Evidence map›Paper›PMID 38756367›Full record

ArticleHealth affairs scholar2023

Characterizing hospitalization trajectories in the high-need, high-cost population using electronic health record data.

Scott S Lee, Benjamin French, Francis Balucan, Michael D McCann, Eduard E Vasilevskis

Abstract read
In one paragraph

Article in Health affairs scholar, 2023. 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
–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

1 citing paper in PubMed.

  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.

Scott S LeeSection of Hospital Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States.ORCID https://orcid.org/0000-0002-0970-1941
Benjamin FrenchDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.ORCID https://orcid.org/0000-0001-9265-5378
Francis BalucanSection of Hospital Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States.ORCID https://orcid.org/0000-0002-3306-2692
Michael D McCannSection of Hospital Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States.ORCID https://orcid.org/0000-0001-9425-1350
Eduard E VasilevskisDivision of Hospital Medicine, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53726, United States.ORCID https://orcid.org/0000-0001-8165-5321

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High utilization by a minority of patients accounts for a large share of health care costs, but the dynamics of this utilization remain poorly understood. We sought to characterize longitudinal trajectories of hospitalization among adult patients at an academic medical center from 2017 to 2023. Among 3404 patients meeting eligibility criteria, following an initial "rising-risk" period of 3 hospitalizations in 6 months, growth mixture modeling discerned 4 clusters of subsequent hospitalization trajectories: no further utilization, low chronic utilization, persistently high utilization with a slow rate of increase, and persistently high utilization with a fast rate of increase. Baseline factors associated with higher-order hospitalization trajectories included admission to a nonsurgical service, full code status, intensive care unit-level care, opioid administration, discharge home, and comorbid cardiovascular disease, end-stage kidney or liver disease, or cancer. Characterizing hospitalization trajectories and their correlates in this manner lays groundwork for early identification of those most likely to become high-need, high-cost patients.

Indexed as

electronic health record datagrowth mixture modelinghigh utilizershospital utilization

Identifiers

PMID38756367
PMCPMC10986247

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.