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Record W4399280725 · doi:10.33940/001c.116148

The Impact of Outpatient Parenteral Antimicrobial Therapy (OPAT) in Al Hada Armed Forces Hospital, Taif, Saudi Arabia

2024· article· en· W4399280725 on OpenAlexaff
Jean B. De Asis, Abdulrahman Al Ghamdi, Muhammad Affan Abid, Jamal Al Nofeye, Reynan S. Bautista

Bibliographic record

VenuePatient Safety · 2024
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsImpact
Fundersnot available
KeywordsMedicineAntimicrobialIntensive care medicineMicrobiology

Abstract

fetched live from OpenAlex

Background In the realm of healthcare quality, outpatient parenteral antimicrobial therapy (OPAT) has emerged as the gold standard for managing patients who have transitioned from inpatient care but still require extended intravenous antimicrobial treatment. The adoption of OPAT at Al Hada Armed Forces Hospital in Taif, Saudi Arabia, not only bolsters patient satisfaction but also serves as a catalyst for reduced hospitalization durations, lower rates of emergency department readmissions, and an overall reduction in healthcare expenditures. The main objective of this study was to evaluate the effectiveness of OPAT in a tertiary center facility in Saudi Arabia. Methods In this retrospective investigation, we conducted a thorough review of patient records spanning from November 2020 to October 2021. Our study encompassed all patients who had intravenous antibiotics and were participants in the hospital’s OPAT program during this specific timeframe. Our primary goal was to achieve a 20% reduction in the total number of hospital bed days related to long-term antibiotic therapy. Results The incorporation of OPAT has yielded a multifaceted transformation within the hospital. Over the span of one year, from November 2020 to October 2021, there was a notable decrease in the proportion of patients requiring intravenous antibiotics. This percentage initially dropped from 23% to 12% with the implementation of the OPAT quality improvement project, and later, it reached an even lower 8%. This positive transformation not only had a positive impact on patient care but also led to significant cost savings, exceeding 2 million riyals. These savings were primarily driven by the reduction in hospitalization duration and the more efficient allocation of resources. Moreover, this improvement contributed to the avoidance of 673 patient days of hospitalization, thereby creating additional resources for more critical cases. Conclusion OPAT has emerged as a pivotal component of Al Hada Armed Forces Hospital’s commitment to elevating healthcare quality. This abstract offers a concise insight into the quality-driven impact of OPAT within a specific healthcare context, underlining its capacity to optimize patient care, enhance healthcare efficiency, and elevate resource allocation. Ongoing research and continuous evaluation will play a critical role in refining and expanding the OPAT program while preserving its quality-oriented perspective.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.328
Teacher spread0.297 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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