Evidence map›Paper›PMID 32524087›Full record

ArticleProceedings. IEEE International Conference on Healthcare Informatics2019

Obstetric Patients with Repetitious Hospital Location Transfers Have Prolonged Stays.

Cheng Gao, Abel N Kho, Sarah Osmundson, Bradley A Malin, You Chen

Open access · greenAbstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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 at 2 institutions in 1 country.

Cheng GaoDepartment of Biomedical Informatics Vanderbilt University.
Abel N KhoInstitute for Public Health and Medicine Northwestern University.
Sarah OsmundsonDepartment of Obstetrics and Gynecology Vanderbilt University Medical Center.
Bradley A MalinDepartment of Biomedical Informatics Vanderbilt University.
You ChenDepartment of Biomedical Informatics Vanderbilt University.
Vanderbilt University · USVanderbilt University Medical Center · US

Funding

Automated Detection of Anomalous Accesses to Electronic Health RecordsR01LM010207 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MALIN, BRADLEY A. · 2009 to 2017
$2.4M
Learning Patterns of Collaboration to Optimize the Management of Care ProvidersR00LM011933 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CHEN, YOU · 2016 to 2018
$738k
NLM NIH HHS R00 LM011933NLM NIH HHS R01 LM010207
6 · The paper itself

Abstract

There is a general belief that the workflow of surrounding location transfers between locations documented in electronic health record (EHR) during hospitalization is associated with a patient's length of stay (LOS). However, this belief has had little formal investigation in a data-driven manner. Location transfers in patients' hospitalization are hypothesized to be related to LOS. The objective of this study is to assess this relationship, using data derived from the EHR system of a large hospital system, with a focus on the obstetric setting - a clinical environment that exhibits wide swing in resource utilization. We designed a data-driven framework to infer patterns of location transfers and developed a zero-truncated negative binomial model, adjusting for demographics and billed diagnoses, to learn the association between patterns of location transfers and LOS. Indicative factors found to be of indicative of location transfer patterns were further investigated via their odds ratios, Pearson Correlation Coefficients, and Chi-squared test. We evaluated our approach with two years of data on from 5,774 obstetric inpatient encounters from the EHR system of Northwestern Memorial Hospital. The results indicated that the average LOS for patients with patterns of repetitious location transfers (RLTs) was 4.25 days (95% confidence interval [4.02, 4.47]) longer than patients with no RLT. This difference reduced to 3.62 days (95% confidence interval [3.61, 3.64]) after adjusting for age, race and billed diagnoses. We further discovered 21 indicative factors of RLT (statistically significant with a significance level of 0.05), in the form of billed diagnosis codes, each of which exhibited an odds ratio larger than 4. This study suggests that RLT patterns are associated with a prolonged LOS in the obstetric setting. As such, healthcare organizations may need to pay more attention to patients with RLTs to refine location transfers workflow and to boost efficiency in obstetric care.

Indexed as

EHRlength of staylocation transferobstetrics

Identifiers

PMID32524087
PMCPMC7286595
OpenAlexW2989666307

What OpenQuestion holds

Textmetadata
LicenceTDM
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.