The DOI matches its Crossref record.
The Effect of Rostering with a Patient Enrolment Model on Emergency Department Utilization
Open accessAbstract
OBJECTIVE: To assess the effect of rostering with a patient enrolment model (PEM) in Ontario on emergency department utilization for non-emergent care. DATA SOURCES/STUDY SETTING: Administrative 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 DESIGN: Patient-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. CONCLUSION: This 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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Several studies have examined specific primary care models that attach patients, making it difficult to disentangle the effect attributable to attachment from other features of these delivery models (e.g. team-based care, after-hours care) [37–41].”
Citations
Cited by
Singh, J., Dahrouge, S., & Green, M. E. (2019) · BMC Family Practice · doi:10.1186/s12875-019-0942-7 · Cited by 34
Smithman, M. A., Haggerty, J., Gaboury, I., & Breton, M. (2022) · BMC Primary Care · doi:10.1186/s12875-022-01850-4 · Cited by 25
Metadata
- Authors
- Tiagi, R., & Chechulin, Y.
- Affiliations
- Ministry of Health and Long Term Care (CA)
- ORCID
- Tiagi, Raaj
- Venue
- Healthcare Policy | Politiques de Santé
- Publisher
- Longwoods Publishing
- Published
- 2014-05-28
- Volume
- 9
- Issue
- 4
- Pages
- 105-121
- ISSN
- 1715-6572 (ISSN-L)
- 1715-6580
- PMC
- PMC4749888
- OpenAlex
- W2144185040
- Cited by
- 18
- References
- 0
- Citations by year
- 2025: 4 · 2024: 2 · 2023: 1 · 2022: 3 · 2021: 4 · 2019: 1 · 2018: 2 · 2017: 1
- FWCI
- 0.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Healthcare policy (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Health Sciences › Medicine › Family Practice › Primary Care and Health Outcomes
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Keywords
- emergency department utilization, health system efficiency, hospital cost savings, Ontario health system
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Emergency Service, Hospital, Female, Health Services Misuse, Humans, Infant, Infant, Newborn, Male, Middle Aged, Ontario, Primary Health Care, Records, Models, Statistical, Universal Health Insurance, Young Adult, Propensity Score
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
