Evidence map›Paper›PMID 35851287›Full record

ArticleBMJ health & care informatics2022

Translating electronic health record-based patient safety algorithms from research to clinical practice at multiple sites.

Andrew J Zimolzak, Hardeep Singh, Daniel R Murphy, Li Wei, Sahar A Memon, Divvy K Upadhyay, Saritha Korukonda, Lisa Zubkoff, Dean F Sittig

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2022. 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
–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

2 citing papers in PubMed.

  1. Trial
  2. 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

9 authors.

Andrew J ZimolzakCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center, Houston, Texas, USA.ORCID http://orcid.org/0000-0003-0973-5639
Hardeep SinghCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center, Houston, Texas, USA.ORCID http://orcid.org/0000-0002-4419-8974
Daniel R MurphyCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center, Houston, Texas, USA.
Li WeiCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center, Houston, Texas, USA.
Sahar A MemonCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center, Houston, Texas, USA.
Divvy K UpadhyayDivision of Quality, Safety and Patient Experience, Geisinger, Danville, PA, USA.
Saritha KorukondaResearch Institute, Geisinger, Danville, PA, USA.
Lisa ZubkoffGeriatric Research Education and Clinical Center, Birmingham VA Medical Center, Birmingham, Alabama, USA.
Dean F SittigSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA dean.f.sittig@uth.tmc.edu.ORCID http://orcid.org/0000-0001-5811-8915

Funding

Application of a Machine Learning to Enhance e-Triggers to Detect and Learn from Diagnostic Safety EventsR01HS027363 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI SINGH, HARDEEP · 2019 to 2021
$1.5M
AHRQ HHS R01 HS027363
6 · The paper itself

Abstract

introductionResearchers are increasingly developing algorithms that impact patient care, but algorithms must also be implemented in practice to improve quality and safety.

objectiveWe worked with clinical operations personnel at two US health systems to implement algorithms to proactively identify patients without timely follow-up of abnormal test results that warrant diagnostic evaluation for colorectal or lung cancer. We summarise the steps involved and lessons learned.

methodsTwelve sites were involved across two health systems. Implementation involved extensive software documentation, frequent communication with sites and local validation of results. Additionally, we used automated edits of existing code to adapt it to sites' local contexts.

resultsAll sites successfully implemented the algorithms. Automated edits saved sites significant work in direct code modification. Documentation and communication of changes further aided sites in implementation.

conclusionPatient safety algorithms developed in research projects were implemented at multiple sites to monitor for missed diagnostic opportunities. Automated algorithm translation procedures can produce more consistent results across sites.

Indexed as

Electronic Health RecordsPatient SafetyAlgorithmsDocumentationHumansElectronic Health RecordsHealth Services ResearchMedical InformaticsPatient Care

Identifiers

PMID35851287
PMCPMC9289019

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.