Evidence map›Paper›PMID 24973487›Full record

ArticleHealthcare policy = Politiques de sante2014

The effect of rostering with a patient enrolment model on emergency department utilization.

Raaj Tiagi, Yuriy Chechulin

Abstract read
In one paragraph

Article in Healthcare policy = Politiques de sante, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Toward a universal definition of provider-patient attachment in primary care.Canadian family physician Medecin de famille canadien · 2024
    Review
  6. Article
  7. Observational
  8. Article
  9. Article
  10. Article
  11. Article
  12. A modeling study exploring the impact of homelessness on rostered primary care utilization in Calgary, Canada.Canadian journal of public health = Revue canadienne de sante publique · 2018
    Article
  13. Article
  14. Article
  15. Article
  16. 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

2 authors.

Raaj TiagiHealth Analytics Branch, Ministry of Health and Long-Term Care Toronto, ON.
Yuriy ChechulinHealth Analytics Branch, Ministry of Health and Long-Term Care Toronto, ON.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the effect of rostering with a patient enrolment model (PEM) in Ontario on emergency department utilization for non-emergent care. DATA SOURCES/STUDY

settingAdministrative data for fiscal years 2006/07 through 2010/11 from the Ontario Ministry of Health and Long-Term Care were used for the analysis. STUDY

designPatient-level analysis with a difference-in-difference modelling approach was used to study the relationship. A control group was established using propensity score matching. PRINCIPAL FINDING: Results suggest that rostering with a PEM is associated with a statistically significant reduction in emergency department (ED) (non-emergent) visits in Ontario. More specifically, enrolment with PEMs reduced ED visits by 3% during the study period, translating into cost savings of approximately $8 million for hospitals in Ontario.

conclusionThis study shows that PEMs have achieved some degree of success in enhancing health system efficiency in Ontario through the reduction in the use of EDs for non-emergent care.

Indexed as

RecordsAdolescentAdultAgedAged, 80 and overChildChild, PreschoolEmergency Service, HospitalFemaleHealth Services MisuseHumansInfantInfant, NewbornMaleMiddle AgedModels, Statistical

Identifiers

PMID24973487
PMCPMC4749888

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.