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

Producción científica: Capítulo en Libro/Reporte/Acta de conferenciaContribución a la conferencia

Resumen

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.

Idioma originalEnglish
Título de la publicación alojadaArtificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings
EditoresDavid Riaño, Szymon Wilk, Annette ten Teije
EditorialSpringer Verlag
Páginas241-251
Número de páginas11
ISBN (versión impresa)9783030216412
DOI
EstadoPublished - 2019
Evento17th Conference on Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland
Duración: jun. 26 2019jun. 29 2019

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11526 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conference

Conference17th Conference on Artificial Intelligence in Medicine, AIME 2019
País/TerritorioPoland
CiudadPoznan
Período6/26/196/29/19

Nota bibliográfica

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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Citar esto

Abidi, S. S. R., Rad, J., Abusharekh, A., Roy, P. C., Van Woensel, W., Abidi, S. R., Cheng, C., Crocker, B., & Elnenaei, M. (2019). AI-driven pathology laboratory utilization management via data- and knowledge-based analytics. En D. Riaño, S. Wilk, & A. ten Teije (Eds.), Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings (pp. 241-251). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-21642-9_30