Evidence map›Paper›PMID 30526567›Full record

ArticleBMC medical informatics and decision making2018

A prototype of knowledge-based patient safety event reporting and learning system.

Hong Kang, Sicheng Zhou, Bin Yao, Yang Gong

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.4field-weighted citation impact, top 8% 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

2 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. 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

4 authors at 1 institution in 1 country.

Hong KangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, USA.
Sicheng ZhouSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, USA.
Bin YaoSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, USA.
Yang GongSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, USA. Yang.Gong@uth.tmc.edu.
The University of Texas Health Science Center at Houston · US

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

backgroundPatient falls, the most common safety events resulting in adverse patient outcomes, impose significant costs and have become a great burden to the healthcare community. Current patient fall reporting systems remain in the early stage that is far away from reaching the ultimate goal toward a safer healthcare. According to the Kirkpatrick model, the key challenge in reaction, learning, behavior and results is the realization of learning stage due to the lack of knowledge management, sharing and growing mechanism.

methodsBased on the key contributing factors defined by AHRQ Common Formats 2.0, a hierarchical list of contributing factors for patient falls was established by expert review and discussion. Using the list as an infrastructure, we designed and developed a novel reporting system, where a strategy to identify contributing factors is intended to provide reporters knowledge support, in the form of similar cases and potential solutions. A survey containing two scenarios was conducted to evaluate the learning effect of our system.

resultsIn both scenarios, potential solutions recommended by the system were annotated with correct contributing factors, and presented only when the corresponding factors were identified from the query report or selected by the user. The five experts show substantial consistency (Fleiss' kappa > 0.6) and high agreement (ranging between fully agree and mostly agree) in the assessment of the three perspectives of the system, which verifies the effectiveness of the proposed knowledge support toward sharing and learning through the novel reporting system.

conclusionsThis study proposed a profile of contributing factors that could measure the similarity of patient safety events. Based on the profile, a knowledge-based reporting and learning system was developed to bridge the gap between surveillance, reporting, and retrospective analysis in the fall management circle. The system holds promise in improving event reporting toward better and safer healthcare.

Indexed as

Accidental FallsInformation Storage and RetrievalKnowledge BasesPatient SafetyHumansInformation storage and retrievalKnowledge basePatient safety

Identifiers

PMID30526567
PMCPMC6284264
OpenAlexW2905097734

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.