Evidence map›Paper›PMID 42781653›Full record

ArticleJournal of medical biochemistry2026

The effect of auto-verification on turnaround time for clinical chemistry and immunoassay tests.

Havva Yasemin Çinpolat

Abstract read
In one paragraph

Article in Journal of medical biochemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Havva Yasemin ÇinpolatÇanakkale Onsekiz Mart University, Faculty of Medicine, Department of Medical Biochemistry, Çanakkale, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Auto-verification is increasingly recognised as a key tool for improving quality and efficiency in clinical laboratories. This study aimed to investigate the impact of an auto-verification system implemented for clinical chemistry and immunoassay tests on turnaround time (TAT) in a tertiary-care medical biochemistry laboratory. Methods: This study was conducted in the Medical Biochemistry Laboratory of Çanakkale Onsekiz Mart University Hospital, a tertiary healthcare institution. Clinical chemistry and immunoassay tests were automated and verified using the navify® Lab Operations middleware (Roche Diagnostics, Germany), integrated with the MIA-MED Laboratory Information System (MIA Technology, Türkiye). Algorithms were developed in accordance with CLSI AUTO10-A and AUTO 15 guidelines, incorporating rules for quality control, serum indices, analyser flags, delta checks, critical values, consistency checks, and analytical measurement intervals, as well as recommendations from the national health authorities. Validation of the algorithms was carried out using both simulated and patient data. The proportion of results exceeding predefined TAT targets was compared before and after auto-verification implementation using the chi-square test. p< 0.05 was considered statistically significant. Results: Overall, 71% of test results and 21% of tube-based results were verified automatically. Median TAT was reduced by 6 minutes for emergency tests and 12 minutes for routine tests. The proportion of results exceeding the TAT threshold decreased significantly from 6.4% before auto-verification to 5.8% after auto-verification implementation (p< 0.001). Conclusions: Auto-verification, with clearly defined and validated rules, enhances both the reliability and timeliness of laboratory results, thereby supporting quality improvement initiatives in clinical laboratories.

Indexed as

algorithmsautoverificationinformation technologyquality improvementturnaround time (TAT)

Identifiers

PMID42781653
PMCPMC13600552

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.