Evidence map›Paper›PMID 39947738›Full record

ArticleBMJ open quality2025

Successful posthospitalisation oncology workflow implementing LACE+ score to stratify and reduce readmissions within a safety-net hospital.

Cindy M Pabon, Janine Wong, Marcela Perez, Jessica Jimenez-Abarca, Lianchun Xiao, Lymesia Jackson, Anne Park, Anjali Lankford, Vinita Akula, Saumil Datar and 4 more

Abstract read
In one paragraph

Article in BMJ open quality, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

14 authors.

Cindy M PabonGeneral Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA cindymed6@gmail.com.ORCID http://orcid.org/0000-0002-9755-9374
Janine WongAccreditation & Regulatory Affairs, Harris Health System, Houston, Texas, USA.
Marcela PerezGeneral Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Jessica Jimenez-AbarcaGeneral Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Lianchun XiaoBiostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Lymesia JacksonAmbulatory Care Management, Harris Health System, Houston, Texas, USA.
Anne ParkClinical Informations, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Anjali LankfordInternal Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Vinita AkulaInternal Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Saumil DatarInternal Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Taylor HopperInternal Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Yaser AlkhatibGeneral Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Terri EarlesGeneral Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Tejal PatelGeneral Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with advanced cancers visit the emergency room and get hospitalised frequently, with potentially half of these visits being avoidable. Our institution provides comprehensive, low-cost cancer treatment to a safety-net population in Texas. We performed a retrospective review of hospital readmission patterns amongst our oncology patients and developed a posthospitalisation workflow to reduce readmissions.

methodFollowing discharge, oncology patients were risk stratified based on their Length of stay, Acuity of admission, Charlson comorbidity index score and Emergency department visits+index in the past 6 months. The higher the score, the quicker the outpatient oncology follow-up. In addition to addressing acute issues related to hospitalisation, patients were also able to receive newly translated resources while in clinic.

resultsThe preintervention 30-day-readmission rate was 17.3% (June 2022-December 2022) (95% CI 13.4% to 21.8%). Meanwhile, the postintervention 30-day-readmission rate was 14.7% (June 2023-December 2023) (95% CI 10.9% to 19.2%). While a 2.6% reduction in readmissions was achieved, this decrease was not statistically significant (-2.6%; 95% CI -8.4% to 3.2%; p value=0.375). Emergency use utilisation decreased from 90% to 15%.

conclusionsOur team was able to facilitate and coordinate outpatient care for oncology patients following hospitalisation. The expedited care allowed providers to ensure that the care plan after hospitalisation was well understood and accessible to the patient. Furthermore, language-appropriate resources were provided to patients at that time. Our intervention was feasible, easy to implement and quick to produce tangible improvements in patient care. More time is needed to determine whether this will create a statistically significant impact on readmission rates.

Indexed as

Medical OncologyPatient ReadmissionWorkflowAgedEmergency Service, HospitalFemaleHumansMaleMiddle AgedNeoplasmsRetrospective StudiesSafety-net ProvidersTexasAmbulatory careContinuity of Patient CarePatient-centred carePatient DischargeQuality improvement

Identifiers

PMID39947738
PMCPMC11831318

What OpenQuestion holds

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LicenceCC BY-NC
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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.