A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer
Identifieur interne : 001410 ( Main/Exploration ); précédent : 001409; suivant : 001411A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer
Auteurs : Nicolas Jay [France] ; Gilles Nuemi [France] ; Maryse Gadreau [France] ; Catherine Quantin [France]Source :
- BMC Medical Informatics and Decision Making [ 1472-6947 ] ; 2013.
Descripteurs français
- KwdFr :
- Adulte d'âge moyen, Bases de données factuelles (), Coûts hospitaliers (), Examen des demandes de remboursement d'assurance (), Femelle, Fouille de données (), France (épidémiologie), Humains, Hôpitaux (), Mâle, Sujet âgé, Tumeurs du sein (), Tumeurs du sein (économie), Tumeurs du sein (épidémiologie).
- MESH :
- économie : Tumeurs du sein.
- épidémiologie : France, Tumeurs du sein.
- Adulte d'âge moyen, Bases de données factuelles, Coûts hospitaliers, Examen des demandes de remboursement d'assurance, Femelle, Fouille de données, Humains, Hôpitaux, Mâle, Sujet âgé, Tumeurs du sein.
- Wicri :
English descriptors
- KwdEn :
- Aged, Breast Neoplasms (classification), Breast Neoplasms (economics), Breast Neoplasms (epidemiology), Breast Neoplasms (surgery), Data Mining (statistics & numerical data), Databases, Factual (statistics & numerical data), Female, France (epidemiology), Hospital Costs (statistics & numerical data), Hospitals (statistics & numerical data), Humans, Insurance Claim Review (statistics & numerical data), Male, Middle Aged.
- MESH :
- geographic , epidemiology : France.
- classification : Breast Neoplasms.
- economics : Breast Neoplasms.
- epidemiology : Breast Neoplasms.
- statistics & numerical data : Data Mining, Databases, Factual, Hospital Costs, Hospitals, Insurance Claim Review.
- surgery : Breast Neoplasms.
- Aged, Female, Humans, Male, Middle Aged.
- mix :
Abstract
With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources. We propose an approach based on mining claim data to support the exploration of trajectories of care.
A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. The main diagnoses of hospitalizations were used to produce morbidity profiles. Cumulative hospital costs were computed for each profile.
57,552 patients were automatically grouped into 19 classes. The resulting profiles were clinically meaningful and economically relevant. The mean cost per trajectory was 9,600€. Severe conditions were generally associated with higher costs. The lowest costs (6,957€) were observed for patients with in situ carcinoma of the breast, the highest for patients hospitalized for palliative care (26,139€).
Formal Concept Analysis can be applied on claim data to produce an automatic classification of care trajectories. This flexible approach takes advantages of routinely collected data and can be used to setup cost-of-illness studies.
Url:
- http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4220620
- http://www.hal.inserm.fr/inserm-00917359
- https://halshs.archives-ouvertes.fr/halshs-01227973
DOI: 10.1186/1472-6947-13-130
PubMed: 24289668
PubMed Central: 4220620
Affiliations:
- France
- Bourgogne, Bourgogne-Franche-Comté, Grand Est, Lorraine (région)
- Dijon, Nancy
- Université de Bourgogne, Université de Lorraine
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Le document en format XML
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nuts="2">Bourgogne-Franche-Comté</region><region type="old region" nuts="2">Bourgogne</region><settlement type="city">Dijon</settlement></placeName></affiliation></author><author><name sortKey="Gadreau, Maryse" sort="Gadreau, Maryse" uniqKey="Gadreau M" first="Maryse" last="Gadreau">Maryse Gadreau</name><affiliation wicri:level="4"><nlm:aff id="I5">Université de Bourgogne, F-21000, Dijon, France</nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Université de Bourgogne, F-21000, Dijon</wicri:regionArea><placeName><region type="region" nuts="2">Bourgogne-Franche-Comté</region><region type="old region" nuts="2">Bourgogne</region><settlement type="city">Dijon</settlement></placeName><orgName type="university">Université de Bourgogne</orgName></affiliation></author><author><name sortKey="Quantin, Catherine" sort="Quantin, Catherine" uniqKey="Quantin C" first="Catherine" last="Quantin">Catherine Quantin</name><affiliation wicri:level="3"><nlm:aff id="I3">CHRU Dijon, Service de Biostatistique et d’Informatique Médicale (DIM), F-21000, Dijon, France</nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>CHRU Dijon, Service de Biostatistique et d’Informatique Médicale (DIM), F-21000, Dijon</wicri:regionArea><placeName><region type="region" nuts="2">Bourgogne-Franche-Comté</region><region type="old region" nuts="2">Bourgogne</region><settlement type="city">Dijon</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:aff id="I4">Inserm, U866, Univ de Bourgogne, F-21000, Dijon, France</nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Inserm, U866, Univ de Bourgogne, F-21000, Dijon</wicri:regionArea><placeName><region type="region" nuts="2">Bourgogne-Franche-Comté</region><region type="old region" nuts="2">Bourgogne</region><settlement type="city">Dijon</settlement></placeName></affiliation></author></analytic><series><title level="j">BMC Medical Informatics and Decision Making</title><idno type="eISSN">1472-6947</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged</term><term>Breast Neoplasms (classification)</term><term>Breast Neoplasms (economics)</term><term>Breast Neoplasms (epidemiology)</term><term>Breast Neoplasms (surgery)</term><term>Data Mining (statistics & numerical data)</term><term>Databases, Factual (statistics & numerical data)</term><term>Female</term><term>France (epidemiology)</term><term>Hospital Costs (statistics & numerical data)</term><term>Hospitals (statistics & numerical data)</term><term>Humans</term><term>Insurance Claim Review (statistics & numerical data)</term><term>Male</term><term>Middle Aged</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte d'âge moyen</term><term>Bases de données factuelles ()</term><term>Coûts hospitaliers ()</term><term>Examen des demandes de remboursement d'assurance ()</term><term>Femelle</term><term>Fouille de données ()</term><term>France (épidémiologie)</term><term>Humains</term><term>Hôpitaux ()</term><term>Mâle</term><term>Sujet âgé</term><term>Tumeurs du sein ()</term><term>Tumeurs du sein (économie)</term><term>Tumeurs du sein (épidémiologie)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>France</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" qualifier="economics" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Data Mining</term><term>Databases, Factual</term><term>Hospital Costs</term><term>Hospitals</term><term>Insurance Claim Review</term></keywords><keywords scheme="MESH" qualifier="surgery" xml:lang="en"><term>Breast Neoplasms</term></keywords><keywords scheme="MESH" qualifier="économie" xml:lang="fr"><term>Tumeurs du sein</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>France</term><term>Tumeurs du sein</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte d'âge moyen</term><term>Bases de données factuelles</term><term>Coûts hospitaliers</term><term>Examen des demandes de remboursement d'assurance</term><term>Femelle</term><term>Fouille de données</term><term>Humains</term><term>Hôpitaux</term><term>Mâle</term><term>Sujet âgé</term><term>Tumeurs du sein</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>France</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Cancer</term></keywords><keywords scheme="mix" xml:lang="en"><term>Analysing trajectories</term><term>Cancer</term><term>Care</term><term>Claim data</term><term>Data mining</term><term>Formal concept analysis</term><term>Trajectory of care</term><term>breast cancer</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources. We propose an approach based on mining claim data to support the exploration of trajectories of care.</p></sec><sec><title>Methods</title><p>A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. The main diagnoses of hospitalizations were used to produce morbidity profiles. Cumulative hospital costs were computed for each profile.</p></sec><sec><title>Results</title><p>57,552 patients were automatically grouped into 19 classes. The resulting profiles were clinically meaningful and economically relevant. The mean cost per trajectory was 9,600€. Severe conditions were generally associated with higher costs. The lowest costs (6,957€) were observed for patients with in situ carcinoma of the breast, the highest for patients hospitalized for palliative care (26,139€).</p></sec><sec><title>Conclusions</title><p>Formal Concept Analysis can be applied on claim data to produce an automatic classification of care trajectories. This flexible approach takes advantages of routinely collected data and can be used to setup cost-of-illness studies.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Allotey, P" uniqKey="Allotey P">P Allotey</name></author><author><name sortKey="Reidpath, Dd" uniqKey="Reidpath D">DD Reidpath</name></author><author><name sortKey="Yasin, S" uniqKey="Yasin S">S Yasin</name></author><author><name sortKey="Chan, Ck" uniqKey="Chan C">CK Chan</name></author><author><name sortKey="De Graft Aikins, A" uniqKey="De Graft Aikins A">A de-Graft Aikins</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mariotto, Ab" uniqKey="Mariotto A">AB Mariotto</name></author><author><name sortKey="Yabroff, Kr" uniqKey="Yabroff K">KR Yabroff</name></author><author><name sortKey="Shao, Y" uniqKey="Shao Y">Y Shao</name></author><author><name sortKey="Feuer, Ej" uniqKey="Feuer E">EJ Feuer</name></author><author><name 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sortKey="Weiner, J" uniqKey="Weiner J">J Weiner</name></author><author><name sortKey="Mumford, L" uniqKey="Mumford L">L Mumford</name></author><author><name sortKey="Steinwachs, D" uniqKey="Steinwachs D">D Steinwachs</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Grubinger, T" uniqKey="Grubinger T">T Grubinger</name></author><author><name sortKey="Kobel, C" uniqKey="Kobel C">C Kobel</name></author><author><name sortKey="Pfeiffer, Kp" uniqKey="Pfeiffer K">KP Pfeiffer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Smidth, M" uniqKey="Smidth M">M Smidth</name></author><author><name sortKey="Sokolowski, I" uniqKey="Sokolowski I">I Sokolowski</name></author><author><name sortKey="K Rsvang, L" uniqKey="K Rsvang L">L Kærsvang</name></author><author><name sortKey="Vedsted, P" uniqKey="Vedsted P">P Vedsted</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Benchimol, Ei" uniqKey="Benchimol E">EI Benchimol</name></author><author><name sortKey="Manuel, Dg" uniqKey="Manuel D">DG Manuel</name></author><author><name sortKey="To, T" uniqKey="To T">T To</name></author><author><name sortKey="Griffiths, Am" uniqKey="Griffiths A">AM Griffiths</name></author><author><name sortKey="Rabeneck, L" uniqKey="Rabeneck L">L Rabeneck</name></author><author><name sortKey="Guttmann, A" uniqKey="Guttmann A">A Guttmann</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>France</li></country><region><li>Bourgogne</li><li>Bourgogne-Franche-Comté</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Dijon</li><li>Nancy</li></settlement><orgName><li>Université de Bourgogne</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Jay, Nicolas" sort="Jay, Nicolas" uniqKey="Jay N" first="Nicolas" last="Jay">Nicolas Jay</name></region><name sortKey="Gadreau, Maryse" sort="Gadreau, Maryse" uniqKey="Gadreau M" first="Maryse" last="Gadreau">Maryse Gadreau</name><name sortKey="Jay, Nicolas" sort="Jay, Nicolas" uniqKey="Jay N" first="Nicolas" last="Jay">Nicolas Jay</name><name sortKey="Nuemi, Gilles" sort="Nuemi, Gilles" uniqKey="Nuemi G" first="Gilles" last="Nuemi">Gilles Nuemi</name><name sortKey="Quantin, Catherine" sort="Quantin, Catherine" uniqKey="Quantin C" first="Catherine" last="Quantin">Catherine Quantin</name><name sortKey="Quantin, Catherine" sort="Quantin, Catherine" uniqKey="Quantin C" first="Catherine" last="Quantin">Catherine Quantin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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