Evidence map›Paper›PMID 40417481›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Using a Healthcare Process Modeling Approach to Understand Electronic Health Records-based Pressure Injury Data and to Support Development of a Standardized Pressure Injury Phenotyping Pipeline.

Luwei Liu, Min-Jeoung Kang, Michael Sainlaire, Graham Lowenthal, Tanya Martel, Sandy Cho, Debra Furlong, Wadia Gilles-Fowler, Luciana Schleder Goncalves, Lisa Herlihy and 7 more

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Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Luwei LiuDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Min-Jeoung KangDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Michael SainlaireDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Graham LowenthalDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Tanya MartelCenter for Nursing Excellence, Brigham and Women's Hospital, Boston, MA, USA.
Sandy ChoDepartment of Nursing, Newton-Wellesley Hospital, Newton, MA, USA.
Debra FurlongCenter for Nursing Excellence, Brigham and Women's Hospital, Boston, MA, USA.
Wadia Gilles-FowlerCenter for Nursing Excellence, Brigham and Women's Hospital, Boston, MA, USA.
Luciana Schleder GoncalvesDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Lisa HerlihySalem Hospital, Salem, MA, USA.
Veysel Karani BarisDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Jacqueline MassaroCenter for Nursing Excellence, Brigham and Women's Hospital, Boston, MA, USA.
Beth MelansonCenter for Nursing Excellence, Brigham and Women's Hospital, Boston, MA, USA.
Lori D MorrowSalem Hospital, Salem, MA, USA.
Paula WolskiBrigham and Women's Faulkner Hospital, Jamaica Plain, MA, USA.
Wenyu SongDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Patricia C DykesDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complexity of health care processes present significant challenges for using Electronic Health Records (EHR) data to build high fidelity phenotypes. This study leverages a healthcare process modeling (HPM) approach to enable understanding of EHR-based pressure injury (PrI) data patterns needed for building a standardized PrI phenotyping pipeline. The PrI HPM was developed and validated using mixed methods, including exploratory sequential design, through interdisciplinary collaboration among clinical experts, data scientists, database analysts, and informaticians. zThe qualitative analysis identified the dynamics between PrI care and the associated clinical documentation processes. The quantitative analysis identified inherent challenges and limitations of the PrI data. The PrI HPM includes three moderating factors: system configuration, hospital policy, and nurse's individual workflow. We further incorporated the HPM into the PrI phenotype development process to address phenotyping challenges. Moreover, we suggested a set of standardizable recommendations to address PrI phenotyping challenges.

Indexed as

Electronic Health RecordsPressure UlcerHumansPhenotype

Identifiers

PMID40417481
PMCPMC12099414

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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.