FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission

Objective Predictive statistical models used in population stratification programmes are complex and usually difficult to interpret for primary care professionals. We designed FINGER (Forming and Identifying New Groups of Expected Risks), a new model based on clinical criteria, easy to understand an...

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Autores: Orueta Mendia, Juan Francisco, García Álvarez, Arturo, Aurrekoetxea Agirre, Juan José, García Goñi, Manuel
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/30884
Acceso en línea:http://hdl.handle.net/10810/30884
Access Level:acceso abierto
Palabra clave:multiple chronic conditions
basque country
multimorbidity
expenditures
prevalence
medicare
stratification
implementation
population
payment
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spelling FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of AdmissionOrueta Mendia, Juan FranciscoGarcía Álvarez, ArturoAurrekoetxea Agirre, Juan JoséGarcía Goñi, Manuelmultiple chronic conditionsbasque countrymultimorbidityexpendituresprevalencemedicarestratificationimplementationpopulationpaymentObjective Predictive statistical models used in population stratification programmes are complex and usually difficult to interpret for primary care professionals. We designed FINGER (Forming and Identifying New Groups of Expected Risks), a new model based on clinical criteria, easy to understand and implement by physicians. Our aim was to assess the ability of FINGER to predict costs and correctly identify patients with high resource use in the following year. Design Cross-sectional study with a 2-year follow-up. Setting The Basque National Health System. Participants All the residents in the Basque Country (Spain) >= 14 years of age covered by the public healthcare service (n=1 946 884). Methods We developed an algorithm classifying diagnoses of long-term health problems into 27 chronic disease groups. The database was randomly divided into two data sets. With the calibration sample, we calculated a score for each chronic disease group and other variables (age, sex, inpatient admissions, emergency department visits and chronic dialysis). Each individual obtained a FINGER score for the year by summing their characteristics' scores. With the validation sample, we constructed regression models with the FINGER score for the first 12 months as the only explanatory variable. Results The annual FINGER scores obtained by patients ranged from 0 to 57 points, with a mean of 2.06. The coefficient of determination for healthcare costs was 0.188 and the area under the receiver operating characteristic curve was 0.838 for identifying patients with high costs (>95th percentile); 0.875 for extremely high costs (>99th percentile); 0.802 for unscheduled admissions; 0.861 for prolonged hospitalisation (>15 days); and 0.896 for death. Conclusion FINGER presents a predictive power for high risks fairly close to other classification systems. Its simple and transparent architecture allows for immediate calculation by clinicians. Being easy to interpret, it might be considered for implementation in regions involved in population stratification programmes.Manuel Garcia-Goni thanks the Ramon Areces Foundation for financial support under the research project 'Envejecimiento y sistema sanitario y social. El gasto publico y sus efectos en igualdad, dependencia y aseguramiento en Espana'. All authors thank this project for funding publishing charges.BMJ Publishing Group201920192018info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/30884reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://bmjopen.bmj.com/content/8/5/e019830.longinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/es/This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Atribución-NoComercial 3.0 Españaoai:addi.ehu.eus:10810/308842026-06-18T09:23:17Z
dc.title.none.fl_str_mv FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission
title FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission
spellingShingle FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission
Orueta Mendia, Juan Francisco
multiple chronic conditions
basque country
multimorbidity
expenditures
prevalence
medicare
stratification
implementation
population
payment
title_short FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission
title_full FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission
title_fullStr FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission
title_full_unstemmed FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission
title_sort FINGER (Forming and Identifying New Groups of Expected Risks): Developing and Validating a New Predictive Model to Identify Patients With High Healthcare Cost and at Risk of Admission
dc.creator.none.fl_str_mv Orueta Mendia, Juan Francisco
García Álvarez, Arturo
Aurrekoetxea Agirre, Juan José
García Goñi, Manuel
author Orueta Mendia, Juan Francisco
author_facet Orueta Mendia, Juan Francisco
García Álvarez, Arturo
Aurrekoetxea Agirre, Juan José
García Goñi, Manuel
author_role author
author2 García Álvarez, Arturo
Aurrekoetxea Agirre, Juan José
García Goñi, Manuel
author2_role author
author
author
dc.subject.none.fl_str_mv multiple chronic conditions
basque country
multimorbidity
expenditures
prevalence
medicare
stratification
implementation
population
payment
topic multiple chronic conditions
basque country
multimorbidity
expenditures
prevalence
medicare
stratification
implementation
population
payment
description Objective Predictive statistical models used in population stratification programmes are complex and usually difficult to interpret for primary care professionals. We designed FINGER (Forming and Identifying New Groups of Expected Risks), a new model based on clinical criteria, easy to understand and implement by physicians. Our aim was to assess the ability of FINGER to predict costs and correctly identify patients with high resource use in the following year. Design Cross-sectional study with a 2-year follow-up. Setting The Basque National Health System. Participants All the residents in the Basque Country (Spain) >= 14 years of age covered by the public healthcare service (n=1 946 884). Methods We developed an algorithm classifying diagnoses of long-term health problems into 27 chronic disease groups. The database was randomly divided into two data sets. With the calibration sample, we calculated a score for each chronic disease group and other variables (age, sex, inpatient admissions, emergency department visits and chronic dialysis). Each individual obtained a FINGER score for the year by summing their characteristics' scores. With the validation sample, we constructed regression models with the FINGER score for the first 12 months as the only explanatory variable. Results The annual FINGER scores obtained by patients ranged from 0 to 57 points, with a mean of 2.06. The coefficient of determination for healthcare costs was 0.188 and the area under the receiver operating characteristic curve was 0.838 for identifying patients with high costs (>95th percentile); 0.875 for extremely high costs (>99th percentile); 0.802 for unscheduled admissions; 0.861 for prolonged hospitalisation (>15 days); and 0.896 for death. Conclusion FINGER presents a predictive power for high risks fairly close to other classification systems. Its simple and transparent architecture allows for immediate calculation by clinicians. Being easy to interpret, it might be considered for implementation in regions involved in population stratification programmes.
publishDate 2018
dc.date.none.fl_str_mv 2018
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/30884
url http://hdl.handle.net/10810/30884
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://bmjopen.bmj.com/content/8/5/e019830.long
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/es/
Atribución-NoComercial 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/es/
Atribución-NoComercial 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv BMJ Publishing Group
publisher.none.fl_str_mv BMJ Publishing Group
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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