Evidence map›Paper›PMID 30815207›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018

Analyzing Medication Error Reports in Clinical Settings: An Automated Pipeline Approach.

Sicheng Zhou, Hong Kang, Bin Yao, Yang Gong

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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. Review
  2. Review
  3. Assessment and analysis of outpatient medication errors related to pediatric prescriptions.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2021
    Article
  4. Article
  5. Article
  6. Review
  7. Article
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

4 authors.

Sicheng ZhouUniversity of Texas Health Science Center at Houston, Houston, TX, USA.
Hong KangUniversity of Texas Health Science Center at Houston, Houston, TX, USA.
Bin YaoUniversity of Texas Health Science Center at Houston, Houston, TX, USA.
Yang GongUniversity of Texas Health Science Center at Houston, Houston, TX, USA.

Funding

Learning from patient safety events: A case base tool kitR01HS022895 · AHRQ · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI GONG, YANG · 2014 to 2018
$1.2M
AHRQ HHS R01 HS022895
6 · The paper itself

Abstract

Medication error is a severe patient safety event in the United States. Medication error reports collected by Patient Safety Organizations provide an opportunity to analyze and learn from previous errors. However, the current workflow of analyzing the error reports is labor-intensive and time-consuming. To reduce the workloads for clinicians and save time, we developed a pipeline for medication error report pre-analysis by applying automated text classification techniques. The pipeline was proven functional in two tasks, i.e., identifying the error originated stages, error types and error causes from the medication error reports, and calculating the similarity scores between the error reports for re-organization. The proposed pipeline holds promise in helping clinicians understand the nature of medication error in an error report, and better manage the error reports, which could further facilitate the prevention of medication errors in healthcare settings.

Indexed as

Medication ErrorsWorkflowData MiningHumansPatient SafetyUnited StatesWorkload

Identifiers

PMID30815207
PMCPMC6371341

What OpenQuestion holds

Textmetadata
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.