Pular para o conteúdo principal

ETHNOS_APP

Início • Busca • Periódicos • Lista 0

Predicting healthcare costs with diagnoses recorded in primary and secondary care

An Analysis of Linked Records

Dados Bibliográficos

ID4594749
AutoresShaolin Wang (0000-0002-4220-5640, University of Manchester), Laura Anselmi (0000-0002-2499-7656, Manchester Academic Health Science Centre), Yiu-Shing Lau (0000-0002-3915-4168, University of Manchester), Madeline Sutton (0000-0002-6635-2127, Monash University)
Ano2025
Volume378
Páginas118157
Data de publicação2025-08-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoSocial Science & Medicine (JOURNAL)
Identificadores do periódicoISSN: 0277-9536 • E-ISSN: 1873-5347
EditoraElsevier BV (PUBLISHER)
DOI10.1016/j.socscimed.2025.118157
PMID40359621
OpenAlexW4410104573
IdiomaEN
Referências citadas19

Most risk-adjustment models rely on diagnoses recorded during previous contacts in the same care setting to predict service use and cost. When diagnostic information from multiple settings has been used, studies have not examined how diagnoses recorded in different care settings influence model performance. Using a single set of diagnostic indicators recorded in primary or secondary care can incentivise case-finding and treatment outside hospital, but may reduce model fit if secondary care diagnosis indicates higher levels of severity. Using linked primary and secondary care records for 12.8 million patients in England, we used 205 chronic conditions recorded in primary care to complement those recorded during recent hospital admissions. We examined predictions of hospital use and cost for different population groups and considered the related incentives and implications for efficiency and fairness. Most patients (56 %) had at least one condition ever recorded in primary care, while only 15 % had at least one recorded in secondary care in the previous two years. Adding diagnoses recorded only in primary care as a separate additional set of predictors improved the model fit for total costs, planned and unplanned costs, elective and emergency admissions, outpatient visits, and emergency department attendances. Using a single set of diagnoses recorded in either setting did not improve model fit, except for outpatient visits. Including primary care diagnoses reduced under and over-compensation and increased the predicted service needs of younger patients in less deprived areas and older patients in more deprived areas

Economic growth · Economics · Environmental health · Family medicine · Health care · Medical diagnosis · Pathology · Population · Primary care · Primary health care · Public health · Chronic Disease Management Strategies · Healthcare Policy and Management · Medical Coding and Health Information · Medicine · Nursing · Gerontology

  • A Review on Methods of Risk Adjustment and their Use in Integrated Healthcare Systems

    Open Access•Christin Juhnke, Susanne Bethge et al.•International Journal of…•2016

  • Development and Assessment of a New Framework for Disease Surveillance, Prediction, and Risk Adjustment

    Open Access•Randall P Ellis, Heather Hsu et al.•JAMA Health Forum•2022

  • Diagnosis-based Cost Groups in the Dutch Risk-equalization Model

    Frank Eijkenaar, René C J A Van Vliet et al.•Medical Care•2018

  • Adjusting the risk-adjustment

    Open Access•Sean Urwin, Laura Anselmi et al.•Social Science & Medicine•2024

Velocidade de citaçãohistorical
Altamente citadoNão
Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae