Evidence map›Paper›PMID 33694072›Full record

Trial reportJournal of general internal medicine2021

Predictive Model-Driven Hotspotting to Decrease Emergency Department Visits: a Randomized Controlled Trial.

Brady Post, Jeremy Lapedis, Karandeep Singh, Paul Valenstein, Ayşe G Büyüktür, Karin Teske, Andrew M Ryan

Registry-linked trialOpen access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of general internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03293160 (An Evaluation of the Michigan State Innovation Model in Washtenaw and Livingston Counties), which is not on this map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.2field-weighted citation impact, top 13% 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.

NCT03293160 completednot on this map

An Evaluation of the Michigan State Innovation Model in Washtenaw and Livingston Counties

TypeobservationalSponsorUniversity of MichiganRan2017 to 2019Enrolled300ConditionsHigh Utilization of Emergency DepartmentsArmsCare management services
3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 11 citations in OpenAlex.

  1. Trial
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  8. Assessing the net benefit of machine learning models in the presence of resource constraints.Journal of the American Medical Informatics Association : JAMIA · 2023
    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.

Brady PostNortheastern University Bouvè College of Health Sciences, Boston, MA, USA.
Jeremy LapedisWashtenaw Health Plan, Ypsilanti, MI, USA.
Karandeep SinghUniversity of Michigan Medical School, Ann Arbor, MI, USA.
Paul ValensteinUniversity of Michigan Center for Health Research and Transformation, Ann Arbor, MI, USA.
Ayşe G BüyüktürUniversity of Michigan School of Information, Ann Arbor, MI, USA.
Karin TeskeUniversity of Michigan Center for Health Research and Transformation, Ann Arbor, MI, USA.
Andrew M RyanUniversity of Michigan School of Public Health, Ann Arbor, MI, USA. amryan@umich.edu.ORCID 0000-0002-2566-7763
University of Michigan · USNortheastern University · USWashtenaw Community College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmergency department (ED) visits contribute substantially to health care expenditures. Case management has been proposed as a strategy to address the medical and social needs of complex patients. However, strong research designs to evaluate the effectiveness of such interventions are limited.

objectivesTo evaluate whether a community-based case management program was associated with reduced ED utilization among complex patients.

designPatients whose risk exceeded a threshold were randomly assigned to a group offered case management or to the control group. Assignment occurred at five intervals between November 2017 and January 2019. Program effectiveness for all assigned patients was assessed using an intention-to-treat effect. Program effectiveness among those who received treatment was assessed using a local average treatment effect, estimated using instrumental variables. Both estimators were adjusted for baseline characteristics using linear models.

participantsAdults over age 18 with at least one health care encounter with Michigan Medicine or St. Joseph Mercy Health System between June 2, 2016, and November 27, 2018.

interventionsIntervention arm participants (n = 486) were offered coordinated case management across medical, mental health, and social service organizations. Control arm participants (n = 409) received usual care. MAIN MEASURES: The primary outcome was the number of ED visits in the 6 months following randomization into the study. Secondary outcomes were 6-month counts of inpatient and outpatient visits. KEY

resultsOf the 486 patients assigned to the intervention, 131 (27%) consented to receive case management. The intention-to-treat effect on ED visits was + 0.14 (95% CI: - 0.27 to + 0.55). The local average treatment effect among those who consented and received case management was + 0.53 (95% CI: - 1.00 to + 2.05). Intention-to-treat and local average treatment effects were not significant for secondary outcomes.

conclusionsThe community case management intervention targeting ED visits was not associated with reduced utilization. Future case management interventions may benefit from additional patient engagement strategies and longer evaluation time periods.

trial registrationClinicaltrials.gov Identifier: NCT03293160.

Indexed as

Case ManagementEmergency Service, HospitalAdultHealth ExpendituresHumansMental HealthProgram Evaluationcase managementemergency departmentpatientPrior Presentations: Noneutilization

Identifiers

PMID33694072
PMCPMC8390593
OpenAlexW3135147406

What OpenQuestion holds

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Registered trials

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.