{"id":22151,"date":"2023-10-19T08:17:00","date_gmt":"2023-10-19T12:17:00","guid":{"rendered":"https:\/\/intosaijournal.org\/journal-entry\/understand-how-disabled-people-navigate-through-the-residency-path-with-optimal-matching\/"},"modified":"2023-10-29T23:45:31","modified_gmt":"2023-10-30T03:45:31","slug":"understand-how-disabled-people-navigate-through-the-residency-path-with-optimal-matching","status":"publish","type":"journal-entry","link":"https:\/\/intosaijournal.org\/fr\/journal-entry\/understand-how-disabled-people-navigate-through-the-residency-path-with-optimal-matching\/","title":{"rendered":"Comment l\u2019appariement optimal permet de suivre les parcours de r\u00e9sidence des personnes handicap\u00e9es"},"content":{"rendered":"\n

L’Auteur : Robin Kreling, Data Scientist au D\u00e9partement Analyse et Science des Donn\u00e9es, Cour Des Comptes, France<\/strong><\/p>\n\n\n\n

Le nombre de personnes de plus de 50 ans b\u00e9n\u00e9ficiaires d\u2019allocations pour le handicap a augment\u00e9 de 36 % en France entre 2011 et 2019. La Cour des comptes a voulu v\u00e9rifier si les besoins des populations concern\u00e9es \u00e9taient bien couverts.<\/p>\n\n\n\n

\u00c0 cette fin, la technique de l\u2019appariement optimal, consistant \u00e0 faire appara\u00eetre des similitudes dans la succession d\u2019\u00e9v\u00e9nements et donc une \u00e9ventuelle causalit\u00e9 entre eux, a \u00e9t\u00e9 utilis\u00e9e, en adaptant un algorithme issu de la g\u00e9n\u00e9tique, afin de v\u00e9rifier les \u00e9ventuelles ruptures dans le parcours de r\u00e9sidence et le parcours administratif, en fonction des diff\u00e9rentes situations rencontr\u00e9es (selon que les gens disposent ou non d’un accueil sp\u00e9cialis\u00e9 ou d\u2019une reconnaissance du handicap, re\u00e7oivent ou non des soins \u00e0 domicile, etc.).<\/p>\n\n\n\n

L\u2019\u00e9quipe de data scientists<\/em> s\u2019est notamment inspir\u00e9e de l\u2019approche employ\u00e9e par le CHU de Toulouse pour une pr\u00e9c\u00e9dente enqu\u00eate. Cela met en avant l\u2019int\u00e9r\u00eat d\u2019avoir une m\u00e9moire des travaux pass\u00e9s et une continuit\u00e9 d\u2019activit\u00e9 dans l\u2019\u00e9quipe, pour capitaliser sur les m\u00e9thodes, les pratiques et les innovations<\/strong>. L\u2019effort d\u2019ajustement de l\u2019algorithme aux donn\u00e9es utilis\u00e9es par la Cour dans le cadre de la pr\u00e9sente enqu\u00eate a consist\u00e9 en des choix de m\u00e9thode de calcul destin\u00e9s \u00e0 limiter le temps de calcul.<\/p>\n\n\n\n

Les donn\u00e9es ont \u00e9t\u00e9 extraites de la plateforme de services num\u00e9riques ViaTrajectoire<\/a>, qui met en relation les personnes et les \u00e9tablissements et aide ces derniers \u00e0 g\u00e9rer les files d’attente. L\u2019information disponible indiquait si les personnes disposaient de droits ouverts pour un accueil m\u00e9dicalis\u00e9 ou non. Les donn\u00e9es \u00e9taient pseudonymis\u00e9es, avec des cl\u00e9s diff\u00e9rentes pour chaque d\u00e9partement. Il faut souligner qu\u2019\u00e0 la suite de ces premiers travaux, la Cour envisage de r\u00e9aliser des appariements, toujours anonymes, avec d\u2019autres donn\u00e9es administratives pour d\u2019autres enqu\u00eates, en capitalisant sur les connaissances ainsi acquises sur ces bases de donn\u00e9es.<\/p>\n\n\n\n

Un algorithme pour confirmer et objectiver les intuitions de l\u2019\u00e9quipe d\u2019audit<\/strong><\/h2>\n\n\n\n

La technique de l\u2019appariement optimal appliqu\u00e9e \u00e0 ces donn\u00e9es a consist\u00e9 \u00e0 d\u00e9finir une m\u00e9trique de similarit\u00e9 entre les s\u00e9quences, c\u2019est-\u00e0-dire \u00e0 calculer un nombre donnant une indication sur la distance entre deux s\u00e9quences de donn\u00e9es : si de nombreuses modifications sont n\u00e9cessaires pour transformer une s\u00e9quence donn\u00e9e en une autre, elles sont consid\u00e9r\u00e9es comme tr\u00e8s dissemblables et \u00e9loign\u00e9es. Si elles ne n\u00e9cessitent que peu ou pas de modifications, elles sont tr\u00e8s proches. Cette m\u00e9trique est ensuite utilis\u00e9e pour regrouper les s\u00e9quences en grappes de proximit\u00e9.<\/p>\n\n\n\n

Le regroupement en clusters<\/em> de typologie de parcours administratifs et r\u00e9sidentiels a permis de confirmer les intuitions des enqu\u00eateurs et d\u2019affiner leur compr\u00e9hension des parcours administratifs, de soins et r\u00e9sidentiels individuels. Par exemple, 12 % des individus de l\u2019\u00e9chantillon de 45 \u00e0 50 ans sont regroup\u00e9s dans le cluster n\u00b02. Il s’agit de personnes ayant une reconnaissance administrative d’un handicap mais qui ne d\u00e9posent aucune demande connue aupr\u00e8s d’une institution pendant une longue p\u00e9riode : il peut notamment s\u2019agir de personnes qui refusent l\u2019orientation pr\u00e9conis\u00e9e ou m\u00e8nent la proc\u00e9dure de reconnaissance comme une mesure de pr\u00e9caution en vue de besoins futurs d’assistance formelle. Conna\u00eetre et quantifier l\u2019existence de telles d\u00e9marches de pr\u00e9caution est utile pour calculer des indicateurs de tension de solutions d\u2019h\u00e9bergement.<\/p>\n\n\n\n

<\/em><\/p>\n\n\n\n

 
Lecture des graphes : en abscisse, les mois et ann\u00e9es (s\u00e9quences de 5 ans complets, de 0-01 \u00e0 5-12) ; en ordonn\u00e9es : la proportion des observations du cluster, qui va de 0 \u00e0 1. Chaque cluster a une taille diff\u00e9rente (la taille est indiqu\u00e9e par \u00ab Freq. (weighted n=[nombre]) \u00bb. Par exemple, cluster 6, on voit que pr\u00e8s de 60 % des 3 228 personnes de ce cluster ne connaissent pas d\u2019\u00e9volution de leur situation sur une p\u00e9riode de 5 ans.<\/em><\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

Une collaboration entre l\u2019\u00e9quipe de contr\u00f4le et les data scientists<\/em> essentielle pour la r\u00e9ussite de l\u2019enqu\u00eate<\/strong><\/p>\n\n\n\n

Les \u00e9changes entre l\u2019\u00e9quipe de contr\u00f4le et les data scientists<\/em> du d\u00e9partement \u00ab Analyse et sciences des donn\u00e9es \u00bb de la Cour sont intervenus d\u00e8s la r\u00e9daction de la note de faisabilit\u00e9, pr\u00e9alable au d\u00e9clenchement de l\u2019enqu\u00eate. Cela a permis de fluidifier la collaboration et a facilit\u00e9 l\u2019exploitation des bases pendant l\u2019enqu\u00eate.<\/p>\n\n\n\n

L\u2019\u00e9quipe de contr\u00f4le avait notamment tr\u00e8s bien identifi\u00e9 les bases de donn\u00e9es utiles pour l\u2019enqu\u00eate. Ses \u00e9changes avec les data scientists<\/em> \u00e9taient hebdomadaires et ont permis de produire des indicateurs demand\u00e9s par les rapporteurs, de mettre en exergue des situations non-anticip\u00e9es (pr\u00e9pond\u00e9rance de certains handicaps moins visibles) et de souligner les lacunes dans des bases de donn\u00e9es administratives. Il est \u00e0 cet \u00e9gard important de signaler que l\u2019audit de la donn\u00e9e participe en lui-m\u00eame \u00e0 l\u2019audit du pilotage de la politique publique en question<\/strong> : ces lacunes, constat\u00e9es par les data scientists<\/em>, ont donn\u00e9 lieu \u00e0 des recommandations explicites dans le rapport de la Cour.<\/p>\n\n\n\n

La base de donn\u00e9es ainsi constitu\u00e9e est tr\u00e8s r\u00e9cente, mais elle sera utile aux enqu\u00eates ult\u00e9rieures sur le handicap et la d\u00e9pendance. Avec le temps, sa profondeur historique augmentera et permettra de repr\u00e9senter des s\u00e9quences plus longues et plus repr\u00e9sentatives d’un parcours de vie. Elle permettra \u00e9galement des analyses causales sur les effets des futures r\u00e9formes en mati\u00e8re d’autonomie et d’inclusion des personnes handicap\u00e9es.<\/p>\n\n\n\n

Robin Kreling<\/strong>, data scientist<\/em> au d\u00e9partement \u00ab Analyse et sciences des donn\u00e9es \u00bb<\/p>\n\n\n\n

Pour aller plus loin :<\/p>\n\n\n\n

Contacter<\/a> les analystes de la Cour.<\/p>\n\n\n\n

Lisez le rapport <\/strong>en fran\u00e7ais ou <\/strong>un r\u00e9sum\u00e9 en anglais.<\/p>\n\n\n\n

Acc\u00e9dez aux donn\u00e9es.<\/p>\n","protected":false},"excerpt":{"rendered":"

Le nombre de personnes de plus de 50 ans b\u00e9n\u00e9ficiaires d\u2019allocations pour le handicap a augment\u00e9 de 36\u00a0% en France entre 2011 et 2019. La Cour des comptes a voulu v\u00e9rifier si les besoins des populations concern\u00e9es \u00e9taient bien couverts.<\/p>\n","protected":false},"author":182,"featured_media":22137,"template":"","meta":{"_acf_changed":false,"footnotes":""},"journal-categories":[3587],"content-tags":[1832,1938,1879],"country":[3162],"region":[2129],"section":[2645],"coauthors":[3582],"class_list":["post-22151","journal-entry","type-journal-entry","status-publish","has-post-thumbnail","hentry","journal-categories-q2-2023-fr","content-tags-analyse-des-donnees","content-tags-diversite-equite-et-inclusion","content-tags-science-et-technologie","country-france-fr","region-eurosai-fr","section-spotlight-on-diversity-equity-and-inclusion-fr"],"acf":[],"yoast_head":"\nComment l\u2019appariement optimal permet de suivre les parcours de r\u00e9sidence des personnes handicap\u00e9es - INTOSAI Journal<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/intosaijournal.org\/fr\/journal-entry\/understand-how-disabled-people-navigate-through-the-residency-path-with-optimal-matching\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comment l\u2019appariement optimal permet de suivre les parcours de r\u00e9sidence des personnes handicap\u00e9es - INTOSAI Journal\" \/>\n<meta property=\"og:description\" content=\"Le nombre de personnes de plus de 50 ans b\u00e9n\u00e9ficiaires d\u2019allocations pour le handicap a augment\u00e9 de 36\u00a0% en France entre 2011 et 2019. 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