Identifying Patients at Risk of High Healthcare Utilization.

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.

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.

Who cites it

4 citing papers in PubMed.

1. Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?BMC health services research · 2022
   Article
2. Individual-Level and Neighborhood-Level Factors Associated with Longitudinal Changes in Cardiometabolic Measures in Participants of a Clinic-Based Care Coordination Program: A Secondary Data Analysis.Journal of clinical medicine · 2022
   Article
3. Medical care needs for patients receiving home healthcare in Taiwan: Do gender and income matter?PloS one · 2021
   Article
4. Assessing the validity of a data driven segmentation approach: A 4 year longitudinal study of healthcare utilization and mortality.PloS one · 2018
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

6 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as