractyes

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