Evidence map›Paper›PMID 31984352›Full record

ArticleJAMIA open2019

Toward safer health care: a review strategy of FDA medical device adverse event database to identify and categorize health information technology related events.

Hong Kang, Ju Wang, Bin Yao, Sicheng Zhou, Yang Gong

Abstract read
In one paragraph

Article in JAMIA open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. 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

5 authors.

Hong KangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Ju WangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Bin YaoSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Sicheng ZhouSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Yang GongSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, 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

introductionHealth information technology (HIT) is intended to provide safer and better care to patients. However, poorly designed or implemented HIT poses a key risk to patient safety. It is essential for healthcare providers and researchers to investigate the HIT-related events. Unfortunately, the lack of HIT-related event databases in the community hinders the analysis and management of HIT-related events.

objectivesDevelop a standardized process for identifying HIT-related events from a Federal Drug Administration (FDA) database in order to create an HIT exclusive database for analysis and learning.

methodsThe FDA Manufacturer and User Facility Device Experience (MAUDE) database, containing over 7-million reports about medical device malfunctions and problems leading to serious injury or death, was considered as a potential resource to identify HIT-related events. We developed a strategy of identifying and categorizing HIT-related events from the FDA reports through the application of a keyword filter and standardized expert review. Ten percent identified reports were reviewed to measure the consistency among experts and to initialize a database for HIT-related events.

resultsWith the proposed strategy, we initialized an HIT-related event database with over 3500 reports, and updated the estimation of the HIT-related event proportion in the FDA MAUDE database to 0.46∼0.69%, up to 50,000 HIT-related events.

conclusionThe proposed strategy for HIT-related event identification holds promise in aiding the understanding, characterization, discovery, and reporting of HIT-related events toward improved patient safety. The analysis of contributing factors under the 8-dimensional sociotechnical model shows that hardware and software, clinical content, and human-computer interface were identified more frequently than the other dimensions.

Indexed as

databasehealth information technologypatient safety

Identifiers

PMID31984352
PMCPMC6951922

What OpenQuestion holds

Textmetadata
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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.