Evidence map›Paper›PMID 25848422›Full record

ArticleApplied clinical informatics2015

Hospital closure and insights into patient dispersion: the closure of Saint Vincent's Catholic Medical Center in New York City.

N Garg, G Husk, T Nguyen, A Onyile, S Echezona, G Kuperman, J S Shapiro

Open access · bronzeAbstract read
In one paragraph

Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 29% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Simulation for Operational Readiness in a New Freestanding Emergency Department: Strategy and Tactics.Simulation in healthcare : journal of the Society for Simulation in Healthcare · 2016
    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

7 authors at 3 institutions in 1 country.

N GargIcahn School of Medicine at Mount Sinai, Emergency Medicine , New York, New York, United States.
G HuskMount Sinai - Beth Israel, Emergency Medicine , New York, New York, United States.
T NguyenMount Sinai - Beth Israel, Emergency Medicine , New York, New York, United States.
A OnyileIcahn School of Medicine at Mount Sinai, Emergency Medicine , New York, New York, United States.
S EchezonaMount Sinai - Beth Israel, Emergency Medicine , New York, New York, United States.
G KupermanDepartment of Information Systems, New York-Presbyterian Hospital , New York, New York, United States ; Department of Biomedical Informatics, Columbia University , New York, New York, United States.
J S ShapiroIcahn School of Medicine at Mount Sinai, Emergency Medicine , New York, New York, United States.
Icahn School of Medicine at Mount Sinai · USMount Sinai Beth Israel · USNewYork–Presbyterian Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHospital closures are becoming increasingly common in the United States. Patients who received care at the closing hospitals must travel to different, often farther hospitals for care, and nearby remaining hospitals may have difficulty coping with a sudden influx of patients.

objectivesOur objectives are to analyze the dispersion patterns of patients from a closing hospital and to correlate that with distance from the closing hospital for three specific visit types: emergency, inpatient, and ambulatory.

methodsIn this study, we used data from a health information exchange to track patients from Saint Vincent's Medical Center, a hospital in New York City that closed in 2010, to determine where they received emergency, inpatient, and ambulatory care following the closure.

resultsWe found that patients went to the next nearest hospital for their emergency and inpatient care, but ambulatory encounters did not correlate with distance. DISCUSSION: It is likely that patients followed their ambulatory providers as they transitioned to another hospital system. Additional work should be done to determine predictors of impact on nearby hospitals when another hospital in the community closes in order to better prepare for patient dispersion.

Indexed as

Health Facility ClosureHospitalsHumansNew York CityPatientsTime Factorshealth facility closureHealth information systemshealth policyhealth services researchmedical informatics

Identifiers

PMID25848422
PMCPMC4377569
OpenAlexW1629440557

What OpenQuestion holds

Textmetadata
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.