Evidence map›Paper›PMID 35672994›Full record

ArticleStudies in health technology and informatics2022

The Implication of Latent Information Quality to the Reproducibility of Secondary Use of Electronic Health Records.

Sunyang Fu, Andrew Wen, Sandeep Pagali, Nansu Zong, Jennifer St Sauver, Sunghwan Sohn, Jungwei Fan, Hongfang Liu

Abstract read
In one paragraph

Article in Studies in health technology and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  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

8 authors.

Sunyang FuDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA.
Andrew WenDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA.
Sandeep PagaliDepartment of Medicine, Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA.
Nansu ZongDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA.
Jennifer St SauverDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
Sunghwan SohnDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA.
Jungwei FanDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA.
Hongfang LiuDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA.

Funding

Early Detection of Mild Cognitive Impairment, Alzheimer’s Disease and Other Dementias using EHRR01AG068007 · NIA · MAYO CLINIC ROCHESTER · PI Yonas E Geda, Sunghwan Sohn · 2020 to 2026
$3.8M
Interdisciplinary Infrastructure for Aging Research: Rochester Epidemiology ProjectR33AG058738 · NIA · MAYO CLINIC ROCHESTER · PI LEBRASSEUR, NATHAN K, OLSON, JANET E · 2020 to 2022
$2.4M
Secondary use of EMR for residency training in asthma care and documentationR21AI142702 · NIAID · MAYO CLINIC ROCHESTER · PI JUHN, YOUNG J, SOHN, SUNGHWAN · 2019 to 2020
$437k
NIAID NIH HHS R21 AI142702NIA NIH HHS R01 AG068007NIA NIH HHS R33 AG058738
6 · The paper itself

Abstract

Reproducibility is an important quality criterion for the secondary use of electronic health records (EHRs). However, multiple barriers to reproducibility are embedded in the heterogeneous EHR environment. These barriers include complex processes for collecting and organizing EHR data and dynamic multi-level interactions occurring during information use (e.g., inter-personal, inter-system, and cross-institutional). To ensure reproducible use of EHRs, we investigated four information quality dimensions and examine the implications for reproducibility based on a real-world EHR study. Four types of IQ measurements suggested that barriers to reproducibility occurred for all stages of secondary use of EHR data. We discussed our recommendations and emphasized the importance of promoting transparent, high-throughput, and accessible data infrastructures and implementation best practices (e.g., data quality assessment, reporting standard).

Indexed as

Electronic Health RecordsReproducibility of ResultsEHRQuality ImprovementReproducibility

Identifiers

PMID35672994
PMCPMC9754076

What OpenQuestion holds

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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.