Evidence map›Paper›PMID 37868087›Full record

ReviewEJIFCC2023

Best Practices for Effective Management of Point of Care Testing.

Anil K Chokkalla, Brandy D Recio, Sridevi Devaraj

Open access · greenAbstract readReview
In one paragraph

Review in EJIFCC, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 18% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Point of care antimicrobial susceptibility testing.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
    Review
  4. Review
  5. Article
  6. Article
  7. Review
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

3 authors at 2 institutions in 1 country.

Anil K ChokkallaDepartment of Pathology & Immunology, Baylor College of Medicine, Houston, TX, USA.
Brandy D RecioDepartment of Pathology, Texas Children's Hospital, Houston, TX, USA.
Sridevi DevarajDepartment of Pathology & Immunology, Baylor College of Medicine, Houston, TX, USA.
Baylor College of Medicine · USTexas Children's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the recent COVID-19 pandemic, point-of-care testing has gained tremendous attention, particularly in acute care settings. The point-of-care testing landscape is rapidly expanding and being contemplated for any crucial test with a central laboratory turnaround time >25% of the clinical decision time. A typical point-of-care testing program within a large hospital system encompasses a multitude of operators utilizing a wide range of devices across multiple testing sites. Thus, managing a large point-of-care testing network remains a daunting task with challenges related to staffing, standardization, quality management, training and competency assessment, and data management. This review will focus on understanding the general organization as well as the roles and responsibilities of various point-of-care testing stakeholders in addressing these challenges. More importantly, it will discuss the strategies and best practices for effective point-of-care testing management based on consensus recommendations from professional societies as well as our experience at Texas Childrens Hospital.

Indexed as

clinical collaborationlaboratory managementPoint-of-care tesingquality management

Identifiers

PMID37868087
PMCPMC10588082
OpenAlexW4387866150

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.