Evidence map›Paper›PMID 37454280›Full record

ArticleInflammatory bowel diseases2024

Machine Learning-based Characterization of Longitudinal Health Care Utilization Among Patients With Inflammatory Bowel Diseases.

Berkeley N Limketkai, Laura Maas, Mahesh Krishna, Anoushka Dua, Lauren DeDecker, Jenny S Sauk, Alyssa M Parian

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Inflammatory bowel diseases, 2024. 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
–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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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.

Berkeley N LimketkaiCenter for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA.ORCID 0000-0003-2714-6934
Laura MaasDivision of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Mahesh KrishnaDivision of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Anoushka DuaCenter for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA.
Lauren DeDeckerCenter for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA.
Jenny S SaukCenter for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA.
Alyssa M ParianDivision of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInflammatory bowel disease (IBD) is associated with increased health care utilization. Forecasting of high resource utilizers could improve resource allocation. In this study, we aimed to develop machine learning models (1) to cluster patients according to clinical utilization patterns and (2) to predict longitudinal utilization patterns based on readily available baseline clinical characteristics.

methodsWe conducted a retrospective study of adults with IBD at 2 academic centers between 2015 and 2021. Outcomes included different clinical encounters, new prescriptions of corticosteroids, and initiation of biologic therapy. Machine learning models were developed to characterize health care utilization. Poisson regression compared frequencies of clinical encounters.

resultsA total of 1174 IBD patients were followed for more than 5673 12-month observational windows. The clustering method separated patients according to low, medium, and high resource utilizers. In Poisson regression models, compared with low resource utilizers, moderate and high resource utilizers had significantly higher rates of each encounter type. Comparing moderate and high resource utilizers, the latter had greater utilization of each encounter type, except for telephone encounters and biologic therapy initiation. Machine learning models predicted longitudinal health care utilization with 81% to 85% accuracy (area under the receiver operating characteristic curve 0.84-0.90); these were superior to ordinal regression and random choice methods.

conclusionMachine learning models were able to cluster individuals according to relative health care resource utilization and to accurately predict longitudinal resource utilization using baseline clinical factors. Integration of such models into the electronic medical records could provide a powerful semiautomated tool to guide patient risk assessment, targeted care coordination, and more efficient resource allocation.

Indexed as

Inflammatory Bowel DiseasesMachine LearningPatient Acceptance of Health CareAdultFemaleHumansLongitudinal StudiesMaleMiddle AgedRetrospective Studieshealth care utilizationmachine learningquality improvement

Identifiers

PMID37454280
PMCPMC11491632

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.