AI-driven pathology laboratory utilization management via data- and knowledge-based analytics

Syed Sibte Raza Abidi, Jaber Rad, Ashraf Abusharekh, Patrice C. Roy, William Van Woensel, Samina R. Abidi, Calvino Cheng, Bryan Crocker, Manal Elnenaei

Résultat de recherche: Conference contribution

Résumé

Inappropriate pathology test orders are an economic burden on laboratories and compromise patient safety. We pursue a laboratory utilization management strategy that involves raising awareness amongst physicians regarding their test ordering behaviour. We are employing an AI-driven approach for laboratory utilization management, whereby we apply both machine learning and semantic reasoning methods to analyze pathology laboratory data. We are analyzing over 6-years of primary care physician’s pathology test order ‘big’ data. Our analysis generates physician order profiles, based on their case-mix and orders-sets, to inform physicians about their laboratory utilization. We developed an AI-driven platform—i.e. Pathology Laboratory Utilization Scorecards (PLUS) that offers an interactive means for physicians to self-examine their test ordering pattern. PLUS aims to optimize the utilization of the Central Zone pathology laboratory of the Nova Scotia Health Authority.

Langue d'origineEnglish
Titre de la publication principaleArtificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings
ÉditeursDavid Riaño, Szymon Wilk, Annette ten Teije
Maison d'éditionSpringer Verlag
Pages241-251
Nombre de pages11
ISBN (imprimé)9783030216412
DOI
Statut de publicationPublished - 2019
Événement17th Conference on Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland
Durée: juin 26 2019juin 29 2019

Séries de publication

PrénomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11526 LNAI
ISSN (imprimé)0302-9743
ISSN (électronique)1611-3349

Conference

Conference17th Conference on Artificial Intelligence in Medicine, AIME 2019
Pays/TerritoirePoland
VillePoznan
Période6/26/196/29/19

Note bibliographique

Funding Information:
We thank the NSHA Central Zone pathology lab for supporting the project, and Nova Scotia Health Research Foundation for giving the catalyst grant.

Funding Information:
Acknowledgements. We thank the NSHA Central Zone pathology lab for supporting the project, and Nova Scotia Health Research Foundation for giving the catalyst grant.

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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